This document summarizes a review article about pharmacophore modeling and its applications in drug discovery. The key points are:
1) Pharmacophore modeling approaches have become major tools in drug discovery over the past century. Both ligand-based and structure-based methods have been developed and applied successfully in virtual screening, de novo design, and lead optimization.
2) Despite successes, pharmacophore approaches have not reached their full potential, particularly in reducing drug discovery costs. Key challenges include modeling ligand flexibility, molecular alignment, and proper training set selection.
3) Recent advances aim to address these challenges, including new conformational sampling methods, alignment algorithms, and approaches for structure-based pharmacophore modeling using protein structures without ligands
This document summarizes various virtual screening techniques used in drug discovery. It discusses ligand-based methods like similarity searching using 2D and 3D fingerprints, pharmacophore mapping. It also discusses structure-based methods like protein-ligand docking to predict binding poses and scores. Hybrid methods combining different techniques are also used. The document provides an overview of key virtual screening methods and their applications to enrich hit rates and select compounds for further testing from large libraries in an efficient manner during the drug discovery process.
The document discusses pharmacophores, which are abstract descriptions of molecular features necessary for molecular recognition between a ligand and biological macromolecule. A pharmacophore consists of 3D structural features like hydrophobic groups and hydrogen bond donors/acceptors. Pharmacophore mapping is used to define pharmacophoric features and align molecules to identify common binding elements responsible for biological activity. Pharmacophore models can be used in virtual screening to filter large databases and identify new compounds that may bind similarly to known active molecules. The document provides details on different approaches for pharmacophore generation and searching compound libraries.
In this slide I presented the Computer Aided Drug Design and its type :
1.Structure based Drug Design
2. Ligand based Drug Design and its Applications.
(Kartik Tiwari) Denovo Drug Design.pptxKartik Tiwari
Hygia Institute of Pharmaceutical Education and Research provides information on drug design. There are two main types of drug design: ligand-based which relies on existing molecules that bind to the target, and structure-based which relies on the 3D structure of the target. De-novo drug design uses the 3D structure of the receptor to design new molecules and involves optimizing ligands to fit the receptor's active site properties. LUDI software aids de-novo design through identifying interaction sites in the receptor, fitting molecular fragments, and linking fragments together to form new drug candidates.
The document discusses various molecular modeling and computational chemistry techniques used to simulate molecular systems, including molecular dynamics, molecular mechanics, quantum mechanics methods, and molecular docking. It provides an overview of the different modeling strategies and computational tools used, such as determining receptor geometry from X-ray crystallography, energy minimization techniques, force field parameters, and quantum mechanical calculations. The goal of molecular modeling is to develop accurate models of molecular systems to predict properties and behavior without experimental testing.
Molecular docking is a computer modeling technique used to predict the preferred orientation of one molecule to another when bound to form a stable complex. It involves fitting potential drug molecules into the active site of a protein receptor in order to identify which molecules may bind strongly. There are different approaches to molecular docking including rigid docking which treats molecules as rigid bodies, and flexible docking which accounts for conformational changes in ligands. The goal of docking is to find binding orientations that minimize the total energy of the system and maximize intermolecular interactions in order to predict effective drug candidates.
This document summarizes various virtual screening techniques used in drug discovery. It discusses ligand-based methods like similarity searching using 2D and 3D fingerprints, pharmacophore mapping. It also discusses structure-based methods like protein-ligand docking to predict binding poses and scores. Hybrid methods combining different techniques are also used. The document provides an overview of key virtual screening methods and their applications to enrich hit rates and select compounds for further testing from large libraries in an efficient manner during the drug discovery process.
The document discusses pharmacophores, which are abstract descriptions of molecular features necessary for molecular recognition between a ligand and biological macromolecule. A pharmacophore consists of 3D structural features like hydrophobic groups and hydrogen bond donors/acceptors. Pharmacophore mapping is used to define pharmacophoric features and align molecules to identify common binding elements responsible for biological activity. Pharmacophore models can be used in virtual screening to filter large databases and identify new compounds that may bind similarly to known active molecules. The document provides details on different approaches for pharmacophore generation and searching compound libraries.
In this slide I presented the Computer Aided Drug Design and its type :
1.Structure based Drug Design
2. Ligand based Drug Design and its Applications.
Quantitative structure-activity relationships (QSAR) use mathematical models to predict biological activity based on molecular properties. QSAR models are developed using statistical methods like partial least squares on datasets of compounds with known activities. Three-dimensional (3D) QSAR extends this approach by incorporating 3D structural descriptors and molecular fields derived from programs like CoMFA, VolSurf, and Catalyst to model activity based on interactions at binding sites. These 3D-QSAR models can be used to predict activity and design new compounds with improved properties.
1. Pharmacophore mapping involves identifying common binding elements in active compounds, generating potential conformations, and determining the 3D spatial relationships between pharmacophoric elements.
2. Conformational searching is important for pharmacophore mapping to explore a molecule's energy surface and identify low-energy conformations. There are different approaches like systematic search, distance geometry, and molecular dynamics.
3. Systematic search deterministically varies torsion angles to generate conformations. Distance geometry randomly samples conformations and can consider flexibility across multiple molecules simultaneously. Clique detection searches for common inter-feature distance patterns within active molecules to identify pharmacophore combinations.
This document provides an overview of quantitative structure-activity relationship (QSAR) modeling approaches. It discusses various 3D-QSAR methods including contour map analysis, statistical methods like linear regression, principal components analysis, and pattern recognition techniques like cluster analysis and artificial neural networks. The importance of statistical parameters for evaluating and selecting the best QSAR model is also highlighted. In summary, the document outlines different 3D-QSAR modeling techniques, statistical analyses used in QSAR studies, and how statistics help in model selection and evaluation.
This document discusses de novo drug design, which involves using a target receptor's 3D structure to design new molecules that can interact with it, without relying on existing leads. The key steps of computer-based de novo design are generating primary constraints from the receptor, deriving interaction sites in the binding pocket, building up ligand structures using methods like growing and linking fragments, scoring the ligands to evaluate binding affinity, and applying secondary constraints related to drug properties. Successful applications of de novo design include HIV protease inhibitors and COMT inhibitors.
This document discusses de novo drug design, which involves designing novel drug structures based on the receptor structure without using existing ligands. It describes various algorithms and methods for de novo drug design, including outside-in and inside-out methods, active site analysis, whole molecule fitting, site point connection, fragment connection, sequential buildup, and random connection/disconnection methods. Each method has advantages and disadvantages for suggesting potential drug molecules.
Molecular docking is a method for predicting how two molecules, such as a ligand and its protein target, will interact and fit together in three dimensions. Docking has become an important tool in drug discovery for identifying potential binding conformations between drug candidates and protein targets. The key steps in a typical docking workflow involve selecting the receptor and ligand molecules, then using software to computationally predict the orientation of binding and evaluate the fit through scoring functions. Popular molecular docking software packages include AutoDock, GOLD, and Glide. Applications of docking include virtual screening in drug discovery and lead optimization.
3D QSAR techniques like CoMFA and CoMSIA can quantitatively correlate the biological activity of a series of compounds to their 3D molecular properties. CoMFA generates 3D interaction fields around aligned molecules using steric and electrostatic probes, while CoMSIA additionally considers hydrophobic and hydrogen bonding interactions. The document discusses these techniques and provides an example case study applying CoMFA to develop a QSAR model for human eosinophil phosphodiesterase inhibitors with a cross-validated R2 of 0.565. In conclusion, 3D QSAR is a valuable tool for understanding structure-activity relationships and aiding drug design and discovery efforts.
1) De novo drug design involves generating new drug molecules from scratch based on the 3D structure of the target receptor.
2) It uses molecular modeling tools to modify lead compounds to better interact with the receptor's binding site.
3) The process involves defining interaction sites on the receptor, generating potential drug molecules, scoring them based on their fit with the receptor, and using search algorithms to refine candidates.
This document discusses the identification and generation of pharmacophores. A pharmacophore is a specific 3D arrangement of functional groups within a molecule that are necessary for binding to an enzyme or receptor. Pharmacophore identification is important for understanding ligand-receptor interactions. Pharmacophore models are derived from the common features of known active molecules and define the spatial relationships between these features. Several computational methods can be used to generate pharmacophore hypotheses, including systematic search, distance geometry, and clique detection algorithms.
ADMET properties prediction using AI will accelerate the process of drug discovery.
This slide mostly focuses on using graph-based deep learning techniques to predict drug properties.
This document discusses molecular docking, which is a computational method used in structure-based drug design to predict the preferred orientation of molecules when bound to their protein targets to form stable complexes. It begins by introducing drug discovery and computational chemistry approaches. It then defines molecular docking and describes different docking types and software. Applications of docking in modern drug discovery are presented, along with case studies and achievements that have resulted in new drug classes. The document concludes that docking contributes promisingly to drug discovery by aiding in target identification and lead optimization.
This document discusses quantitative structure-activity relationships (QSAR) modeling techniques. It introduces 2D-QSAR which uses molecular descriptors to correlate structure and activity. It also discusses 3D-QSAR techniques like CoMFA and CoMSIA which use 3D molecular fields/properties and statistical methods like PLS to model activity. These techniques are useful for drug design, virtual screening, and predicting absorption, distribution, metabolism, excretion properties.
The document discusses several key concepts in pharmacophore modeling:
1) A pharmacophore defines the important chemical features shared among active molecules, such as hydrogen bond donors/acceptors and hydrophobic regions.
2) Bioisosteres are atoms or groups with similar physical/chemical properties that produce similar biological effects.
3) 3D pharmacophores specify the spatial relationships between features as distance ranges and angles.
4) Constrained systematic searching and ensemble distance geometry are used to identify pharmacophores from a set of molecules while considering multiple conformations.
5) Clique detection identifies all possible combinations of pharmacophoric groups in molecules by finding "maximal completely connected subgraphs".
1) Pharmacophores are sets of steric and electronic features common to active drug molecules that interact with biological targets in a specific way. They include features like hydrogen bond donors/acceptors and hydrophobic regions.
2) Feature trees (Ftrees) are a ligand-based approach that represents molecules as trees to capture major building blocks and overall alignment in a conformation-independent way, supporting "lead hopping" between chemical classes.
3) Ftrees describe molecular fragments as nodes labeled with shape and chemical descriptors. Molecules are compared by matching subtrees using topology-preserving search algorithms. This allows identification of actives from different chemical scaffolds.
This document discusses pharmacophore mapping for the NS1 protein of H1N1 viruses. It provides background on drug design, including structure-based drug design and ligand-based drug design. It also discusses virtual screening techniques and describes the H1N1 (swine flu) virus. The document defines pharmacophores and discusses the "lock and key" model of molecular recognition. It concludes by describing two pharmacophore mapping software programs, Discovery Studio and LigandScout, that can be used to model protein-ligand interactions and create pharmacophores.
This document is a Q&A with Sean Ekins about his work developing pharmacophores for drug discovery. Some key points:
- He started using pharmacophores in 1996 to understand CYP2B6 substrates, and found them intuitive and useful for predicting new molecules.
- Pharmacophores allowed him to work on more complex endpoints beyond just P450 enzymes, including transporters.
- While some of his early work lacked high resolution images, the models still provided insights and suggested features to look for or avoid in new molecules.
- Over time he expanded his use of pharmacophores to understand target evolution, drug repurposing, environmental impacts, and more. Transporters are an area
Pharmacophore based ligand-designing_using_substructure_searching_to_explore_...Prasanthperceptron
The document describes a protocol for identifying new chemical entities (NCEs) using pharmacophore-based ligand design and substructure searching. It involves taking a known biologically active "pivot" molecule, searching for similar structures in PubChem based on smiles or InChi key, uploading matched structures to PharmaGist for pharmacophore analysis, and interpreting the results to identify potential new ligands that maintain essential pharmacophore features. The goal is to discover novel molecules for patenting while leveraging knowledge of established pharmacophores.
1) The document discusses the basics of drug design including defining the disease process, identifying targets for drug design like enzymes, receptors and nucleic acids, and the different approaches of ligand-based drug design and structure-based drug design.
2) It also covers important techniques in drug design like computer-aided drug design using computational methods, quantitative structure-activity relationships (QSAR), and the uses of computer graphics in molecular modeling and dynamics simulations.
3) Important experimental techniques discussed are x-ray crystallography and NMR spectroscopy that provide structural information for target biomolecules essential for structure-based drug design.
Simplified receptor based pharmacophore approach to retrieve potent ptp lar i...rajmaha9
Simplified Receptor Based Pharmacophore Approach to Retrieve Potent PTP-LAR Inhibitors Using Apoenzyme
M. Elizabeth Sobhia*
Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S.A.S.
Nagar, Punjab 160062, India
Abstract: The design of biological active compounds from the apoenzyme is still a challenging task. Herein a simple yet efficient technique is reported to generate a receptor based pharmacophore solely using a ligand-free protein crystal structure. Human leukocyte antigen-related phosphatase (PTP-LAR) is an apoenzyme and a receptor like transmembrane phosphatase that has emerged as a drug target for diabetes, obesity and cancer. The prior knowledge of the active residues responsible for the mechanism of action of the protein was used to generate the LUDI interaction map. Then, the complement negative image of the binding site was used to generate the pharmacophore features. A unique strategy was
followed to design a pharmacophore query maintaining crucial interactions with all the active residues, essential for the enzyme inhibition. The same query was used to screen several databases consisting of the Specs, IBS, iniMaybridge, NCI and an in-house PTP inhibitor databases. In order to overcome the common bioavailability problem associated with phosphatases, the hits obtained were filtered by Lipinski’s Rule of Five, SADMET properties and validated by docking studies in Glide and GOLD. These docking studies not only suggest the essential ligand binding interactions but also the binding patterns necessary for the LAR inhibition. The ligand pharmacophore mapping studies further validated the
screened protocol and supported that the final screened molecules, presumably, showed potent inhibitory activity.
Subsequently, these molecules were subjected to Derek toxicity predictions and nine new molecules with different
scaffold were obtained as non-toxic PTP-LAR inhibitors. The present prospective strategy is a powerful technique to
identify potent inhibitors using the protein 3D structure alone and is a valid alternative to other structure-based and
random docking approaches.
The students created a pharmacophore model using LigandScout to identify hydrophobic regions of selected drug targets. They took previously identified benzene molecules and separated them into files using Pymol. Pharmacophore features were then generated for each molecule in LigandScout and merged to create an overall pharmacophore model. This model will be used by another group to virtually pre-screen over 19 million drug-like compounds and identify potential hits.
This document provides a bibliography or list of references for the topic of "Identification and Validation of Drug Targets". It includes over 150 references in APA citation format from various sources like published papers, patents, books and reports. The references are grouped alphabetically and provide information to support further research on drug target identification and validation methods.
Many emerging companies make the mistake of putting all of their resources into immediate needs, and often neglect longterm regulatory strategy concerns when it comes to submissions and approvals. Don’t neglect the strategy piece in your planning! This lunch will provide a deep-dive foundation of how to develop a regulatory strategy. Topics to be addressed include:
What are different types of regulatory submissions for devices?
What are current trends in regulatory agencies?
What regulations around devices affect your organization?
Attendees will have the opportunity to ask questions with their company’s needs in mind.
Join us and Halloran Consulting at M2D2 for this expert lunch. Food will be served.
This document discusses chemoinformatics and its role in distance education. It defines chemoinformatics as the application of informatics methods to solve chemical problems, involving the design, creation, organization, management, analysis and use of chemical information. The document then outlines proposed courses for a distance learning program in chemoinformatics, including introductory courses covering fundamental concepts as well as more advanced topics involving programming, databases, and molecular modeling. It concludes by discussing how chemoinformatics can help improve distance education programs in chemistry fields.
This document discusses cheminformatics and various computer representations of molecules. It describes cheminformatics as encompassing concepts and techniques like molecular similarity, QSAR, substructure search, and molecular depiction/encoding. Common file formats like MOL and SMILES are introduced for representing molecular structures on computers. Fingerprints and other methods of measuring molecular similarity are also summarized.
Targeted drug delivery aims to increase the concentration of drugs in specific tissues while reducing systemic toxicity. It can target drugs to the first order (specific organs), second order (specific cell types), or third order (intracellular sites). Approaches include direct application to affected areas, passive accumulation through leaky vasculature, and active targeting using ligands. Parameters that determine efficacy are the size and blood flow of the target, and number of binding sites. Passive targeting uses physiological or physicochemical factors while active targeting uses carriers functionalized with targeting ligands. Main approaches are retrometabolic systems using drug-carrier complexes and prodrugs that are activated after biotransformation. Examples of targeted delivery systems discussed are magnetic nanoparticles, liposomes
This document summarizes Ebsa Bushura's senior seminar presentation on using mycobacterium metabolic pathways as drug targets. It discusses several key metabolic pathways in mycobacteria, such as mycobactin biosynthesis, peptidoglycan biosynthesis, and polyketide sugar unit biosynthesis, that are unique to the pathogen and could be targeted by drugs. It also mentions pathways involved in virulence and persistence, such as the glyoxylate bypass, that represent potential drug targets. The document reviews current approaches to identifying essential genes and those involved in persistence as potential novel anti-tuberculosis drug targets.
Cheminformatics plays a key role in modern drug discovery by helping chemists organize and analyze the vast amounts of chemical data being produced. It combines fields like chemistry, biology, and informatics to transform data into knowledge. Specifically, cheminformatics aids in tasks like identifying drug targets, finding lead compounds, optimizing leads, and conducting pre-clinical trials through methods such as high-throughput screening, structure-activity modeling, and predictive toxicity analysis. It also provides tools for tasks like drawing and searching chemical structures in databases.
Hepatitis B is a liver infection caused by the HBV virus. It can lead to liver failure or cancer. The core protein and viral DNA polymerase (P protein) are potential drug targets. Lamivudine and telbivudine are approved drugs that target the P protein. Derivatives of these drugs were created and their properties analyzed. Docking showed that a lamivudine derivative followed Lipinski's rules best and could be a future potential treatment for hepatitis B.
Sequence alig Sequence Alignment Pairwise alignment:-naveed ul mushtaq
Sequence Alignment Pairwise alignment:- Global Alignment and Local AlignmentTwo types of alignment Progressive Programs for multiple sequence alignment BLOSUM Point accepted mutation (PAM)PAM VS BLOSUM
This document discusses pharmacophore approach in rational drug design. It begins by defining a pharmacophore as an abstract description of molecular features necessary for molecular recognition by a biological macromolecule. It then discusses pharmacophore mapping, methods of pharmacophore screening, and applications. Key steps in developing a pharmacophore model include selecting a training set of ligands, conformational analysis, molecular superposition, abstraction, and validation. Pharmacophore models can be used to retrieve potential drug leads from databases and design new molecules.
Bioinformatics role in Pharmaceutical industriesMuzna Kashaf
Bioinformatics plays a key role in the pharmaceutical industry by enabling target identification of diseases, rational drug design, compound refinement, and other processes. It facilitates identifying target diseases and compounds, detecting molecular bases of diseases, designing drugs, refining compounds, and testing drug solubility and effects. Bioinformatics supports various stages of drug development including formulation, crystallization determination, polymer modeling, and testing before human use. Its integration into the pharmaceutical industry supports drug discovery, healthcare advances, and realizing the promises of projects like the Human Genome Project.
Computational chemistry plays an important role in drug design and discovery. The presentation outlines how computational methods can be used to identify hit compounds with activity against drug targets, improve lead compounds' potency, and optimize lead compounds into drug-like molecules. It discusses various computational approaches like molecular docking, molecular dynamics, and quantum mechanics that facilitate hit identification, lead generation and optimization. The goal is to computationally design safe and effective drug molecules that have qualities like bioavailability, metabolic stability, selectivity for target tissues, and minimal side effects.
The document discusses the applications of bioinformatics in drug discovery. It describes how bioinformatics supports computer-aided drug design through computational methods to simulate drug-receptor interactions. It also discusses how virtual high-throughput screening can identify compounds that strongly bind to protein targets. The document outlines the key steps in drug design, including identifying the disease target, studying lead compounds, rational drug design techniques, and testing drugs. It emphasizes that bioinformatics can predict important drug characteristics like absorption and toxicity to save costs during development.
Bioinformatics plays an important role in drug discovery and development by enabling target identification, rational drug design, compound refinement, and other processes. Key applications of bioinformatics include virtual screening of large compound libraries to identify potential drug leads, homology modeling of protein structures to inform drug design, and similarity searches to find analogs of existing drug molecules. The overall drug development process involves studying the disease, identifying drug targets, designing compounds, testing and refining candidates, and conducting clinical trials. Computational techniques expedite many steps but experimental validation is still needed.
MOLECULAR DOCKING IN DRUG DESIGN AND DEVELOPMENT BY PRANAVI linkedin.pptxPranavi Uppuluri
This document discusses molecular docking, which is a technique used in bioinformatics and drug design to predict how biological molecules, like proteins and ligands, bind to each other. It begins by defining bioinformatics and explaining why molecular docking is important for applications like drug design and signal transduction. The document then discusses key concepts in molecular docking like rigid and flexible docking, different docking software tools, and challenges associated with molecular docking.
In spite of extensive effort by industry and academia to develop new drugs, there are still several diseases that are in need of therapeutic agents and have yet to be developed.
10 years the identification rate of disease-associated targets has been higher than the therapeutics identification rate.
Nevertheless, it is apparent that computational tools provide high hopes that many of the diseases under investigation can be brought under control.
1. Bioinformatics uses computer science and information technology to analyze biological data and assist with drug discovery. It helps identify drug targets and design drug candidates.
2. The drug design process involves identifying a disease target, studying compounds of interest, detecting molecular disease bases, rational drug design, refinement, and testing. Bioinformatics tools assist with each step.
3. CADD uses computational methods to simulate drug-receptor interactions and is heavily dependent on bioinformatics tools and databases. It supports techniques like virtual screening, sequence analysis, homology modeling, and physicochemical modeling to aid drug development.
Pharmaceutical companies use computers in many aspects of the drug discovery process. Computers allow researchers to analyze thousands of molecular structures and rapidly search databases to identify promising drug candidates that can bind to disease targets. They use computational modeling and simulations to predict how well a molecule will bind to and affect its target. This helps streamline the process of discovering and developing new drugs compared to traditional trial-and-error methods. Computers play a key role in expediting tasks from target identification to lead optimization and preclinical testing.
Computational modeling in drug dispositionSUJITHA MARY
This document discusses computational modeling techniques for predicting drug disposition properties. It covers modeling approaches for drug absorption, distribution, and excretion. For absorption, it describes models for predicting solubility, intestinal permeability, and transporters. For distribution, it discusses models for volume of distribution, plasma protein binding, and blood-brain barrier permeability. For excretion, it summarizes models for hepatic and renal clearance. Current challenges include incorporating active transporters and generating predictive models from physiological understanding rather than empirical correlations.
Computers play several important roles in the drug discovery process:
1) They analyze thousands of molecular structures and properties to identify candidate molecules that may bind to disease targets. This virtual screening allows faster evaluation of large libraries.
2) Databases organize data on chemical structures to facilitate computer-aided searches for promising drug candidates.
3) Software allows scientists to visualize and model molecular interactions, guiding the design of molecules that optimally bind to targets.
This document discusses strategies for drug repurposing to treat COVID-19. It begins with an introduction to COVID-19 and issues with conventional drug development. It then covers drug repurposing approaches like in silico screening using molecular docking and dynamics simulations to identify existing drugs that may bind virus targets. Examples of drugs being repurposed for COVID-19 that are in clinical trials are mentioned, including remdesivir, hydroxychloroquine, and favipiravir. Overall strategies for drug repurposing including computational methods and examples of their application to COVID-19 are summarized.
Computer-aided design (CAD) is the use of computers (or workstations) to aid in the creation, modification, analysis, or optimization of a design: 3 This software is used to increase the productivity of the designer, improve the quality of design, improve communications through documentation, and to create a database for manufacturing: 4 Designs made through CAD software are helpful in protecting products and inventions when used in patent applications. CAD output is often in the form of electronic files for print, machining, or other manufacturing operations. The terms computer-aided drafting (CAD) and computer-aided design and drafting (CADD) are also used
Computational modelling of drug disposition lalitajoshi9
computational modelling of drug disposition is the integral part of computer aided drug design. different kinds of tools being used in the prediction of drug disposition in human body. This topic in the CADD explains the details about the drug disposition, active transporters and tools.
Rational drug design involves identifying a biological target related to a disease, determining the target's structure and function, and designing drug molecules that interact with the target in a beneficial way. Key aspects of rational drug design include using computational tools to model protein targets based on their 3D structure, designing drugs that complement the target's active site, and generating new drug leads through database searching and de novo design methods. The goal is to develop effective medications in a time and cost efficient manner by applying knowledge of a drug target's molecular properties.
The Cutting Edge of Chemistry, Apr. - Jun. 2010 -- Pharma Matters ReportThomson Reuters
The document summarizes the latest issue of "The Cutting Edge of Chemistry", a report on new developments in medicinal chemistry research. The report is divided into sections that cover organic synthesis schemes, molecular scaffolds, new mechanisms of action, and emerging drug candidates. The main article discusses an efficient large-scale synthesis of EDP-420, a first-in-class ketolide antibiotic currently in phase II clinical trials for respiratory infections. The ten-step synthesis process is outlined, along with modifications needed to scale it up from discovery to pilot plant levels.
Molecular docking and its importance in drug designdevilpicassa01
The document discusses molecular docking and its importance in drug design. Molecular docking is a method used to predict how two molecules, such as a drug and a protein, fit together and interact with one another. It can be used to find potential inhibitors for target proteins and aid in rational drug design. Using molecular docking can help reduce the time and costs associated with new drug discovery by cutting down the research timeline through computer modeling and simulation. It is a key technique in modern computational biology and drug development.
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
論文紹介:A Systematic Survey of Prompt Engineering on Vision-Language Foundation ...Toru Tamaki
Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr "A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models" arXiv2023
https://arxiv.org/abs/2307.12980
Are you interested in dipping your toes in the cloud native observability waters, but as an engineer you are not sure where to get started with tracing problems through your microservices and application landscapes on Kubernetes? Then this is the session for you, where we take you on your first steps in an active open-source project that offers a buffet of languages, challenges, and opportunities for getting started with telemetry data.
The project is called openTelemetry, but before diving into the specifics, we’ll start with de-mystifying key concepts and terms such as observability, telemetry, instrumentation, cardinality, percentile to lay a foundation. After understanding the nuts and bolts of observability and distributed traces, we’ll explore the openTelemetry community; its Special Interest Groups (SIGs), repositories, and how to become not only an end-user, but possibly a contributor.We will wrap up with an overview of the components in this project, such as the Collector, the OpenTelemetry protocol (OTLP), its APIs, and its SDKs.
Attendees will leave with an understanding of key observability concepts, become grounded in distributed tracing terminology, be aware of the components of openTelemetry, and know how to take their first steps to an open-source contribution!
Key Takeaways: Open source, vendor neutral instrumentation is an exciting new reality as the industry standardizes on openTelemetry for observability. OpenTelemetry is on a mission to enable effective observability by making high-quality, portable telemetry ubiquitous. The world of observability and monitoring today has a steep learning curve and in order to achieve ubiquity, the project would benefit from growing our contributor community.
How RPA Help in the Transportation and Logistics Industry.pptxSynapseIndia
Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.
Best Practices for Effectively Running dbt in Airflow.pdfTatiana Al-Chueyr
As a popular open-source library for analytics engineering, dbt is often used in combination with Airflow. Orchestrating and executing dbt models as DAGs ensures an additional layer of control over tasks, observability, and provides a reliable, scalable environment to run dbt models.
This webinar will cover a step-by-step guide to Cosmos, an open source package from Astronomer that helps you easily run your dbt Core projects as Airflow DAGs and Task Groups, all with just a few lines of code. We’ll walk through:
- Standard ways of running dbt (and when to utilize other methods)
- How Cosmos can be used to run and visualize your dbt projects in Airflow
- Common challenges and how to address them, including performance, dependency conflicts, and more
- How running dbt projects in Airflow helps with cost optimization
Webinar given on 9 July 2024
Comparison Table of DiskWarrior Alternatives.pdfAndrey Yasko
To help you choose the best DiskWarrior alternative, we've compiled a comparison table summarizing the features, pros, cons, and pricing of six alternatives.
An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)
YOUR RELIABLE WEB DESIGN & DEVELOPMENT TEAM — FOR LASTING SUCCESS
WPRiders is a web development company specialized in WordPress and WooCommerce websites and plugins for customers around the world. The company is headquartered in Bucharest, Romania, but our team members are located all over the world. Our customers are primarily from the US and Western Europe, but we have clients from Australia, Canada and other areas as well.
Some facts about WPRiders and why we are one of the best firms around:
More than 700 five-star reviews! You can check them here.
1500 WordPress projects delivered.
We respond 80% faster than other firms! Data provided by Freshdesk.
We’ve been in business since 2015.
We are located in 7 countries and have 22 team members.
With so many projects delivered, our team knows what works and what doesn’t when it comes to WordPress and WooCommerce.
Our team members are:
- highly experienced developers (employees & contractors with 5 -10+ years of experience),
- great designers with an eye for UX/UI with 10+ years of experience
- project managers with development background who speak both tech and non-tech
- QA specialists
- Conversion Rate Optimisation - CRO experts
They are all working together to provide you with the best possible service. We are passionate about WordPress, and we love creating custom solutions that help our clients achieve their goals.
At WPRiders, we are committed to building long-term relationships with our clients. We believe in accountability, in doing the right thing, as well as in transparency and open communication. You can read more about WPRiders on the About us page.
Details of description part II: Describing images in practice - Tech Forum 2024BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and transcript: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
Choose our Linux Web Hosting for a seamless and successful online presencerajancomputerfbd
Our Linux Web Hosting plans offer unbeatable performance, security, and scalability, ensuring your website runs smoothly and efficiently.
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Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
Fluttercon 2024: Showing that you care about security - OpenSSF Scorecards fo...Chris Swan
Have you noticed the OpenSSF Scorecard badges on the official Dart and Flutter repos? It's Google's way of showing that they care about security. Practices such as pinning dependencies, branch protection, required reviews, continuous integration tests etc. are measured to provide a score and accompanying badge.
You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter.
The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.
20240704 QFM023 Engineering Leadership Reading List June 2024
Pharmacophore
1. REVIEWS Drug Discovery Today Volume 15, Numbers 11/12 June 2010
Pharmacophore modeling and
Reviews INFORMATICS
applications in drug discovery:
challenges and recent advances
Sheng-Yong Yang
State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan 610041, China
Pharmacophore approaches have become one of the major tools in drug discovery after the past
century’s development. Various ligand-based and structure-based methods have been developed for
improved pharmacophore modeling and have been successfully and extensively applied in virtual
screening, de novo design and lead optimization. Despite these successes, pharmacophore approaches
have not reached their expected full capacity, particularly in facing the demand for reducing the current
expensive overall cost associated with drug discovery and development. Here, the challenges of
pharmacophore modeling and applications in drug discovery are discussed and recent advances and
latest developments are described, which provide useful clues to the further development and
application of pharmacophore approaches.
Introduction lead optimization and multitarget drug design (Fig. 1). A variety of
The concept of pharmacophore was first introduced in 1909 by automated tools for pharmacophore modeling and applications
Ehrlich [1], who defined the pharmacophore as ‘a molecular appeared constantly after the advances in computational chem-
framework that carries (phoros) the essential features responsible istry in the past 20 years; these pharmacophore modeling tools,
for a drug’s (pharmacon) biological activity’. After a century’s together with their inventor(s) and typical characteristics, are
development, the basic pharmacophore concept still remains summarized in Supplementary Table S1. Many successful stories
unchanged, but its intentional meaning and application range of pharmacophore approaches in facilitating drug discovery have
have been expanded considerably. According to the very recent been reported in recent years [6,7]. The pharmacophore approach,
definition by IUPAC [2], a pharmacophore model is ‘an ensemble however, still faces many challenges that limit its capability to
of steric and electronic features that is necessary to ensure the reach its expected potential, particularly with the demand for
optimal supramolecular interactions with a specific biological reducing the current high cost associated with the discovery
target and to trigger (or block) its biological response’. Apart from and development of a new drug. This article discusses the chal-
this official definition, some other similar definitions, as well as lenges of pharmacophore modeling and applications in drug
remarks, have been described in the literature [3–5]. The overall discovery and reviews the most recent advances in dealing with
development and history of the pharmacophore concept through these challenges.
¨
the past century has been reviewed by Gund [3] and Wermuth [4].
A pharmacophore model can be established either in a ligand- Ligand-based pharmacophore modeling
based manner, by superposing a set of active molecules and Ligand-based pharmacophore modeling has become a key com-
extracting common chemical features that are essential for their putational strategy for facilitating drug discovery in the absence of
bioactivity, or in a structure-based manner, by probing possible a macromolecular target structure. It is usually carried out by
interaction points between the macromolecular target and extracting common chemical features from 3D structures of a
ligands. Pharmacophore approaches have been used extensively set of known ligands representative of essential interactions
in virtual screening, de novo design and other applications such as between the ligands and a specific macromolecular target. In
general, pharmacophore generation from multiple ligands
E-mail address: yangsy@scu.edu.cn. (usually called training set compounds) involves two main steps:
444 www.drugdiscoverytoday.com 1359-6446/06/$ - see front matter ß 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.drudis.2010.03.013
2. Drug Discovery Today Volume 15, Numbers 11/12 June 2010 REVIEWS
[13]. The first approach has the advantage of lower computing cost
for conducting molecular alignment at the expense of a possible
need for a mass storage capacity. The second approach does not
need mass storage but might need higher CPU time for conducting
rigorous optimization. It has been demonstrated that the pre-
enumerating method outperforms the on-the-fly calculation
approach [15]. Currently, a substantial number of advanced algo-
rithms have been established to sample the conformational spaces
of small molecules, which are listed in Supplementary Table S2.
Reviews INFORMATICS
Some of these algorithms, such as poling restraints [16], systematic
torsional grids [17], directed tweak [18], genetic algorithms [19]
and Monte Carlo [20], have been implemented in various com-
mercial and academic pharmacophore modeling programs. Never-
theless, a good conformation generator should satisfy the
following conditions: (i) efficiently generating all the putative
bound conformations that small molecules adopt when they
interact with macromolecules, (ii) keeping the list of low-energy
conformations as short as possible to avoid the combinational
explosion problem and (iii) being less time-consuming for the
conformational calculations. Several new or modified tools devel-
oped recently for conformational generation seem to outperform
the previous algorithms in some aspects. MED-3DMC, developed
by Sperandio et al. [21], uses a combination of the Metropolis
Monte Carlo algorithm, based on a SMARTS mapping of the
rotational bond, and the MMFF94 van der Waals energy term.
FIGURE 1 MED-3DMC has been reported to outperform Omega when
The full framework of pharmacophore architecture. applied on certain molecules with a low to medium number of
rotatable bonds [21]. Liu et al. [22] developed a conformation
sampling method named ‘Cyndi’, which is based on a multiob-
creating the conformational space for each ligand in the training jective evolution algorithm. Cyndi was validated to be markedly
set to represent conformational flexibility of ligands, and aligning superior to other conformation generators in reproducing the
the multiple ligands in the training set and determining the bioactive conformations against a set of 329 testing structures
essential common chemical features to construct pharmacophore [22]. CAESAR [23] is another conformer generator, which is based
models. Handling conformational flexibility of ligands and con- on a divide-and-conquer and recursive conformer buildup
ducting molecular alignment represent the key techniques and approach. This approach also takes into consideration local rota-
also the main difficulties in ligand-based pharmacophore model- tional symmetry to enable the elimination of conformer dupli-
ing. Currently, various automated pharmacophore generators cates owing to topological symmetry in the systematic search.
have been developed, including commercially available software CAESAR has been demonstrated to be consistently 5–20 times
– such as HipHop [8], HypoGen [9] (Accelrys Inc., http://www.ac- faster than Catalyst/FAST.1 The speedup is even more notable
celrys.com), DISCO [10], GASP [11], GALAHAD (Tripos Inc., for molecules with high topological symmetry or for molecules
¨
http://www.tripos.com), PHASE [12] (Schrodinger Inc., http:// that require a large number of conformational samplings.
www.schrodinger.com) and MOE (Chemical Computing Group, Molecular alignment is the second challenging issue in ligand-
http://www.chemcomp.com) – and several academic programs. based pharmacophore modeling. The alignment methods can be
These programs differ mainly in the algorithms used for handling classified into two categories in terms of their fundamental nature:
the flexibility of ligands and for the alignment of molecules, which point-based and property-based approaches [15]. The points (in
are outlined in Supplementary Table S1. There are some references the point-based method) can be further differentiated as atoms,
in literature, such as Refs. [5,13,14], showing the differences, fragments or chemical features [5]. In point-based algorithms,
advantages and disadvantages of these programs; however, pairs of atoms, fragments or chemical feature points are usually
describing and analyzing the different programs is not our goal superimposed using a least-squares fitting. The biggest limitation
here. of these approaches is the need for predefined anchor points
Despite the great advances, several key challenges in ligand- because the generation of these points can become problematic
based pharmacophore modeling still exist. The first challenging in the case of dissimilar ligands. The property-based algorithms
problem is the modeling of ligand flexibility. Currently, two make use of molecular field descriptors, usually represented by sets
strategies have been used to deal with this problem: the first is of Gaussian functions, to generate alignments. The alignment
the pre-enumerating method, in which multiple conformations optimization is carried out with some variant of similarity measure
for each molecule are precomputed and saved in a database [13].
The second is the on-the-fly method, in which the conformation 1
Catalyst is now incorporated into Discovery Studio, available from Accelrys
analysis is carried out in the pharmacophore modeling process Inc., San Diego, CA, USA.
www.drugdiscoverytoday.com 445
3. REVIEWS Drug Discovery Today Volume 15, Numbers 11/12 June 2010
Reviews INFORMATICS
FIGURE 2
Pharmacophore models of cyclin-dependent kinase 2 (CDK2) inhibitors. (a) Pharmacophore models of CDK2 inhibitors developed using Catalyst by Hecker et al.
[28], Toba et al. [29] and Vadivelan et al. [30]. (b) The basic process for the generation of multicomplex-based comprehensive pharmacophore map of CDK2
inhibitors. The chemical features are color coded: green, hydrogen-bond acceptor; magenta, hydrogen-bond donor; light blue, hydrophobic feature; orange,
aromatic ring.
of the intermolecular overlap of the Gaussians as the objective ligand complex. The protocol of structure-based pharmacophore
function. Conventional molecular alignment algorithms have modeling involves an analysis of the complementary chemical
been extensively reviewed elsewhere [15]. New alignment meth- features of the active site and their spatial relationships, and a
ods continue to be actively developed. Recently developed meth- subsequent pharmacophore model assembly with selected fea-
ods include stochastic proximity embedding [24], atomic property tures. The structure-based pharmacophore modeling methods
fields [25], fuzzy pattern recognition [26] and grid-based interac- can be further classified into two subcategories: macromolecule–
tion energies [27]. ligand-complex based and macromolecule (without ligand)-based.
Another challenging problem lies in the practical task of proper The macromolecule–ligand-complex-based approach is conveni-
selection of training set compounds. This problem, apparently ent in locating the ligand-binding site of the macromolecular
being simple and non-technical, often confuses users, even experi- target and determining the key interaction points between ligands
enced ones. It has been demonstrated that the type of ligand and macromolecule. LigandScout [31] is an excellent representa-
molecules, the size of the dataset and its chemical diversity affect tion that incorporates the macromolecule–ligand-complex-based
the final generated pharmacophore model considerably [13]. In scheme. Other macromolecule–ligand-complex-based pharmaco-
some cases, completely different pharmacophore models of phore modeling programs include Pocket v.2 [32] and GBPM [33].
ligands interacting with the same macromolecular target could The limitation of this approach is the need for the 3D structure of
be generated from the same algorithm and program that uses macromolecule–ligand complex, implying that it cannot be
different training sets. For example, Hecker et al. [28], Toba applied to cases when no compounds targeting the binding site
et al. [29] and Vadivelan et al. [30] have independently generated of interest are known. This can be overcome by the macromole-
three pharmacophore models of cyclin-dependent kinase 2 cule-based approach. The structure-based pharmacophore (SBP)
(CDK2) inhibitors. They used the same program, Catalyst, but method2 implemented in Discovery Studio3 is a typical example of
different training sets. The three pharmacophore models are found a macromolecule-based approach. SBP converts LUDI [34] inter-
to be totally different from one another in terms of the feature action maps within the protein-binding site into Catalyst phar-
categories, as well as the location constraints of features (Fig. 2a), macophoric features: H-bond acceptor, H-bond donor and
for which a further discussion is presented in a subsequent section hydrophobe. The main limitation of the SBP flowchart is that
of this review. the derived interaction maps generally consist of a large number of
Structure-based pharmacophore modeling 2
SBP is now incorporated into Discovery Studio, available from Accelrys Inc.,
Structure-based pharmacophore modeling works directly with the San Diego, CA, USA.
3
3D structure of a macromolecular target or a macromolecule– Discovery Studio available from Accelrys Inc., San Diego, CA, USA.
446 www.drugdiscoverytoday.com
4. Drug Discovery Today Volume 15, Numbers 11/12 June 2010 REVIEWS
unprioritized Catalyst features, which complicates its application between known CDK2 inhibitors and the molecules of focused
in such tasks as 3D database searches. To overcome this problem, inactive dataset but also that it is capable of correctly predicting
Barillari et al. [35] recently proposed a fast knowledge-based the activities of a wide variety of CDK2 inhibitors in an external
approach, hot-spots-guided receptor-based pharmacophores (HS- active dataset [38].
Pharm). This approach enables the prioritization of cavity atoms
that should be targeted for ligand binding, by training machine Pharmacophore-model-based virtual screening
learning algorithms with atom-based fingerprints of known Once a pharmacophore model is generated by either the ligand-
ligand-binding pockets. Tintori et al. [36] have also reported based or the structure-based approach, it can be used for querying
another apoprotein-based approach. With this approach, the the 3D chemical database to search for potential ligands, which is
Reviews INFORMATICS
GRID [37] molecular interaction fields (MIFs) are first calculated so-called ‘pharmacophore-based virtual screening’ (VS). Pharma-
by using different probes for the binding site of interest, followed cophore-based VS and docking-based VS represent the mainstream
by the selection and subsequent conversion of the points of of VS tools at the present time. In contrast to its counterpart, the
minimum of MIFs into pharmacophoric features. docking-based VS method, pharmacophore-based VS reduces the
A frequently encountered problem for structure-based pharma- problems arising from inadequate consideration of protein flex-
cophore modeling, not only the macromolecule-based approach, ibility or the use of insufficiently designed or optimized scoring
is that too many chemical features (generally not prioritized) can functions by introducing a tolerance radius for each pharmaco-
be identified for a specific binding site of the macromolecular phoric feature.
target. However, a pharmacophore model composed of too many In the pharmacophore-based VS approach, a pharmacophore
chemical features (for example, 7 chemical features) is not hypothesis is taken as a template. The purpose of screening is
suitable for practical applications, such as 3D database screening. actually to find such molecules (hits) that have chemical features
Thus, it is necessary to select a limited number of chemical features similar to those of the template. Some of these hits might be
(typically three to seven features) to construct a practical pharma- similar to known active compounds, but some others might be
cophore hypothesis, although this is not an easy task in many entirely novel in scaffold. The searching for compounds with
cases. Another problem is that the obtained pharmacophore different scaffolds, while sharing a biological activity is usually
hypothesis cannot reflect the quantitative structure–activity rela- called ‘scaffold hopping’ [39]. The screening process involves two
tionship (QSAR) because the model is derived just based on a single key techniques and difficulties: handling the conformational flex-
macromolecule–ligand complex or a single macromolecule. In an ibility of small molecules and pharmacophore pattern identifica-
attempt to overcome these problems, we, in a recent study, have tion. The strategies for handling the flexibility of small molecules
suggested using a multicomplex-based comprehensive map and in pharmacophore-based VS are very similar to those used in
most-frequent pharmacophore model [38]. In that study, a multi- pharmacophore modeling. Again, the flexibility of small mole-
complex-based method was used to generate a comprehensive cules is handled by either pre-enumerating multiple conforma-
pharmacophore map of CDK2 based on a collection of 124 crystal tions for each molecule in the database or conformational
structures of human CDK2 inhibitor complex. The chemical fea- sampling at search time. Pharmacophore pattern identification,
tures for each complex were first identified by LigandScout, fol- usually called ‘substructure searching’, is actually to check
lowed by clustering all the features to form a comprehensive whether a query pharmacophore is present in a given conformer
pharmacophore map. The established pharmacophore map con- of a molecule. The frequently used approaches for substructure
tains almost all the chemical features important for CDK2–inhi- searching are based on graph theory, which include Ullmann [40],
bitor interactions (Fig. 2b). We found that, with the exception of a the backtracking algorithm [41], and the GMA algorithm [42].
feature of aromatic ring (orange) in Hecker model, every pharma- Pharmacophore-based VS can be very time-consuming, espe-
cophore feature in the reported ligand-based models (Hecker cially in cases of screening large chemical databases with flexible
model, Toba model and Vadivelan model; Fig. 2a) can be matched molecules, which is currently a key challenge in pharmacophore-
to a feature in our comprehensive map, suggesting that these based VS. A commonly used method to speed up the screening
ligand-based models are subgraphs of our comprehensive map. process is the multilevel searching approach [5]. In this approach,
The only exception (the aromatic ring feature in Hecker model) a series of screening filters are applied to the molecules in an
seems to occur in ligand scaffolds. A chemical feature occurred in increasing order of complexity so that the first filters are fast and
small molecular scaffolds, which should be a pseudo-pharmaco- simple, whereas successive ones are more time-consuming but are
phore feature because it does not represent a ligand–macromole- applied only to a small subset of the entire database.
cule interaction, cannot be detected by a structure-based However, the most challenging problem for pharmacophore-
pharmacophore modeling approach. Because the comprehensive based VS is that in many cases, few percentages of the virtual hits
pharmacophore map is too restrictive and not suitable for the are really bioactive; in other words, the screening results bear a
virtual screening, a reduced model is needed for a real application. higher ‘false positive’ rate and/or a higher ‘false negative’ rate.
A feasible solution is to select the most-frequent features that were Many factors can contribute to this problem, including the quality
recognized as the features important to the activity of the CDK2 and composition of the pharmacophore model and whether and
inhibitors. Thus, the top-ranked seven features, which are present how much the macromolecular target information is involved.
in the 124 complexes with more than 25% probability, have been First, the most apparent factor is associated with the deficiency of a
selected and combined to form a most-frequent-feature pharma- pharmacophore hypothesis. To address this problem requires a
cophore model. Validation studies of the most-frequent-feature comprehensive validation and optimization to the pharmaco-
model have shown not only that it can discriminate successfully phore model. Various validation methods such as cross-validation
www.drugdiscoverytoday.com 447
5. REVIEWS Drug Discovery Today Volume 15, Numbers 11/12 June 2010
Reviews INFORMATICS
FIGURE 3
Schematic representation of the relationship between the full pharmacophore map identified by the structure-based approach and the pharmacophore models
established by the ligand-based method. The upper schematically shows the chemical features in the ligand-binding site of a macromolecular target. The middle
indicates the various pharmacophore models established by a ligand-based pharmacophore modeling method. The lower schematically depicts several possible
cases in pharmacophore-based virtual screening. A tick (H) means that the selected molecule conforms to the requirements of pharmacophore and the shape of
ligand-binding site. A cross (Â) indicates that the selected molecule does not satisfy the requirements of either pharmacophore or the shape of ligand-binding site
(because of the atomic bumping).
and test set method have been suggested, which were reviewed addition, most of the interactions between ligand and protein are
recently by Triballeau et al. [43]. The validation process is usually distance sensitive – particularly the short-range interactions, such
associated with model optimization. Lately, Sun et al. [44] have as the electrostatic interaction, for which a pharmacophore model
developed a genetic algorithm-guided pharmacophore query opti- is difficult to account for. An efficient approach is the synergistic
mization program, in which the optimization is carried out by combination of pharmacophore-based VS and docking-based VS.
automatically adjusting the position and tolerance radius of each Because inherent limitations of each of these screening techniques
pharmacophoric feature. The final query has been validated by are not easily resolved, their combination in a hybrid protocol can
using a test set method, which shows a considerably improved hit help to mutually compensate for these limitations and capitalize
rate. Second, because the pharmacophore model used for 3D query on their mutual strengths. Various combined virtual screening
is generally one of the subgraphs of the full pharmacophore map, strategies and their validity have been well reviewed by Talevi et al.
screening with this pharmacophore query might not retrieve [47], Kirchmair et al. [48] and Muegge [49]. This approach has also
molecules that match other subgraphs except for the selected been routinely used in our group, with which we have successfully
one, which is probably an important reason for the higher false obtained several real hits validated experimentally for inhibition
negative rate in some studies (Fig. 3). Third, the flexibility of target against protein kinases Aurora-A [50], Syk [51] and ALK5 [52].
macromolecule in pharmacophore approaches is handled by
introducing a tolerance radius for each pharmacophoric feature, Pharmacophore-based de novo design
which is unlikely to fully account for macromolecular flexibility in Besides the pharmacophore-based VS described above, another
some cases. Some recent attempts [45,46] to incorporate molecular application of pharmacophore is de novo design of ligands. The
dynamics simulations in pharmacophore modeling have sug- compounds obtained from pharmacophore-based VS are usually
gested that the dynamics pharmacophore models generated from existing chemicals, which might be patent protected. In contrast
MD simulation trajectories show considerably better representa- to pharmacophore-based VS, the de novo design approach can be
tion of the flexibility of pharmacophore. used to create completely novel candidate structures that conform
Another factor that might lead to the high false positive rate is to the requirements of a given pharmacophore. The first pharma-
that the steric restriction by the macromolecular target is not cophore-based de novo design program is NEWLEAD [53], which
sufficiently considered in pharmacophore models, although it is uses as input a set of disconnected molecular fragments that are
partly counted for by the consideration of excluded volumes. In consistent with a pharmacophore model, and the selected sets of
448 www.drugdiscoverytoday.com
6. Drug Discovery Today Volume 15, Numbers 11/12 June 2010 REVIEWS
disconnected pharmacophore fragments are subsequently con- Concluding remarks
nected by using linkers (such as atoms, chains or ring moieties). Pharmacophore approaches have evolved to be one of the most
Actually, NEWLEAD can only handle the cases in which the successful concepts in medicinal chemistry through the collective
pharmacophore features are concrete functional groups (not efforts of many researchers in the past century. In particular, con-
abstract chemical features). Other shortcomings of the NEWLEAD siderable progress of pharmacophore technology in the past two
program include that the sterically forbidden region of the binding decades has made pharmacophore approaches one of the main tools
site is not considered and that, as in traditional de novo design in drug discovery. Despite the advances in key techniques of phar-
programs, the compounds created by the NEWLEAD program macophore modeling, there is still room for further improvement to
might be difficult to chemically synthesize. Other programs such derive more accurate and optimal pharmacophore models, which
Reviews INFORMATICS
as LUDI4 and BUILDER [54] can also be used to combine identi- include better handling of ligand flexibility, more efficient mole-
fication of structure-based pharmacophore with de novo design. cular alignment algorithms and more accurate model optimization.
They, however, need the knowledge of 3D structures of the macro- Lower efficiency (computational time cost) and poor effect (lower
molecular targets. hit rate) of pharmacophore-based VS seriously obstructs the appli-
To overcome drawbacks of the current pharmacophore-based de cations of pharmacophore in drug discovery. The former, however,
novo design software, we have developed a new program, PhDD (a will be further reduced and diminished by the increasing capacity
pharmacophore-based de novo design method of drug-like mole- and reducing cost of computer hardware. ‘Synergistic’ combination
cules) [55]. PhDD can automatically generate drug-like molecules of pharmacophore method and other molecular modeling
that satisfy the requirements of an input pharmacophore hypoth- approaches such as docking is a good strategy to further improve
esis. The pharmacophore used in PhDD can be composed of a set of the effect. Compared with pharmacophore-based VS, pharmaco-
abstract chemical features and excluded volumes that are the phore-based de novo design shows a unique advantage in building
sterically forbidden region of the binding site. PhDD first generates completely novel hit compounds. In addition to virtual screening
a set of new molecules that completely conform to the require- and de novo design, the applications of pharmacophore have also
ments of the given pharmacophore model. Then a series of assess- been extended to lead optimization [56], multitarget drug design
ments to the generated molecules are carried out, including [57], activity profiling [58] and target identification [59]. The
assessments of drug-likeness, bioactivity and synthetic accessibil- increasing application ranges of pharmacophore, together with
ity. PhDD was tested on three typical examples: pharmacophore success stories in drug discovery, enable further enrichment of
hypotheses of histone deacetylase, CDK2 and HIV-1 integrase the pharmacophore concept and promote the development and
inhibitors. The test results showed that PhDD was able to generate application of pharmacophore approaches.
molecules with completely novel scaffolds. A similarity analysis
with the use of Tanimoto coefficients demonstrated that the Appendix A. Supplementary data
generated molecules should have similar biological functions to Supplementary data associated with this article can be found, in
the existing inhibitors, although they are structurally different. the online version, at doi:10.1016/j.drudis.2010.03.013.
References
1 Ehrlich, P. (1909) Ueber den jetzigen Stand der Chemotherapie. Ber. Dtsch. Chem. 10 Martin, Y.C. (2000) DISCO: what we did right and what we missed. In
Ges. 42, 17–47 ¨
Pharmacophore Perception, Development, and Use in Drug Design (Guner, O.F., ed.), pp.
2 Wermuth, C.G. et al. (1998) Glossary of terms used in medicinal chemistry (IUPAC 49–68, International University Line
Recommendations 1997). Annu. Rep. Med. Chem. 33, 385–395 11 Jones, G. and Willet, P. (2000) GASP: genetic algorithm superimposition program. In
¨
3 Gund, P. (2000) Evolution of the pharmacophore concept in pharmaceutical ¨
Pharmacophore Perception, Development, and Use in Drug Design (Guner, O.F., ed.), pp.
research. In Pharmacophore Perception, Development, and Use in Drug Design (Guner,¨ 85–106, International University Line
O.F., ed.), pp. 171–189, International University Line (IUL) 12 Dixon, S.L. et al. (2006) PHASE: a new engine for pharmacophore perception, 3D
4 Wermuth, C.G. (2006) Pharmacophores: historical perspective and viewpoint QSAR model development, and 3D database screening. 1. Methodology and
from a medicinal chemist. In Pharmacophores and Pharmacophore Searches (Langer, preliminary results. J. Comput. Aid. Mol. Des. 20, 647–671
T. and Hoffmann, R.D., eds), pp. 3–13, Wiley–VCH 13 Poptodorov, K. et al. (2006) Pharmacophore model generation software tools. In
5 Dror, O. et al. (2006) Predicting molecular interactions in silico. I. An updated guide Pharmacophores and Pharmacophore Searches (Langer, T. and Hoffmann, R.D., eds), pp.
to pharmacophore identification and its applications to drug design. Front. Med. 17–47, Wiley–VCH
Chem. 3, 551–584 14 Van Drie, J.H. (2004) Pharmacophore discovery: a critical review. In Computational
6 Kubinyi, H. (2006) Success stories of computer-aided design. In Computer Medicinal Chemistry for Drug Discovery (Bultinck, P., ed.), pp. 437–460, Marcel Dekker
Applications in Pharmaceutical Research and Development (Ekins, S., ed.), pp. 377–424, 15 Wolber, G. et al. (2008) Molecule-pharmacophore superpositioning and pattern
Wiley–Interscience matching in computational drug design. Drug Discov. Today 13, 23–29
7 Mustata, G. et al. (2009) Discovery of novel Myc-Max heterodimer disruptors with a 16 Smellie, A. et al. (1994) Poling: promoting conformational variation. J. Comput.
three-dimensional pharmacophore model. J. Med. Chem. 52, 1247–1250 Chem. 16, 171–187
8 Barnum, D. et al. (1996) Identification of common functional configurations among 17 Gippert, G.P. et al. (1998) Distributed torsion angle grid search in high dimensions:
molecules. J. Chem. Inf. Comput. Sci. 36, 563–571 a systematic approach to NMR structure determination. J. Biomol. NMR 11, 241–263
9 Li, H. et al. (2000) HypoGen: an automated system for generating 3D predictive 18 Hurst, T. (1994) Flexible 3D searching: the directed tweak technique. J. Chem. Inf.
pharmacophore models. In Pharmacophore Perception, Development, and Use in Drug Comput. Sci. 34, 190–196
¨
Design (Guner, O.F., ed.), pp. 171–189, International University Line 19 Jones, G. et al. (1995) A genetic algorithm for flexible molecular overlay and
pharmacophore elucidation. J. Comput. Aid. Mol. Des. 9, 532–549
20 Li, Z. and Scheraga, H.A. (1987) Monte Carlo-minimization approach to the
4 multiple-minima problem in protein folding. Proc. Natl. Acad. Sci. U. S. A. 84,
LUDU is now incorporated into Discovery Studio, available from Accelrys
Inc., San Diego, CA, USA. 6611–6615
www.drugdiscoverytoday.com 449
7. REVIEWS Drug Discovery Today Volume 15, Numbers 11/12 June 2010
21 Sperandio, O. et al. (2009) MED-3DMC: a new tool to generate 3D conformation 40 Ullmann, J.R. (1976) An algorithm for subgraph isomorphism. J. ACM 23, 31–42
ensembles of small molecules with a Monte Carlo sampling of the conformational 41 Barnard, J.M. (1993) Substructure searching methods: old and new. J. Chem. Inf.
space. Eur. J. Med. Chem. 44, 1405–1409 Comput. Sci. 33, 532–538
22 Liu, X. et al. (2009) Cyndi: a multi-objective evolution algorithm based method for 42 Xu, J. (1996) GMA: a generic match algorithm for structural homorphism,
bioactive molecular conformational generation. BMC Bioinformatics 10, 1–14 isomorphism, maximal common substructure match and its applications. J. Chem.
23 Li, J. et al. (2007) CAESAR: a new conformer generation algorithm based on recursive Inf. Comput. Sci. 36, 25–34
buildup and local rotational symmetry consideration. J. Chem. Inf. Model. 47, 1923– 43 Triballeau, N. et al. (2006) Are you sure you have a good model? Pharmacophores:
1932 historical perspective and viewpoint from a medicinal chemist. In Pharmacophores
24 Bandyopadhyay, D. and Agrafiotis, D.K. (2008) A self-organizing algorithm for and Pharmacophore Searches (Langer, T. and Hoffmann, R.D., eds), pp. 325–364,
molecular alignment and pharmacophore development. J. Comput. Chem. 29, 965– Wiley–VCH
982 44 Sun, H. (2008) Pharmacophore-based virtual screening. Curr. Med. Chem. 15, 1018–
Reviews INFORMATICS
25 Totrov, M. (2008) Atomic property fields: generalized 3D pharmacophoric potential 1024
for automated ligand superposition, pharmacophore elucidation and 3D QSAR. 45 Deng, J. et al. (2006) Dynamic pharmacophore model optimization: identification
Chem. Biol. Drug Des. 71, 15–27 of novel HIV-1 integrase inhibitors. J. Med. Chem. 49, 1684–1692
26 Nettles, J.H. et al. (2007) Flexible 3D pharmacophores as descriptors of dynamic ´
46 Gimenez-Oya, V. et al. (2009) Mimicking direct protein–protein and solvent-
biological space. J. Mol. Graph. Model. 26, 622–633 mediated interactions in the CDP-methylerythritol kinase homodimer: a
27 Baroni, M. et al. (2007) A common reference framework for analyzing/comparing pharmacophore-directed virtual screening approach. J. Mol. Model. 15, 997–1007
proteins and ligands. Fingerprints for Ligands and Proteins (FLAP): theory and 47 Talevi, A. et al. (2009) Combined virtual screening strategies. Curr. Comput. Aid.
application. J. Chem. Inf. Model. 47, 279–294 Drug. Des. 5, 23–37
28 Hecker, E.A. et al. (2002) Use of Catalyst pharmacophore models for screening of 48 Kirchmair, J. et al. (2008) Enhancing drug discovery through in silico screening:
large combinatorial libraries. J. Chem. Inf. Comput. Sci. 42, 1204–1211 strategies to increase true positives retrieval rates. Curr. Med. Chem. 15, 2040–2053
29 Toba, S. et al. (2006) Using pharmacophore models to gain insight into structural 49 Muegge, I. (2008) Synergies of virtual screening approaches. Mini Rev. Med. Chem. 8,
binding and virtual screening: an application study with CDK2 and human DHFR. J. 927–933
Chem. Inf. Model. 46, 728–735 50 Deng, X.-Q. et al. (2008) Pharmacophore modeling and docking study for
30 Vadivelan, S. et al. (2007) Virtual screening studies to design potent CDK2-Cyclin A identification of new Aurora-A kinase inhibitors. Chem. Biol. Drug Des. 71, 533–539
inhibitors. J. Chem. Inf. Model. 47, 1526–1535 51 Xie, H.-Z. et al. (2009) Pharmacophore modeling study based on known spleen
31 Wolber, G. et al. (2005) LigandScout: 3-D pharmacophores derived from protein- tyrosine kinase inhibitors together with virtual screening for identifying novel
bound ligands and their use as virtual screening filters. J. Chem. Inf. Model. 45, 160– inhibitors. Bioorg. Med. Chem. Lett. 19, 1944–1949
169 52 Ren, J.-X. et al. (2009) Pharmacophore modeling and virtual screening for the
32 Chen, J. and Lai, L.H. (2006) Pocket v.2: further developments on receptor-based discovery of new transforming growth factor-b type I receptor (ALK5) inhibitors.
pharmacophore modeling. J. Chem. Inf. Model. 46, 2684–2691 Eur. J. Med. Chem. 11, 4259–4265
33 Ortuso, F. et al. (2006) GBPM: GRID based pharmacophore model. Concept and 53 Tschinke, V. and Cohen, N. (1993) The NEWLEAD program: a new method for the
application studies to protein–protein recognition. Bioinformatics 22, 1449–1455 design of candidate structures from pharmacophoric hypotheses. J. Med. Chem. 36,
¨
34 Bohm, H.J. (1992) The computer program LUDI: a new method for the de novo 3863–3870
design of enzyme inhibitors. J. Comput. Aid. Mol. Des. 6, 61–78 54 Roe, D.C. and Kuntz, I. (1995) BUILDER v.2: improving the chemistry of a de novo
35 Barillari, C. et al. (2008) Hot-spots-guided receptor-based pharmacophores (HS- design strategy. J. Comput. Aid. Mol. Des. 9, 269–282
Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein 55 Huang, Q. et al. (2010) PhDD: a new pharmacophore-based de novo design method
cavities and prioritize structure-based pharmacophores. J. Chem. Inf. Model. 48, of drug-like molecules combined with assessment of synthetic accessibility. J. Mol.
1396–1410 Graph. Model. 10.1016/j.jmgm.2010.02.002
36 Tintori, C. (2008) Targets looking for drugs: a multistep computational protocol for 56 Brenk, R. and Klebe, G. (2006) ‘Hot spot’ analysis of protein-binding sites as a
the development of structure-based pharmacophores and their applications for hit prerequisite for structure-based virtual screening and lead optimization. In
discovery. J. Chem. Inf. Model. 48, 2166–2179 Pharmacophores and Pharmacophore Searches (Langer, T. and Hoffmann, R.D., eds), pp.
37 Goodford, P.J. (1985) A computational procedure for determining energetically 171–192, Wiley–VCH
favorable binding sites on biologically important macromolecules. J. Med. Chem. 28, 57 Wei, D. et al. (2008) Discovery of multi-target inhibitors by combining molecular
849–857 docking with common pharmacophore matching. J. Med. Chem. 51, 7882–7888
38 Zou, J. et al. (2008) Towards more accurate pharmacophore modeling: 58 Steindl, T.M. et al. (2006) Parallel screening: a novel concept in pharmacophore
multicomplex-based comprehensive pharmacophore map and most-frequent- modeling and virtual screening. J. Chem. Inf. Model. 46, 2146–2157
feature pharmacophore model of CDK2. J. Mol. Graph. Model. 27, 430–438 59 Rollinger, J.M. et al. (2004) Acetylcholinesterase inhibitory activity of scopolin and
39 Zhao, H. (2007) Scaffold selection and scaffold hopping in lead generation: a scopoletin discovered by virtual screening of natural products. J. Med. Chem. 47,
medicinal chemistry perspective. Drug Discov. Today 12, 149–155 6248–6254
450 www.drugdiscoverytoday.com