Analysing the drug targets in the human genome

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

This document discusses pharmacophore modeling and computer-aided drug design. It begins with objectives of studying pharmacophore modeling methods and techniques for lead prioritization and optimization. An introduction defines pharmacophore as the schematic representation of bioactive functional groups and their distances. It then reviews surveys on G-protein coupled receptors in drug development and advances in pharmacophore modeling applications. The document outlines the process of pharmacophore-based drug design and provides an example using morphine. It also shows 3D pharmacophores and how computer-aided design is used. It concludes that pharmacophore approaches have been major tools in drug discovery and various methods have been applied successfully in virtual screening and lead optimization.

Pharmacophore modeling is an important technique in drug discovery. A pharmacophore model explains how structurally diverse ligands can bind to a common receptor site by identifying the essential chemical features necessary for biological activity. Pharmacophore models can be used in virtual screening to identify potential new drug leads from chemical databases that share the same features and binding orientation. Key steps in developing a pharmacophore model include selecting a training set of ligands, conformational analysis, molecular superimposition, and validation against biological activity data.

this power point is all about rational drug design and pharmacophore based drug design approaches...

This document provides an overview of pharmacophore mapping and pharmacophore-based screening. It defines a pharmacophore as the pattern of molecular features responsible for a drug's biological activity. The key steps in pharmacophore modeling are identifying common binding elements in active compounds, generating potential ligand conformations, and determining the 3D relationships between pharmacophore elements. Pharmacophore models can be generated manually based on known active ligands or automatically using software. Receptor-based pharmacophore generation uses the 3D structure of the target protein to identify favorable binding sites. Overall, pharmacophore mapping is used in computer-aided drug design to identify novel ligands that interact with the same biological target.

Comparative modeling predicts the 3D structure of a target protein sequence based on its alignment to template protein structures of known structure. It consists of four main steps: fold assignment, alignment of the target and template sequences, building a model based on the alignment, and predicting errors in the model. Comparative modeling is often used to facilitate functional characterization of a protein when its experimental structure is unknown, as it can provide a useful 3D structural model for proteins related to templates.

This document discusses rational drug design using computational methods. It begins by explaining how drugs work by binding to biological targets like proteins. It then discusses the need for new drugs to treat new diseases or improve current treatments. The document outlines several methods for screening and designing new drugs, including studying natural products, making modifications, and rational drug design based on understanding the molecular disease process. It describes using the 3D structure of protein targets and molecular docking to design ligands that selectively bind targets. The goals of drug design are to find molecules that effectively bind targets while also having suitable absorption, distribution, metabolism, excretion and toxicity properties. Computational methods can help streamline the drug discovery process.

Computer Aided Drug Design uses computational methods to help streamline the drug discovery process. Key steps include identifying drug targets, generating molecular structures, evaluating interactions between potential drug compounds and targets through docking simulations, and developing quantitative structure-activity relationship (QSAR) models to predict compound activity and guide synthetic efforts. The overall goal is to reduce the cost and time needed to develop new pharmaceutical agents in a rational, mechanism-based manner.

This document discusses structure-based drug design. It begins by explaining that structure-based drug design relies on knowledge of the three-dimensional structure of biological targets, usually determined through methods like X-ray crystallography. The structure of the target is then used to design ligands that will bind to the target. The process involves identifying drug targets, determining the target's structure, performing computer-aided drug design to identify potential binding ligands, and building or modifying ligands to optimize binding to the target.