Lecture 3 rational drug design

The document provides an overview of the modern drug discovery process, which involves 5 main steps: 1) Target identification and validation to find the molecular structures involved in the disease. 2) Hit identification and validation to find small molecule leads that have the desired effect on the targets. 3) Moving from a hit to a lead by refining hits into more selective compounds. 4) Lead optimization to improve properties and address any deficiencies while maintaining desired effects. 5) Late lead optimization to further assess safety before clinical trials. Modern drug discovery is an expensive process that can cost over $1 billion on average due to large investments required. Bioinformatics and genomic/proteomic technologies help accelerate the process and reduce

This document discusses drug discovery and the process of identifying potential new drug targets. It outlines the need for drug discovery to develop treatments for diseases without existing therapies. The key steps in drug discovery include target identification using genomics and proteomics to study the genome and map protein-protein interactions, as well as target validation using techniques like RNA interference and transgenic animal models. Bioinformatics plays an important role in analyzing large datasets to aid in drug target discovery and validation.

Drug discovery and development is a long, expensive, and complex process averaging about 12 years and $500 million to bring a new prescription medication to market. Only 1 in 10,000 compounds eventually becomes an approved drug. The process involves discovery, preclinical research, clinical trials, and regulatory approval. Discovery aims to identify candidate drug molecules, while preclinical research studies their safety and efficacy in animal models before human testing. Clinical trials then evaluate new drugs with patients for safety and effectiveness over several phases before regulatory approval and marketing.

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 basic aspects of drug discovery starts from target discovery and validation further going to lead identification and optimization. In this particular slide discussion is regarding the target discovery and the tools that have been utilized in this process.

The document provides an overview of the modern drug discovery process, focusing on lead identification and lead optimization. It discusses how lead compounds are initially identified through screening compound libraries or structure-based drug design. These leads are then optimized through chemical modifications to improve properties like efficacy, potency, pharmacokinetics and toxicity profile. The goal is to develop compounds suitable for preclinical and clinical testing towards becoming an approved drug. Methods for lead optimization include modifying functional groups, exploring structure-activity relationships, and altering aspects like stereochemistry.

This document discusses the key steps in the drug discovery process, including target identification and validation, lead identification, and lead optimization. It describes how identifying the biological target of a disease is the first step, followed by validating that target. Leads are then identified, which are compounds that show desired biological activity against the validated target. The leads undergo optimization to improve properties like potency. Methods for target identification, lead identification, and lead optimization are also outlined.

The document discusses the economics of drug discovery. It notes that drug discovery takes 3-20 years and costs several billion to tens of billions of dollars. The process involves determining the causes of diseases and finding compounds for treatment. Drugs then undergo pre-clinical and clinical trials, with the three phases of clinical trials costing upwards of $100 million alone. A new 2020 study estimated the median cost of getting a new drug to market is $985 million, with the average being $1.3 billion. This is lower than previous estimates of $2.8 billion. The document also outlines the present costs involved in various stages of drug discovery and development.

Target identification, target validation, lead identification and lead Optimization. • Economics of drug discovery. • Target Discovery and validation-Role of Genomics, Proteomics and Bioinformatics. • Role of Nucleic acid microarrays, Protein microarrays, Antisense technologies, siRNAs, antisense oligonucleotides, Zinc finger proteins. • Role of transgenic animals in target validation.

Combinatorial chemistry is a collection of techniques which allow for the synthesis of multiple compounds at the same time. Combinatorial chemistry is one of the important new methodologies developed by researchers in the pharmaceutical industry to reduce the time and costs associated with producing effective and competitive new drugs, By accelerating the process of chemical synthesis, this method is having a profound effect on all branches of chemistry, but especially on drug discovery.

1. Structure-based drug design relies on knowledge of the three-dimensional structure of the biological target obtained through methods such as x-ray crystallography. Candidate drugs that are predicted to bind with high affinity and selectivity to the target can be designed. 2. Structure-based drug design approaches include receptor-based drug design, which involves "building" ligands within the constraints of the binding pocket, and ligand-based drug design. 3. De novo drug design is a receptor-based approach that uses the target's 3D structure to design new molecules without existing leads. It involves building ligands that complement the active site properties through manual or automated methods.

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.