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Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...

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Related Experiment Video

Updated: Dec 29, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

A Comprehensive Computational Platform to Guide Drug Development Using Graph-Based Signature Methods.

Douglas E V Pires^1,2,3, Stephanie Portelli^1,2, Pâmela M Rezende^3,4

¹Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.

Methods in Molecular Biology (Clifton, N.J.)

|February 2, 2020

Summary

This summary is machine-generated.

Computational tools accelerate drug discovery by optimizing molecular interactions and predicting compound properties. This enhances efficiency and reduces costs in developing new medicines.

Keywords:

Docking Drug development Graph-based signatures Interatomic interactions Mutation Protein-ligand mCSM

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Area of Science:

Computational chemistry and cheminformatics
Drug discovery and development
Pharmacology and toxicology

Background:

High-throughput computational techniques are essential for improving drug development efficiency and reducing costs.
Designing ligands for specific protein targets enables understanding and optimization of molecular interactions before synthesis.
Predicting binding affinity, pharmacokinetics, and toxicity aids in early lead compound filtering.

Purpose of the Study:

To present a suite of computational tools for various stages of drug discovery.
To demonstrate how these tools can guide chemical and biological experiments.
To facilitate the selection of chemically and biologically feasible drug candidates.

Main Methods:

Utilizing EasyVS for hit identification.
Employing CSM-lig, mCSM-lig, Arpeggio, and pkCSM for lead optimization and candidate selection.
Integrating computational predictions into the drug development pipeline.

Main Results:

Computational tools provide insights into molecular interactions and binding affinities.
Early prediction of pharmacokinetics and toxicity helps in filtering suboptimal leads.
The described tools support decision-making throughout the drug discovery process.

Conclusions:

Incorporating these computational tools enhances the success rate of drug development.
These tools ensure that candidate leads are both chemically and biologically viable.
Optimized resource allocation leads to more tractable and successful drug candidates.