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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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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.

A Acharya¹, R Agarwal^2,3, M B Baker⁴

¹School of Physics, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

Journal of Chemical Information and Modeling

|December 16, 2020

Summary

This study introduces a supercomputer pipeline for drug discovery, combining molecular dynamics (MD) and ensemble docking. This approach accurately predicts drug candidates by considering protein dynamics, showing high hit rates for SARS-CoV-2 targets.

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Area of Science:

Computational chemistry
Drug discovery
Molecular dynamics simulations

Background:

Drug discovery requires efficient methods to identify potential therapeutic compounds.
Understanding protein dynamics is crucial for accurate drug-target interactions.
SARS-CoV-2 proteome presents multiple targets for antiviral drug development.

Purpose of the Study:

To develop and validate a supercomputer-driven pipeline for in silico drug discovery.
To integrate enhanced sampling molecular dynamics (MD) with ensemble docking for improved accuracy.
To assess the pipeline's efficacy against SARS-CoV-2 targets.

Main Methods:

Utilized temperature replica exchange enhanced sampling molecular dynamics (MD) on the Summit supercomputer.
Employed ensemble docking, using MD-derived protein conformations for compound screening.
Performed large-scale virtual screening of repurposing databases against 24 SARS-CoV-2 systems.

Main Results:

Achieved over 1 ms of enhanced sampling MD per day.
Demonstrated high hit rates for predicted compounds when compared to experimental data.
Successfully docked one billion compounds in under 24 hours using Autodock-GPU.

Conclusions:

The presented pipeline effectively leverages supercomputing for rapid and accurate in silico drug discovery.
Ensemble docking, informed by MD, significantly enhances the identification of potential drug candidates.
Future improvements include integrating quantum mechanics, machine learning, and AI for further pipeline optimization.