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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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Updated: May 14, 2026

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

Using generative AI to transform peptide hits into small molecule leads.

Joshua Mills^1,2, Yu Heng Lau^1,2

¹School of Chemistry, The University of Sydney, Camperdown NSW 2006, Australia.

Beilstein Journal of Organic Chemistry

|May 13, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) can accelerate drug discovery by transforming peptide binders into small molecule leads. AI tools aid in structure prediction, de novo design, scaffold generation, and affinity prediction for efficient candidate screening.

Keywords:

diffusion models drug discovery generative AI peptides small molecules

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Last Updated: May 14, 2026

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Protein Target Prediction and Validation of Small Molecule Compound

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

Area of Science:

Computational chemistry
Drug discovery
Artificial intelligence in medicine

Background:

Abundant structural data exists for peptide-protein interactions, offering a foundation for small molecule inhibitor design.
Improving pharmacokinetic properties of peptide-based therapeutics is a key challenge in drug development.

Purpose of the Study:

To highlight the potential of AI-enabled tools in transforming peptide hits into small molecule leads.
To outline an AI-driven workflow for structure-based drug design, from peptide to small molecule.

Main Methods:

Utilizing AI for target protein structure prediction.
Employing generative models for de novo peptide binder design.
Leveraging diffusion models for novel small molecule scaffold generation.
Applying deep learning for binding affinity prediction and candidate triage.

Main Results:

AI tools can potentially automate and expedite multiple stages of the drug design pipeline.
Integration of AI can enhance the efficiency of converting peptide binders into viable small molecule drug candidates.
AI facilitates rapid screening and selection of promising small molecules based on predicted binding affinity.

Conclusions:

AI presents a transformative approach to structure-based drug design, significantly accelerating the journey from peptide binders to small molecule leads.
The described AI-enabled workflow offers a comprehensive strategy for optimizing drug discovery processes.
Future applications of AI in medicinal chemistry hold promise for developing improved therapeutics with enhanced pharmacokinetic profiles.