Allostery Illuminated: Harnessing AI and Machine Learning for Drug Discovery.

Area of Science:

• Pharmacology
• Computational Chemistry
• Drug Discovery

Background:

• Artificial intelligence (AI) and machine learning (ML) are increasingly adopted in pharmaceutical research.
• Allosteric modulators offer therapeutic potential but present discovery challenges.

Purpose of the Study:

• To review recent AI/ML applications in allosteric modulator discovery.
• To highlight advancements in AlphaFold, structure-based drug discovery (SBDD), and medicinal chemistry.
• To discuss current challenges in identifying allosteric sites and ligands.

Main Methods:

• Review of recent literature on AI/ML in drug discovery.
• Focus on AlphaFold for protein structure prediction.
• Analysis of SBDD and medicinal chemistry applications.

Main Results:

• AI/ML tools, including AlphaFold, are significantly impacting allosteric modulator discovery.
• Structure-based approaches combined with AI/ML show promise.
• Progress has been made in integrating computational methods into drug design.

Conclusions:

• AI and ML are powerful tools revolutionizing allosteric modulator drug discovery.
• Further advancements are needed to overcome challenges in allosteric site and ligand identification.
• The integration of AI/ML promises more efficient and effective drug development pipelines.