Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.

Area of Science:

• Drug discovery and development
• Computational chemistry and structural biology
• Genomics and bioinformatics

Background:

• Drug discovery is a lengthy, costly, and high-risk process, often taking over a decade and significant investment.
• Genomics, structural biology, and computational chemistry support early-stage research but face translation challenges.
• Machine learning (ML) offers a powerful approach to enhance efficiency across the drug discovery pipeline.

Purpose of the Study:

• To review the applications of ML in drug discovery, focusing on translational and regulatory aspects.
• To discuss emerging ML directions and their potential impact on pharmaceutical R&D.
• To highlight the challenges and requirements for effective ML integration in drug discovery.

Main Methods:

• Review of current literature on ML applications in drug discovery.
• Analysis of ML's role in target identification, lead discovery, optimization, and outcome prediction.
• Examination of translational, regulatory, and ethical considerations for ML adoption.

Main Results:

• ML aids in target identification, lead discovery, optimization, and predicting preclinical/clinical outcomes.
• Recent advances include ML for protein structure prediction, antifibrotic compound discovery, and antibiotic identification.
• Significant challenges persist, including data scarcity, model generalizability, translation bottlenecks, and regulatory hurdles.

Conclusions:

• Effective ML integration requires rigorous validation, interdisciplinary collaboration, and responsible data governance.
• Addressing data biases, model interpretability, and ethical concerns is crucial for regulatory acceptance and adoption.
• Emerging ML approaches like hybrid physics-AI and explainable AI show promise for future drug discovery.