AI-Driven Drug Discovery: A Comprehensive Review.

Area of Science:

• Pharmaceutical Sciences
• Computational Biology
• Artificial Intelligence

Background:

• Traditional drug discovery faces significant hurdles including high costs, long timelines, and low success rates.
• Artificial intelligence (AI) and machine learning (ML) present promising solutions to overcome these challenges.
• Recent advancements (2019-2024) show AI/ML's potential across the drug discovery pipeline.

Purpose of the Study:

• To critically analyze recent AI/ML methodologies in drug discovery (2019-2024).
• To examine AI applications in key stages: target identification, lead discovery, optimization, and safety assessment.
• To identify challenges and propose future directions for AI integration in pharmaceutical R&D.

Main Methods:

• Comprehensive literature review of AI/ML advancements in drug discovery.
• Analysis of diverse AI techniques: deep learning, graph neural networks, transformers.
• Comparative assessment of AI approaches, focusing on data quality, validation, and ethics.

Main Results:

• AI/ML techniques show significant promise in accelerating various stages of drug discovery.
• Key applications identified in target identification, lead generation, and preclinical safety.
• Limitations include data accessibility, model interpretability, and clinical translation challenges.

Conclusions:

• AI/ML integration offers transformative potential for developing safer, more effective medicines.
• Addressing challenges in data, interpretability, and validation is crucial for successful implementation.
• Future efforts should focus on transparent methodologies and ethical frameworks for responsible AI adoption.