Industrializing AI/ML during the end-to-end drug discovery process

Area of Science:

• Computational chemistry and pharmacology
• Biotechnology and pharmaceutical sciences
• Artificial intelligence and machine learning applications

Background:

• Drug discovery is a complex process aiming to identify targets and drug candidates for unmet clinical needs.
• Traditional drug discovery pipelines face challenges in efficiency and target identification.
• The integration of artificial intelligence and machine learning (AI/ML) is transforming the pharmaceutical landscape.

Purpose of the Study:

• To review recent advancements in AI/ML methods applied across the entire drug discovery process.
• To highlight the role of AI/ML in target identification and molecule optimization.
• To provide an overview of current industrial trends in end-to-end AI/ML drug discovery platforms.

Main Methods:

• Review of current literature on AI/ML applications in drug discovery.
• Analysis of AI/ML methodologies from target identification through candidate selection.
• Examination of data-driven platforms and their impact on discovery pipelines.

Main Results:

• AI/ML significantly enhances the identification of novel drug targets, diversifying discovery pipelines.
• Machine learning technologies accelerate the optimization of drug candidates, complementing traditional methods.
• The development of end-to-end AI/ML platforms is a major industrial trend.

Conclusions:

• AI/ML is revolutionizing drug discovery by improving efficiency and success rates.
• End-to-end AI/ML platforms offer a comprehensive approach to addressing unmet clinical needs.
• Continued innovation in AI/ML is crucial for the future of pharmaceutical research and development.