Reinforcement Learning for Personalized Drug Discovery and Design for Complex Diseases: A Systems Pharmacology Perspective

TL;DR

This paper reviews how reinforcement learning can be applied to systems pharmacology for designing personalized therapies for complex, multi-genic diseases, highlighting current methods, challenges, and future directions.

Contribution

It provides a comprehensive survey of reinforcement learning applications in drug discovery for complex diseases and discusses challenges and potential solutions in personalized medicine.

Findings

Reinforcement learning has been successfully applied to target-based drug discovery.

Challenges remain in applying reinforcement learning to systems pharmacology.

New strategies are needed for personalized de novo drug design.

Abstract

Many multi-genic systemic diseases such as neurological disorders, inflammatory diseases, and the majority of cancers do not have effective treatments yet. Reinforcement learning powered systems pharmacology is a potentially effective approach to design personalized therapies for untreatable complex diseases. In this survey, state-of-the-art reinforcement learning methods and their latest applications to drug design are reviewed. The challenges on harnessing reinforcement learning for systems pharmacology and personalized medicine are discussed. Potential solutions to overcome the challenges are proposed. In spite of successful application of advanced reinforcement learning techniques to target-based drug discovery, new reinforcement learning strategies are needed to address systems pharmacology-oriented personalized de novo drug design.