Reinforcement learning for systems pharmacology-oriented and personalized drug design

Abstract

Introduction: 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 designing personalized therapies for untreatable complex diseases.

Areas covered: 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.

Expert opinion: 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.

Keywords: Drug discovery; deep learning; machine learning; precision medicine; systems pharmacology.

Figure 1.

A taxonomy of RL algorithms

Figure 2.

RL-based methods for drug design

Figure 3.

Illustration of a RL framework for systems pharmacology-oriented lead optimization.