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

• URL: http://arxiv.org/abs/2201.08894v1
• Date: Fri, 21 Jan 2022 21:29:46 GMT
• Title: Reinforcement Learning for Personalized Drug Discovery and Design for Complex Diseases: A Systems Pharmacology Perspective
• Authors: Ryan K. Tan, Yang Liu, Lei Xie
• Abstract summary: We review the potential of reinforcement learning in systems pharmacology-oriented drug discovery and design. New reinforcement learning techniques are needed to boost generalizability and transferability of reinforcement learning.
• Score: 13.103056213998213
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Many multi-genic systematic diseases such as Alzheimer's disease and majority of cancers do not have effective treatments yet. Systems pharmacology is a potentially effective approach to designing personalized therapies for untreatable complexed diseases. In this article, we review the potential of reinforcement learning in systems pharmacology-oriented drug discovery and design. In spite of successful application of advanced reinforcement learning techniques to target-based drug discovery, new reinforcement learning techniques are needed to boost generalizability and transferability of reinforcement learning in partially observed and changing environments, optimize multi-objective reward functions for system-level molecular phenotype readouts and generalize predictive models for out-of-distribution data. A synergistic integration of reinforcement learning with other machine learning techniques and related fields such as biophysics and quantum computing is needed to achieve the ultimate goal of systems pharmacology-oriented de novo drug design for personalized medicine.

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