mlsanityData to 7 Oct 2026

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

arXiv 2201.08894 ↗First posted 21 Jan 2022q-bio.BM cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 52 of its 117 references that are in the map. First posted on arXiv 21 Jan 2022.

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52 of 117 references.

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  1. 2023Revolutionizing Genomics with Reinforcement Learning Techniquescited by 0