Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 Aug;17(8):849-863.
doi: 10.1080/17460441.2022.2072288. Epub 2022 Aug 5.

Reinforcement learning for systems pharmacology-oriented and personalized drug design

Ryan K Tan  1 , Yang Liu  1 , Lei Xie  1   2   3
Affiliations
Review

Reinforcement learning for systems pharmacology-oriented and personalized drug design

Ryan K Tan et al. Expert Opin Drug Discov. 2022 Aug.

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.

PubMed Disclaimer

Conflict of interest statement

Declaration of Interest:

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Figures

Figure 1.
Figure 1.
A taxonomy of RL algorithms
Figure 2.
Figure 2.
RL-based methods for drug design
Figure 3.
Figure 3.
Illustration of a RL framework for systems pharmacology-oriented lead optimization.
See this image and copyright information in PMC

References

    1. Polishchuk PG, Madzhidov TI, Varnek A. Estimation of the Size of Drug-like Chemical Space Based on GDB-17 Data. J Comput Aided Mol Des. 2013;27(8):675–679. - PubMed
    1. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative Adversarial Networks. Commun ACM. 2014;63(11):139–144.
    1. Kingma DP, Welling M. Auto-Encoding Variational Bayes. 2nd Int Conf Learn Represent ICLR 2014 - Conf Track Proc. 2013.
    1. Rezende DJ, Mohamed S. Variational Inference with Normalizing Flows. 32nd Int Conf Mach Learn ICML 2015. 2015;2:1530–1538.
    1. Madhawa K, Ishiguro K, Nakago K, Abe M. GraphNVP: An Invertible Flow Model for Generating Molecular Graphs. 2019.

Publication types

LinkOut - more resources