Optimization of binding affinities in chemical space with transformer and deep reinforcement learning

Name: Optimization of binding affinities in chemical space with transformer and deep reinforcement learning
Keywords: Hit discovery, Transformers, Reinforcement learning, Molecular docking, Drug design

Xu, Xiaopeng; Zhou, Juexiao; Zhu, Chen; Zhan, Qing; Li, Zhongxiao; Zhang, Ruochi; Wang, Yu; Liao, Xingyu

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Software . 2023

License: https://opensource.org/licenses/MIT

Data sources: Datacite

Optimization of binding affinities in chemical space with transformer and deep reinforcement learning

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 01 Feb 2023Publisher:Zenodo

Authors: Xu, Xiaopeng; Zhou, Juexiao; Zhu, Chen; Zhan, Qing; Li, Zhongxiao; Zhang, Ruochi; Wang, Yu; +2 Authors

doi: 10.5281/zenodo.7595997

Optimization of binding affinities in chemical space with transformer and deep reinforcement learning

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Abstract

This repository contains the code and data for SGPT-RL, a tool for chemical design using transformer and deep reinforcement learning. Through employing GPT model as the policy network, SGPT-RL can learn scaffold patterns in exploring the chemical space. Source code: https://github.com/charlesxu90/sgpt

Keywords

Hit discovery, Transformers, Reinforcement learning, Molecular docking, Drug design

1 Research products, page 1 of 1

Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning -- source data
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Optimization of binding affinities in chemical space with transformer and deep reinforcement learning

Optimization of binding affinities in chemical space with transformer and deep reinforcement learning

1 Research products, page 1 of 1

Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning -- source data