research-article

Deep Reinforcement Learning and Docking Simulations for autonomous molecule generation in de novo Drug Design

Authors:

Xiaotong HuAuthors Info & Claims

MMAsia '21: Proceedings of the 3rd ACM International Conference on Multimedia in Asia

Article No.: 79, Pages 1 - 6

https://doi.org/10.1145/3469877.3497694

Published: 10 January 2022 Publication History

Abstract

In medicinal chemistry programs, it is key to design and make compounds that are efficacious and safe. In this study, we developed a new deep Reinforcement learning-based compounds molecular generation method. Because chemical space is impractically large, and many existing generation models generate molecules that lack effectiveness, novelty and unsatisfactory molecular properties. Our proposed method-DeepRLDS, which integrates transformer network, balanced binary tree search and docking simulation based on super large-scale supercomputing, can solve these problems well. Experiments show that more than 96 of the generated molecules are chemically valid, 99 of the generated molecules are chemically novelty, the generated molecules have satisfactory molecular properties and possess a broader chemical space distribution.

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Cited By

Wang QWei ZHu XWang ZLu HLiu H(2024)Target-aware Guided equivariant Diffusion model for 3D molecule Generation2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10822749(4497-4504)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10822749

Index Terms

Deep Reinforcement Learning and Docking Simulations for autonomous molecule generation in de novo Drug Design
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
    2. Computational biology
      1. Molecular structural biology
2. Computing methodologies
  1. Machine learning

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

MMAsia '21: Proceedings of the 3rd ACM International Conference on Multimedia in Asia

December 2021

508 pages

ISBN:9781450386074

DOI:10.1145/3469877

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 10 January 2022

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Research-article
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Refereed limited

Funding Sources

Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao)
National Key Research and Development Program of China
Qingdao Independent Innovation Major Project

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MMAsia '21

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SIGMM

MMAsia '21: ACM Multimedia Asia

December 1 - 3, 2021

Gold Coast, Australia

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Overall Acceptance Rate 59 of 204 submissions, 29%

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Cited By

Wang QWei ZHu XWang ZLu HLiu H(2024)Target-aware Guided equivariant Diffusion model for 3D molecule Generation2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10822749(4497-4504)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10822749

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