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Memory-assisted reinforcement learning for diverse molecular de novo design
In de novo molecular design, recurrent neural networks (RNN) have been shown to be effective methods for sampling and generating novel chemical structures. Using a technique called reinforcement learning (RL), an RNN can be tuned to target a particular section of chemical space with optimized desira...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654024/ https://www.ncbi.nlm.nih.gov/pubmed/33292554 http://dx.doi.org/10.1186/s13321-020-00473-0 |
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author | Blaschke, Thomas Engkvist, Ola Bajorath, Jürgen Chen, Hongming |
author_facet | Blaschke, Thomas Engkvist, Ola Bajorath, Jürgen Chen, Hongming |
author_sort | Blaschke, Thomas |
collection | PubMed |
description | In de novo molecular design, recurrent neural networks (RNN) have been shown to be effective methods for sampling and generating novel chemical structures. Using a technique called reinforcement learning (RL), an RNN can be tuned to target a particular section of chemical space with optimized desirable properties using a scoring function. However, ligands generated by current RL methods so far tend to have relatively low diversity, and sometimes even result in duplicate structures when optimizing towards desired properties. Here, we propose a new method to address the low diversity issue in RL for molecular design. Memory-assisted RL is an extension of the known RL, with the introduction of a so-called memory unit. As proof of concept, we applied our method to generate structures with a desired AlogP value. In a second case study, we applied our method to design ligands for the dopamine type 2 receptor and the 5-hydroxytryptamine type 1A receptor. For both receptors, a machine learning model was developed to predict whether generated molecules were active or not for the receptor. In both case studies, it was found that memory-assisted RL led to the generation of more compounds predicted to be active having higher chemical diversity, thus achieving better coverage of chemical space of known ligands compared to established RL methods. |
format | Online Article Text |
id | pubmed-7654024 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-76540242020-11-10 Memory-assisted reinforcement learning for diverse molecular de novo design Blaschke, Thomas Engkvist, Ola Bajorath, Jürgen Chen, Hongming J Cheminform Research Article In de novo molecular design, recurrent neural networks (RNN) have been shown to be effective methods for sampling and generating novel chemical structures. Using a technique called reinforcement learning (RL), an RNN can be tuned to target a particular section of chemical space with optimized desirable properties using a scoring function. However, ligands generated by current RL methods so far tend to have relatively low diversity, and sometimes even result in duplicate structures when optimizing towards desired properties. Here, we propose a new method to address the low diversity issue in RL for molecular design. Memory-assisted RL is an extension of the known RL, with the introduction of a so-called memory unit. As proof of concept, we applied our method to generate structures with a desired AlogP value. In a second case study, we applied our method to design ligands for the dopamine type 2 receptor and the 5-hydroxytryptamine type 1A receptor. For both receptors, a machine learning model was developed to predict whether generated molecules were active or not for the receptor. In both case studies, it was found that memory-assisted RL led to the generation of more compounds predicted to be active having higher chemical diversity, thus achieving better coverage of chemical space of known ligands compared to established RL methods. Springer International Publishing 2020-11-10 /pmc/articles/PMC7654024/ /pubmed/33292554 http://dx.doi.org/10.1186/s13321-020-00473-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Blaschke, Thomas Engkvist, Ola Bajorath, Jürgen Chen, Hongming Memory-assisted reinforcement learning for diverse molecular de novo design |
title | Memory-assisted reinforcement learning for diverse molecular de novo design |
title_full | Memory-assisted reinforcement learning for diverse molecular de novo design |
title_fullStr | Memory-assisted reinforcement learning for diverse molecular de novo design |
title_full_unstemmed | Memory-assisted reinforcement learning for diverse molecular de novo design |
title_short | Memory-assisted reinforcement learning for diverse molecular de novo design |
title_sort | memory-assisted reinforcement learning for diverse molecular de novo design |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654024/ https://www.ncbi.nlm.nih.gov/pubmed/33292554 http://dx.doi.org/10.1186/s13321-020-00473-0 |
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