Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Blaschke, Thomas, Engkvist, Ola, Bajorath, Jürgen, Chen, Hongming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
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
_version_ 1783607995400192000
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
work_keys_str_mv AT blaschkethomas memoryassistedreinforcementlearningfordiversemoleculardenovodesign
AT engkvistola memoryassistedreinforcementlearningfordiversemoleculardenovodesign
AT bajorathjurgen memoryassistedreinforcementlearningfordiversemoleculardenovodesign
AT chenhongming memoryassistedreinforcementlearningfordiversemoleculardenovodesign