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Transformer-based molecular optimization beyond matched molecular pairs
Molecular optimization aims to improve the drug profile of a starting molecule. It is a fundamental problem in drug discovery but challenging due to (i) the requirement of simultaneous optimization of multiple properties and (ii) the large chemical space to explore. Recently, deep learning methods h...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8962145/ https://www.ncbi.nlm.nih.gov/pubmed/35346368 http://dx.doi.org/10.1186/s13321-022-00599-3 |
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author | He, Jiazhen Nittinger, Eva Tyrchan, Christian Czechtizky, Werngard Patronov, Atanas Bjerrum, Esben Jannik Engkvist, Ola |
author_facet | He, Jiazhen Nittinger, Eva Tyrchan, Christian Czechtizky, Werngard Patronov, Atanas Bjerrum, Esben Jannik Engkvist, Ola |
author_sort | He, Jiazhen |
collection | PubMed |
description | Molecular optimization aims to improve the drug profile of a starting molecule. It is a fundamental problem in drug discovery but challenging due to (i) the requirement of simultaneous optimization of multiple properties and (ii) the large chemical space to explore. Recently, deep learning methods have been proposed to solve this task by mimicking the chemist’s intuition in terms of matched molecular pairs (MMPs). Although MMPs is a widely used strategy by medicinal chemists, it offers limited capability in terms of exploring the space of structural modifications, therefore does not cover the complete space of solutions. Often more general transformations beyond the nature of MMPs are feasible and/or necessary, e.g. simultaneous modifications of the starting molecule at different places including the core scaffold. This study aims to provide a general methodology that offers more general structural modifications beyond MMPs. In particular, the same Transformer architecture is trained on different datasets. These datasets consist of a set of molecular pairs which reflect different types of transformations. Beyond MMP transformation, datasets reflecting general structural changes are constructed from ChEMBL based on two approaches: Tanimoto similarity (allows for multiple modifications) and scaffold matching (allows for multiple modifications but keep the scaffold constant) respectively. We investigate how the model behavior can be altered by tailoring the dataset while using the same model architecture. Our results show that the models trained on differently prepared datasets transform a given starting molecule in a way that it reflects the nature of the dataset used for training the model. These models could complement each other and unlock the capability for the chemists to pursue different options for improving a starting molecule. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00599-3. |
format | Online Article Text |
id | pubmed-8962145 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-89621452022-03-30 Transformer-based molecular optimization beyond matched molecular pairs He, Jiazhen Nittinger, Eva Tyrchan, Christian Czechtizky, Werngard Patronov, Atanas Bjerrum, Esben Jannik Engkvist, Ola J Cheminform Research Article Molecular optimization aims to improve the drug profile of a starting molecule. It is a fundamental problem in drug discovery but challenging due to (i) the requirement of simultaneous optimization of multiple properties and (ii) the large chemical space to explore. Recently, deep learning methods have been proposed to solve this task by mimicking the chemist’s intuition in terms of matched molecular pairs (MMPs). Although MMPs is a widely used strategy by medicinal chemists, it offers limited capability in terms of exploring the space of structural modifications, therefore does not cover the complete space of solutions. Often more general transformations beyond the nature of MMPs are feasible and/or necessary, e.g. simultaneous modifications of the starting molecule at different places including the core scaffold. This study aims to provide a general methodology that offers more general structural modifications beyond MMPs. In particular, the same Transformer architecture is trained on different datasets. These datasets consist of a set of molecular pairs which reflect different types of transformations. Beyond MMP transformation, datasets reflecting general structural changes are constructed from ChEMBL based on two approaches: Tanimoto similarity (allows for multiple modifications) and scaffold matching (allows for multiple modifications but keep the scaffold constant) respectively. We investigate how the model behavior can be altered by tailoring the dataset while using the same model architecture. Our results show that the models trained on differently prepared datasets transform a given starting molecule in a way that it reflects the nature of the dataset used for training the model. These models could complement each other and unlock the capability for the chemists to pursue different options for improving a starting molecule. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00599-3. Springer International Publishing 2022-03-28 /pmc/articles/PMC8962145/ /pubmed/35346368 http://dx.doi.org/10.1186/s13321-022-00599-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 He, Jiazhen Nittinger, Eva Tyrchan, Christian Czechtizky, Werngard Patronov, Atanas Bjerrum, Esben Jannik Engkvist, Ola Transformer-based molecular optimization beyond matched molecular pairs |
title | Transformer-based molecular optimization beyond matched molecular pairs |
title_full | Transformer-based molecular optimization beyond matched molecular pairs |
title_fullStr | Transformer-based molecular optimization beyond matched molecular pairs |
title_full_unstemmed | Transformer-based molecular optimization beyond matched molecular pairs |
title_short | Transformer-based molecular optimization beyond matched molecular pairs |
title_sort | transformer-based molecular optimization beyond matched molecular pairs |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8962145/ https://www.ncbi.nlm.nih.gov/pubmed/35346368 http://dx.doi.org/10.1186/s13321-022-00599-3 |
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