DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology

Abstract In polypharmacology drugs are required to bind to multiple specific targets, for example to enhance efficacy or to reduce resistance formation. Although deep learning has achieved a breakthrough in de novo design in drug discovery, most of its applications only focus on a single drug target...

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Autores principales:	Xuhan Liu, Kai Ye, Herman W. T. van Vlijmen, Michael T. M. Emmerich, Adriaan P. IJzerman, Gerard J. P. van Westen
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Deep learning Adenosine receptors Cheminformatics Reinforcement learning Multi-objective optimization Exploration strategy Information technology T58.5-58.64 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/0871ce20cd084c358f772f94aba024a5
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spelling	oai:doaj.org-article:0871ce20cd084c358f772f94aba024a52021-11-14T12:33:31ZDrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology10.1186/s13321-021-00561-91758-2946https://doaj.org/article/0871ce20cd084c358f772f94aba024a52021-11-01T00:00:00Zhttps://doi.org/10.1186/s13321-021-00561-9https://doaj.org/toc/1758-2946Abstract In polypharmacology drugs are required to bind to multiple specific targets, for example to enhance efficacy or to reduce resistance formation. Although deep learning has achieved a breakthrough in de novo design in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules. However, in reality drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug-like molecules towards multiple targets or one specific target while avoiding off-targets (the two adenosine receptors, A1AR and A2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment. Both the agent and the environment were pre-trained in advance and then interplayed under a reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used to construct Pareto ranks of the generated molecules. For this ranking a non-dominated sorting algorithm and a Tanimoto-based crowding distance algorithm using chemical fingerprints are applied. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile towards multiple targets, offering the potential of high efficacy and low toxicity.Xuhan LiuKai YeHerman W. T. van VlijmenMichael T. M. EmmerichAdriaan P. IJzermanGerard J. P. van WestenBMCarticleDeep learningAdenosine receptorsCheminformaticsReinforcement learningMulti-objective optimizationExploration strategyInformation technologyT58.5-58.64ChemistryQD1-999ENJournal of Cheminformatics, Vol 13, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep learning Adenosine receptors Cheminformatics Reinforcement learning Multi-objective optimization Exploration strategy Information technology T58.5-58.64 Chemistry QD1-999
spellingShingle	Deep learning Adenosine receptors Cheminformatics Reinforcement learning Multi-objective optimization Exploration strategy Information technology T58.5-58.64 Chemistry QD1-999 Xuhan Liu Kai Ye Herman W. T. van Vlijmen Michael T. M. Emmerich Adriaan P. IJzerman Gerard J. P. van Westen DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology
description	Abstract In polypharmacology drugs are required to bind to multiple specific targets, for example to enhance efficacy or to reduce resistance formation. Although deep learning has achieved a breakthrough in de novo design in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules. However, in reality drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug-like molecules towards multiple targets or one specific target while avoiding off-targets (the two adenosine receptors, A1AR and A2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment. Both the agent and the environment were pre-trained in advance and then interplayed under a reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used to construct Pareto ranks of the generated molecules. For this ranking a non-dominated sorting algorithm and a Tanimoto-based crowding distance algorithm using chemical fingerprints are applied. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile towards multiple targets, offering the potential of high efficacy and low toxicity.
format	article
author	Xuhan Liu Kai Ye Herman W. T. van Vlijmen Michael T. M. Emmerich Adriaan P. IJzerman Gerard J. P. van Westen
author_facet	Xuhan Liu Kai Ye Herman W. T. van Vlijmen Michael T. M. Emmerich Adriaan P. IJzerman Gerard J. P. van Westen
author_sort	Xuhan Liu
title	DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology
title_short	DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology
title_full	DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology
title_fullStr	DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology
title_full_unstemmed	DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology
title_sort	drugex v2: de novo design of drug molecules by pareto-based multi-objective reinforcement learning in polypharmacology
publisher	BMC
publishDate	2021
url	https://doaj.org/article/0871ce20cd084c358f772f94aba024a5
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DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology

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