Prediction of pharmacological activities from chemical structures with graph convolutional neural networks

Abstract Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such...

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Autores principales:	Miyuki Sakai, Kazuki Nagayasu, Norihiro Shibui, Chihiro Andoh, Kaito Takayama, Hisashi Shirakawa, Shuji Kaneko
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e24b906189ad43caa5818c58ca25debc
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spelling	oai:doaj.org-article:e24b906189ad43caa5818c58ca25debc2021-12-02T14:12:10ZPrediction of pharmacological activities from chemical structures with graph convolutional neural networks10.1038/s41598-020-80113-72045-2322https://doaj.org/article/e24b906189ad43caa5818c58ca25debc2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80113-7https://doaj.org/toc/2045-2322Abstract Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds. For models that make quantitative predictions of activity, more complex information has been utilized, such as the three-dimensional structures of compounds and the amino acid sequences of their respective target proteins. As another approach, we hypothesized that if sufficient experimental data were available and there were enough nodes in hidden layers, a simple compound representation would quantitatively predict activity with satisfactory accuracy. In this study, we report that GCN models constructed solely from the two-dimensional structural information of compounds demonstrated a high degree of activity predictability against 127 diverse targets from the ChEMBL database. Using the information entropy as a metric, we also show that the structural diversity had less effect on the prediction performance. Finally, we report that virtual screening using the constructed model identified a new serotonin transporter inhibitor with activity comparable to that of a marketed drug in vitro and exhibited antidepressant effects in behavioural studies.Miyuki SakaiKazuki NagayasuNorihiro ShibuiChihiro AndohKaito TakayamaHisashi ShirakawaShuji KanekoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Miyuki Sakai Kazuki Nagayasu Norihiro Shibui Chihiro Andoh Kaito Takayama Hisashi Shirakawa Shuji Kaneko Prediction of pharmacological activities from chemical structures with graph convolutional neural networks
description	Abstract Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds. For models that make quantitative predictions of activity, more complex information has been utilized, such as the three-dimensional structures of compounds and the amino acid sequences of their respective target proteins. As another approach, we hypothesized that if sufficient experimental data were available and there were enough nodes in hidden layers, a simple compound representation would quantitatively predict activity with satisfactory accuracy. In this study, we report that GCN models constructed solely from the two-dimensional structural information of compounds demonstrated a high degree of activity predictability against 127 diverse targets from the ChEMBL database. Using the information entropy as a metric, we also show that the structural diversity had less effect on the prediction performance. Finally, we report that virtual screening using the constructed model identified a new serotonin transporter inhibitor with activity comparable to that of a marketed drug in vitro and exhibited antidepressant effects in behavioural studies.
format	article
author	Miyuki Sakai Kazuki Nagayasu Norihiro Shibui Chihiro Andoh Kaito Takayama Hisashi Shirakawa Shuji Kaneko
author_facet	Miyuki Sakai Kazuki Nagayasu Norihiro Shibui Chihiro Andoh Kaito Takayama Hisashi Shirakawa Shuji Kaneko
author_sort	Miyuki Sakai
title	Prediction of pharmacological activities from chemical structures with graph convolutional neural networks
title_short	Prediction of pharmacological activities from chemical structures with graph convolutional neural networks
title_full	Prediction of pharmacological activities from chemical structures with graph convolutional neural networks
title_fullStr	Prediction of pharmacological activities from chemical structures with graph convolutional neural networks
title_full_unstemmed	Prediction of pharmacological activities from chemical structures with graph convolutional neural networks
title_sort	prediction of pharmacological activities from chemical structures with graph convolutional neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e24b906189ad43caa5818c58ca25debc
work_keys_str_mv	AT miyukisakai predictionofpharmacologicalactivitiesfromchemicalstructureswithgraphconvolutionalneuralnetworks AT kazukinagayasu predictionofpharmacologicalactivitiesfromchemicalstructureswithgraphconvolutionalneuralnetworks AT norihiroshibui predictionofpharmacologicalactivitiesfromchemicalstructureswithgraphconvolutionalneuralnetworks AT chihiroandoh predictionofpharmacologicalactivitiesfromchemicalstructureswithgraphconvolutionalneuralnetworks AT kaitotakayama predictionofpharmacologicalactivitiesfromchemicalstructureswithgraphconvolutionalneuralnetworks AT hisashishirakawa predictionofpharmacologicalactivitiesfromchemicalstructureswithgraphconvolutionalneuralnetworks AT shujikaneko predictionofpharmacologicalactivitiesfromchemicalstructureswithgraphconvolutionalneuralnetworks
_version_	1718391816987869184

Prediction of pharmacological activities from chemical structures with graph convolutional neural networks

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