Exploring the chemical space of protein–protein interaction inhibitors through machine learning

Abstract Although protein–protein interactions (PPIs) have emerged as the basis of potential new therapeutic approaches, targeting intracellular PPIs with small molecule inhibitors is conventionally considered highly challenging. Driven by increasing research efforts, success rates have increased si...

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Autores principales:	Jiwon Choi, Jun Seop Yun, Hyeeun Song, Nam Hee Kim, Hyun Sil Kim, Jong In Yook
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d4b406ee4e724930b430cee4e2117dfb
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id	oai:doaj.org-article:d4b406ee4e724930b430cee4e2117dfb
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spelling	oai:doaj.org-article:d4b406ee4e724930b430cee4e2117dfb2021-12-02T14:33:51ZExploring the chemical space of protein–protein interaction inhibitors through machine learning10.1038/s41598-021-92825-52045-2322https://doaj.org/article/d4b406ee4e724930b430cee4e2117dfb2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92825-5https://doaj.org/toc/2045-2322Abstract Although protein–protein interactions (PPIs) have emerged as the basis of potential new therapeutic approaches, targeting intracellular PPIs with small molecule inhibitors is conventionally considered highly challenging. Driven by increasing research efforts, success rates have increased significantly in recent years. In this study, we analyze the physicochemical properties of 9351 non-redundant inhibitors present in the iPPI-DB and TIMBAL databases to define a computational model for active compounds acting against PPI targets. Principle component analysis (PCA) and k-means clustering were used to identify plausible PPI targets in regions of interest in the active group in the chemical space between active and inactive iPPI compounds. Notably, the uniquely defined active group exhibited distinct differences in activity compared with other active compounds. These results demonstrate that active compounds with regions of interest in the chemical space may be expected to provide insights into potential PPI inhibitors for particular protein targets.Jiwon ChoiJun Seop YunHyeeun SongNam Hee KimHyun Sil KimJong In YookNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jiwon Choi Jun Seop Yun Hyeeun Song Nam Hee Kim Hyun Sil Kim Jong In Yook Exploring the chemical space of protein–protein interaction inhibitors through machine learning
description	Abstract Although protein–protein interactions (PPIs) have emerged as the basis of potential new therapeutic approaches, targeting intracellular PPIs with small molecule inhibitors is conventionally considered highly challenging. Driven by increasing research efforts, success rates have increased significantly in recent years. In this study, we analyze the physicochemical properties of 9351 non-redundant inhibitors present in the iPPI-DB and TIMBAL databases to define a computational model for active compounds acting against PPI targets. Principle component analysis (PCA) and k-means clustering were used to identify plausible PPI targets in regions of interest in the active group in the chemical space between active and inactive iPPI compounds. Notably, the uniquely defined active group exhibited distinct differences in activity compared with other active compounds. These results demonstrate that active compounds with regions of interest in the chemical space may be expected to provide insights into potential PPI inhibitors for particular protein targets.
format	article
author	Jiwon Choi Jun Seop Yun Hyeeun Song Nam Hee Kim Hyun Sil Kim Jong In Yook
author_facet	Jiwon Choi Jun Seop Yun Hyeeun Song Nam Hee Kim Hyun Sil Kim Jong In Yook
author_sort	Jiwon Choi
title	Exploring the chemical space of protein–protein interaction inhibitors through machine learning
title_short	Exploring the chemical space of protein–protein interaction inhibitors through machine learning
title_full	Exploring the chemical space of protein–protein interaction inhibitors through machine learning
title_fullStr	Exploring the chemical space of protein–protein interaction inhibitors through machine learning
title_full_unstemmed	Exploring the chemical space of protein–protein interaction inhibitors through machine learning
title_sort	exploring the chemical space of protein–protein interaction inhibitors through machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d4b406ee4e724930b430cee4e2117dfb
work_keys_str_mv	AT jiwonchoi exploringthechemicalspaceofproteinproteininteractioninhibitorsthroughmachinelearning AT junseopyun exploringthechemicalspaceofproteinproteininteractioninhibitorsthroughmachinelearning AT hyeeunsong exploringthechemicalspaceofproteinproteininteractioninhibitorsthroughmachinelearning AT namheekim exploringthechemicalspaceofproteinproteininteractioninhibitorsthroughmachinelearning AT hyunsilkim exploringthechemicalspaceofproteinproteininteractioninhibitorsthroughmachinelearning AT jonginyook exploringthechemicalspaceofproteinproteininteractioninhibitorsthroughmachinelearning
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Exploring the chemical space of protein–protein interaction inhibitors through machine learning

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