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Machine Learning Models to Predict Protein–Protein Interaction Inhibitors

Protein–protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to...

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Autores principales: Díaz-Eufracio, Bárbara I., Medina-Franco, José L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694076/
https://www.ncbi.nlm.nih.gov/pubmed/36432086
http://dx.doi.org/10.3390/molecules27227986
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author Díaz-Eufracio, Bárbara I.
Medina-Franco, José L.
author_facet Díaz-Eufracio, Bárbara I.
Medina-Franco, José L.
author_sort Díaz-Eufracio, Bárbara I.
collection PubMed
description Protein–protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available.
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spelling pubmed-96940762022-11-26 Machine Learning Models to Predict Protein–Protein Interaction Inhibitors Díaz-Eufracio, Bárbara I. Medina-Franco, José L. Molecules Article Protein–protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available. MDPI 2022-11-17 /pmc/articles/PMC9694076/ /pubmed/36432086 http://dx.doi.org/10.3390/molecules27227986 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Díaz-Eufracio, Bárbara I.
Medina-Franco, José L.
Machine Learning Models to Predict Protein–Protein Interaction Inhibitors
title Machine Learning Models to Predict Protein–Protein Interaction Inhibitors
title_full Machine Learning Models to Predict Protein–Protein Interaction Inhibitors
title_fullStr Machine Learning Models to Predict Protein–Protein Interaction Inhibitors
title_full_unstemmed Machine Learning Models to Predict Protein–Protein Interaction Inhibitors
title_short Machine Learning Models to Predict Protein–Protein Interaction Inhibitors
title_sort machine learning models to predict protein–protein interaction inhibitors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694076/
https://www.ncbi.nlm.nih.gov/pubmed/36432086
http://dx.doi.org/10.3390/molecules27227986
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