Cargando…

Deep learning-based classification model for GPR151 activator activity prediction

BACKGROUND: GPR151 is a kind of protein belonging to G protein-coupled receptor family that is closely associated with a variety of physiological and pathological processes.The potential use of GPR151 as a therapeutic target for the management of metabolic disorders has been demonstrated in several...

Descripción completa

Detalles Bibliográficos
Autores principales: Xu, Huangchao, Zhang, Baohua, Liu, Qian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10257328/
https://www.ncbi.nlm.nih.gov/pubmed/37296398
http://dx.doi.org/10.1186/s12859-023-05369-y
_version_ 1785057279554355200
author Xu, Huangchao
Zhang, Baohua
Liu, Qian
author_facet Xu, Huangchao
Zhang, Baohua
Liu, Qian
author_sort Xu, Huangchao
collection PubMed
description BACKGROUND: GPR151 is a kind of protein belonging to G protein-coupled receptor family that is closely associated with a variety of physiological and pathological processes.The potential use of GPR151 as a therapeutic target for the management of metabolic disorders has been demonstrated in several studies, highlighting the demand to explore its activators further. Activity prediction serves as a vital preliminary step in drug discovery, which is both costly and time-consuming. Thus, the development of reliable activity classification model has become an essential way in the process of drug discovery, aiming to enhance the efficiency of virtual screening. RESULTS: We propose a learning-based method based on feature extractor and deep neural network to predict the activity of GPR151 activators. We first introduce a new molecular feature extraction algorithm which utilizes the idea of bag-of-words model in natural language to densify the sparse fingerprint vector. Mol2vec method is also used to extract diverse features. Then, we construct three classical feature selection algorithms and three types of deep learning model to enhance the representational capacity of molecules and predict activity label by five different classifiers. We conduct experiments using our own dataset of GPR151 activators. The results demonstrate high classification accuracy and stability, with the optimal model Mol2vec-CNN significantly improving performance across multiple classifiers. The svm classifier achieves the best accuracy of 0.92 and F1 score of 0.76 which indicates promising applications for our method in the field of activity prediction. CONCLUSION: The results suggest that the experimental design of this study is appropriate and well-conceived. The deep learning-based feature extraction algorithm established in this study outperforms traditional feature selection algorithm for activity prediction. The model developed can be effectively utilized in the pre-screening stage of drug virtual screening.
format Online
Article
Text
id pubmed-10257328
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-102573282023-06-11 Deep learning-based classification model for GPR151 activator activity prediction Xu, Huangchao Zhang, Baohua Liu, Qian BMC Bioinformatics Research BACKGROUND: GPR151 is a kind of protein belonging to G protein-coupled receptor family that is closely associated with a variety of physiological and pathological processes.The potential use of GPR151 as a therapeutic target for the management of metabolic disorders has been demonstrated in several studies, highlighting the demand to explore its activators further. Activity prediction serves as a vital preliminary step in drug discovery, which is both costly and time-consuming. Thus, the development of reliable activity classification model has become an essential way in the process of drug discovery, aiming to enhance the efficiency of virtual screening. RESULTS: We propose a learning-based method based on feature extractor and deep neural network to predict the activity of GPR151 activators. We first introduce a new molecular feature extraction algorithm which utilizes the idea of bag-of-words model in natural language to densify the sparse fingerprint vector. Mol2vec method is also used to extract diverse features. Then, we construct three classical feature selection algorithms and three types of deep learning model to enhance the representational capacity of molecules and predict activity label by five different classifiers. We conduct experiments using our own dataset of GPR151 activators. The results demonstrate high classification accuracy and stability, with the optimal model Mol2vec-CNN significantly improving performance across multiple classifiers. The svm classifier achieves the best accuracy of 0.92 and F1 score of 0.76 which indicates promising applications for our method in the field of activity prediction. CONCLUSION: The results suggest that the experimental design of this study is appropriate and well-conceived. The deep learning-based feature extraction algorithm established in this study outperforms traditional feature selection algorithm for activity prediction. The model developed can be effectively utilized in the pre-screening stage of drug virtual screening. BioMed Central 2023-06-09 /pmc/articles/PMC10257328/ /pubmed/37296398 http://dx.doi.org/10.1186/s12859-023-05369-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Xu, Huangchao
Zhang, Baohua
Liu, Qian
Deep learning-based classification model for GPR151 activator activity prediction
title Deep learning-based classification model for GPR151 activator activity prediction
title_full Deep learning-based classification model for GPR151 activator activity prediction
title_fullStr Deep learning-based classification model for GPR151 activator activity prediction
title_full_unstemmed Deep learning-based classification model for GPR151 activator activity prediction
title_short Deep learning-based classification model for GPR151 activator activity prediction
title_sort deep learning-based classification model for gpr151 activator activity prediction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10257328/
https://www.ncbi.nlm.nih.gov/pubmed/37296398
http://dx.doi.org/10.1186/s12859-023-05369-y
work_keys_str_mv AT xuhuangchao deeplearningbasedclassificationmodelforgpr151activatoractivityprediction
AT zhangbaohua deeplearningbasedclassificationmodelforgpr151activatoractivityprediction
AT liuqian deeplearningbasedclassificationmodelforgpr151activatoractivityprediction