MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information.
Calcitonin gene-related peptide
Cheminformatics
Feature selection
Machine learning
Meta-model
QSAR
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
21 Oct 2024
21 Oct 2024
Historique:
received:
24
07
2024
accepted:
07
10
2024
medline:
22
10
2024
pubmed:
22
10
2024
entrez:
21
10
2024
Statut:
epublish
Résumé
Migraine is considered one of the debilitating primary headache conditions with an estimated worldwide occurrence of approximately 14-15%, contributing highly to factors responsible for global disability. Calcitonin gene-related peptide (CGRP) is a neuropeptide that plays a crucial role in the pathophysiology of migraines and thus, its inhibition can help relieve migraine symptoms. However, conventional process of CGRP drug development has been laborious and time-consuming with incurred costs exceeding one billion dollars. On the other hand, machine learning (ML)-based approaches that are capable of accurately identifying CGRP inhibitors could greatly facilitate in expediting the discovery of novel CGRP drugs. Therefore, this study proposes a novel and high-accuracy meta-model, namely MetaCGRP, that can precisely identify CGRP inhibitors. To the best of our knowledge, MetaCGRP is the first SMILES-based approach that has been developed to identify CGRP inhibitors without the use of 3D structural information. In brief, we initially employed different molecular representation methods coupled with popular ML algorithms to construct a pool of baseline models. Then, all baseline models were optimized and used to generate multi-view features. Finally, we employed the feature selection method to optimize the multi-view features and determine the best feature subset to enable the construction of the meta-model. Both cross-validation and independent tests indicated that MetaCGRP clearly outperforms several conventional ML classifiers, with accuracies of 0.898 and 0.799 on the training and independent test datasets, respectively. In addition, MetaCGRP in conjunction with molecular docking was utilized to identify five potential natural product candidates from Thai herbal pharmacopoeia and analyze their binding affinity and interactions to CGRP. To facilitate community-wide efforts in expediting the discovery of novel CGRP inhibitors, a user-friendly web server for MetaCGRP is freely available at https://pmlabqsar.pythonanywhere.com/MetaCGRP .
Identifiants
pubmed: 39433940
doi: 10.1038/s41598-024-75487-x
pii: 10.1038/s41598-024-75487-x
doi:
Substances chimiques
Calcitonin Gene-Related Peptide
JHB2QIZ69Z
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
24764Subventions
Organisme : National Research Council of Thailand and Mahidol University
ID : N42A660380
Informations de copyright
© 2024. The Author(s).
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