MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information
Issued Date
2024-10-21
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85207201657
Pubmed ID
39433940
Journal Title
Scientific reports
Volume
14
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific reports Vol.14 No.1 (2024) , 24764
Suggested Citation
Schaduangrat N., Khemawoot P., Jiso A., Charoenkwan P., Shoombuatong W. MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information. Scientific reports Vol.14 No.1 (2024) , 24764. doi:10.1038/s41598-024-75487-x Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101831
Title
MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information
Corresponding Author(s)
Other Contributor(s)
Abstract
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 .