Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan, 10540, Thailand.
Sci Rep. 2024 Oct 21;14(1):24764. doi: 10.1038/s41598-024-75487-x.
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 .
偏头痛被认为是一种使人虚弱的原发性头痛疾病，全球发病率约为 14-15%，对全球残疾负有重要责任。降钙素基因相关肽（CGRP）是一种神经肽，在偏头痛的病理生理学中起着至关重要的作用，因此，其抑制作用可以帮助缓解偏头痛症状。然而，CGRP 药物开发的传统过程既费力又耗时，成本超过 10 亿美元。另一方面，能够准确识别 CGRP 抑制剂的基于机器学习（ML）的方法可以极大地促进新型 CGRP 药物的发现。因此，本研究提出了一种新颖且高精度的元模型，即 MetaCGRP，它可以精确识别 CGRP 抑制剂。据我们所知，MetaCGRP 是第一个开发的基于 SMILES 的方法，可在不使用 3D 结构信息的情况下识别 CGRP 抑制剂。简而言之，我们最初采用了不同的分子表示方法，并结合了流行的 ML 算法，构建了一个基线模型池。然后，优化所有基线模型并用于生成多视图特征。最后，我们采用特征选择方法优化多视图特征，并确定最佳特征子集，以构建元模型。交叉验证和独立测试均表明，MetaCGRP 明显优于几种传统的 ML 分类器，在训练和独立测试数据集上的准确率分别为 0.898 和 0.799。此外，MetaCGRP 与分子对接结合使用，从泰国草药药典中鉴定了五个潜在的天然产物候选物，并分析了它们与 CGRP 的结合亲和力和相互作用。为了促进社区在加速新型 CGRP 抑制剂发现方面的努力，MetaCGRP 的用户友好型网络服务器可在 https://pmlabqsar.pythonanywhere.com/MetaCGRP 免费获得。
