Syahid Nur Fadhilah, Weerapreeyakul Natthida, Srisongkram Tarapong
Graduate School in the Program of Pharmaceutical Chemistry and Natural Products, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
Division of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
ACS Omega. 2023 Jun 1;8(23):20881-20891. doi: 10.1021/acsomega.3c01641. eCollection 2023 Jun 13.
The B-rapidly accelerated fibrosarcoma (BRAF) is a proto-oncogene that plays a vital role in cell signaling and growth regulation. Identifying a potent BRAF inhibitor can enhance therapeutic success in high-stage cancers, particularly metastatic melanoma. In this study, we proposed a stacking ensemble learning framework for the accurate prediction of BRAF inhibitors. We obtained 3857 curated molecules with BRAF inhibitory activity expressed as a predicted half-maximal inhibitory concentration value (pIC) from the ChEMBL database. Twelve molecular fingerprints from PaDeL-Descriptor were calculated for model training. Three machine learning algorithms including extreme gradient boosting, support vector regression, and multilayer perceptron were utilized for constructing new predictive features (PFs). The meta-ensemble random forest regression, called StackBRAF, was created based on the 36 PFs. The StackBRAF model achieves lower mean absolute error (MAE) and higher coefficient of determination ( and ) than the individual baseline models. The stacking ensemble learning model provides good -randomization results, indicating a strong correlation between molecular features and pIC. An applicability domain of the model with an acceptable Tanimoto similarity score was also defined. Moreover, a large-scale high-throughput screening of 2123 FDA-approved drugs against the BRAF protein was successfully demonstrated using the StackBRAF algorithm. Thus, the StackBRAF model proved beneficial as a drug design algorithm for BRAF inhibitor drug discovery and drug development.
B-快速加速纤维肉瘤（BRAF）是一种原癌基因，在细胞信号传导和生长调节中起着至关重要的作用。鉴定一种有效的BRAF抑制剂可以提高晚期癌症，特别是转移性黑色素瘤的治疗成功率。在本研究中，我们提出了一种堆叠集成学习框架，用于准确预测BRAF抑制剂。我们从ChEMBL数据库中获得了3857个经过整理的具有BRAF抑制活性的分子，其抑制活性以预测的半数最大抑制浓度值（pIC）表示。为进行模型训练，计算了来自PaDeL-Descriptor的12种分子指纹。利用包括极端梯度提升、支持向量回归和多层感知器在内的三种机器学习算法构建新的预测特征（PFs）。基于这36个PFs创建了元集成随机森林回归模型，称为StackBRAF。与单个基线模型相比，StackBRAF模型实现了更低的平均绝对误差（MAE）和更高的决定系数（和）。堆叠集成学习模型提供了良好的随机化结果，表明分子特征与pIC之间存在强相关性。还定义了具有可接受的Tanimoto相似性分数的模型适用域。此外，使用StackBRAF算法成功展示了针对BRAF蛋白的2123种FDA批准药物的大规模高通量筛选。因此，StackBRAF模型被证明是一种有益的药物设计算法，可用于BRAF抑制剂药物发现和药物开发。
