Enhanced prediction of beta-secretase inhibitory compounds with mol2vec technique and machine learning algorithms

A comprehensive computational strategy that combined QSAR modelling, molecular docking, and ADMET analysis was used to discover potential inhibitors for β-secretase 1 (BACE-1). A dataset of 1,138 compounds with established BACE-1 inhibitory activities was used to build a QSAR model using mol2vec descriptors and support vector regression. The obtained model demonstrated strong predictive performance (training set: <i>r</i>² = 0.790, RMSE = 0.540, MAE = 0.362; test set: <i>r</i>² = 0.705, RMSE = 0.641, MAE = 0.495), indicating its reliability in identifying potent BACE-1 inhibitors. By applying this QSAR model together with molecular docking, seven compounds (ZINC8790287, ZINC20464117, ZINC8878274, ZINC96116481, ZINC217682404, ZINC217786309 and ZINC96113994) were identified as promising candidates, exhibiting predicted log IC₅₀ values ranging from 0.361 to 1.993 and binding energies ranging from −10.8 to −10.7 kcal/mol. Further analysis using ADMET studies and molecular dynamics simulations provided further support for the potential of compound 279 (ZINC96116481) and compound 945 (ZINC96113994) as drug candidates. However, since our study is purely theoretical, further experimental validation through in vitro and in vivo studies is essential to confirm these promising findings.

本研究采用结合定量构效关系（QSAR）建模、分子对接与吸收、分布、代谢、排泄与毒性（ADMET）分析的综合计算策略，用于发掘β-分泌酶1（BACE-1）的潜在抑制剂。本研究使用包含1138种已验证BACE-1抑制活性化合物的数据集，以mol2vec描述符与支持向量回归构建QSAR模型。所构建的模型展现出优异的预测性能（训练集：决定系数r²=0.790，均方根误差RMSE=0.540，平均绝对误差MAE=0.362；测试集：决定系数r²=0.705，均方根误差RMSE=0.641，平均绝对误差MAE=0.495），证实其在筛选强效BACE-1抑制剂方面具备可靠性。联合应用该QSAR模型与分子对接技术，最终筛选得到7种极具开发潜力的候选化合物（ZINC8790287、ZINC20464117、ZINC8878274、ZINC96116481、ZINC217682404、ZINC217786309及ZINC96113994），其预测对数半数抑制浓度（log IC₅₀）范围为0.361至1.993，结合能介于-10.8至-10.7 kcal/mol之间。后续通过ADMET性质分析与分子动力学模拟开展的进一步验证，证实化合物279（ZINC96116481）与化合物945（ZINC96113994）具备候选药物的开发潜力。然而本研究仅为纯理论计算研究，仍需通过体外与体内实验开展进一步验证，方能确认上述极具潜力的研究结果。