华南理工大学学报（自然科学版）2026，Vol.54Issue(2)：91-101,11.DOI:10.12141/j.issn.1000-565X.250020
集成机器学习和元启发式算法的靶点抑制剂活性预测
Prediction of Target Inhibitor Activity by Integrating Machine Learning and Metaheuristic Algorithms
摘要
Abstract
Traditional machine learning(ML)and deep learning(DL)play a key role in predicting the activity of target inhibitors.Many models based on existing datasets can predict compound bioactivity.However,debate per-sists regarding whether ML or DL performs better for such prediction tasks.In this study,datasets were constructed based on different molecular representations.Ten metaheuristic algorithms were applied to optimize the hyperpa-rameters of eleven ML and DL models,aiming to systematically compare their predictive performance and identify the optimal ones.The results show that ML and DL models whose hyperparameters were optimized by metaheuristic algorithms significantly outperformed those optimized using the traditional grid search method.Furthermore,in low-dimensional feature spaces,graph-based DL models,such as SSA-GAT and SSA-Attentive FP,can automatically extract informative features from data via an end-to-end learning mechanism,yielding better performance than ML models.In contrast,in high-dimensional feature spaces(e.g.,the feature space formed by combining RDKit descriptors with ECFP,AtomPairs,and MACCS fingerprints),ML methods,leveraging the complementary information in molecular features and the powerful optimization capability of metaheuristic algorithms,can effectively capture complex feature interactions.Consequently,ML methods often demonstrate higher accuracy and robustness in high-dimensional modeling.These findings provide valuable guidance for selecting between ML and DL approaches for target inhibitor activity prediction.关键词
元启发式优化算法/机器学习/深度学习/靶点抑制剂活性/分子指纹/分子图Key words
metaheuristic optimization algorithm/machine learning/deep learning/target inhibitor activity/molecular fingerprints/molecular graph分类
信息技术与安全科学引用本文复制引用
凌飞,顾学荣..集成机器学习和元启发式算法的靶点抑制剂活性预测[J].华南理工大学学报（自然科学版）,2026,54(2):91-101,11.基金项目
国家自然科学基金项目(12322119,12401630)Supported by the National Natural Science Foundation of China(12322119,12401630) （12322119,12401630）
