摘要
Abstract
Background:In many developing countries,up to 80％of the population depends on traditional medicine.This includes the use of plants like Hypoxis hemerocallidea(African potato),which is widely utilized in Southern Africa for human immunodeficiency virus(HIV)treatment,despite limited scientific evidence to support its efficacy.
Objective:The objective of this study was to bridge African ethnopharmacology and modern science by employing artificial intelligence(AI)and computational chemistry to predict antiretroviral mechanisms of action among H.hemerocallidea phytochemicals.
Methods:The machine learning(ML)models,namely HistGradientBoosting(HGB)and XGBoost(XGB)were trained on seven ChEMBL datasets to predict five antiretroviral mechanisms of action,including reverse tran-scriptase inhibitor(RTI),protease inhibitor(PI),the envelope glycoprotein 160 of the human immunodeficiency virus(gp160)entry inhibitors,C-C chemokine receptor type 5(CCR5)inhibitors,and integrase strand trans-fer inhibitor(INSTI).Molecular descriptors were generated using extended connectivity fingerprints 4(ECFP4).A rigorous feature selection procedure(threshold＜0.01)was applied,and hyperparameter optimization was performed using Optuna,with model performance evaluated through 10-fold cross-validation.The resulting quantitative structure-activity relationship(QSAR)and ML models screened 47 main phytochemicals from H.hemerocallidea.Compounds were considered potentially active when the average predicted pIC50 exceeded 6 and the coefficient of variation among predictions was below 5％.
Results:The models demonstrated excellent predictive performance,especially for CCR5 inhibitors.HGB achieved an R2 score of 0.91 on the testing set,while XGB achieved 0.94.Procyanidin A2 was identified as a potent potential CCR5 inhibitor.No other compounds met the activity criteria(mean pIC50 ≥ 6;CV＜5％)for any of the remaining antiretroviral mechanisms.
Conclusion:By integrating AI and ethnopharmacology,this study applied machine learning to identify potential antiretroviral mechanisms of action among the main phytochemicals of H.hemerocallidea,providing a molecular rationale for traditional medicine and predicting potential bioactive candidates.Experimental validation remains necessary to confirm these computational findings.关键词
Hypoxis hemerocallidea/机器学习/定量构效关系/抗逆转录病毒/民族药理学Key words
Hypoxis hemerocallidea/Machine learning/QSAR/Antiretroviral/Ethnopharmacology
