磺基转移酶(SULTs)是主要的II期代谢酶类别,对FDA批准的药物的II期代谢贡献约20%。忽略SULT介导的代谢的潜力会导致药物-药物相互作用的强大潜力,经常导致后期药物发现失败或FDA标签上的黑盒警告。现有模型仅使用可访问性描述符和机器学习(ML)方法来进行SULT的磺化类别和站点(SOS)预测。在这项研究中,各种可达性,反应性,并开发了混合模型和算法来进行准确的底物和SOS预测。与文献模型不同,脂族或芳族羟基(R/Ar-O-H)的反应性参数,对于SOS预测,债券解离能量(BDE)给出了真实阳性率(TPR)=0.84的准确模型。我们提供了机械论的见解来解释这些在文献中没有认识到的新发现。可达性参数,例如Chemgauss4评分(CGS)和分子量(MW)CGS/MW的比率以及与辅因子的距离(Dis)对于类别预测至关重要,并显示TPR=0.72。基质的BDE含量一直较低,Dis,和CGS/MW比非底物。混合模型对于SOS预测也是可以接受的。使用最好的模型,算法在类别预测中给出了可接受的性能:TPR=0.62,假阳性率(FPR)=0.24,平衡精度(BA)=0.69,SOS预测:TPR=0.98,FPR=0.60,BA=0.69。添加了基于规则的方法来提高预测性能,改进了算法TPR,FPR,和BA。对于最佳算法,使用药物样化合物的外部数据集的验证给出类别预测:TPR=0.67,FPR=0.00,和SOS预测:TPR=0.80和FPR=0.44。与标准ML模型的比较还表明,我们的算法对外部数据集的分类显示出更高的预测性能。总的来说,这些模型和算法(SOS预测器)为SULT介导的II期代谢提供了准确的底物类别和位点(SOS)预测,对学术界和工业界的药物发现界将是有价值的.SOS预测器可通过GitHub链接免费用于学术/非营利研究。
Sulphotransferases (SULTs) are a major phase II metabolic enzyme class contributing ~20 % to the Phase II metabolism of FDA-approved drugs. Ignoring the potential for SULT-mediated metabolism leaves a strong potential for drug-drug interactions, often causing late-stage drug discovery failures or black-boxed warnings on FDA labels. The existing models use only accessibility descriptors and machine learning (ML) methods for class and site of sulfonation (SOS) predictions for SULT. In this study, a variety of accessibility,
reactivity, and hybrid models and algorithms have been developed to make accurate substrate and SOS predictions. Unlike the literature models,
reactivity parameters for the aliphatic or aromatic hydroxyl groups (R/Ar-O-H), the Bond Dissociation Energy (BDE) gave accurate models with a True Positive Rate (TPR)=0.84 for SOS predictions. We offer mechanistic insights to explain these novel findings that are not recognized in the literature. The accessibility parameters like the ratio of Chemgauss4 Score (CGS) and Molecular Weight (MW) CGS/MW and distance from cofactor (Dis) were essential for class predictions and showed TPR=0.72. Substrates consistently had lower BDE, Dis, and CGS/MW than non-substrates. Hybrid models also performed acceptablely for SOS predictions. Using the best models, Algorithms gave an acceptable performance in class prediction: TPR=0.62, False Positive Rate (FPR)=0.24, Balanced accuracy (BA)=0.69, and SOS prediction: TPR=0.98, FPR=0.60, and BA=0.69. A rule-based method was added to improve the predictive performance, which improved the algorithm TPR, FPR, and BA. Validation using an external dataset of drug-like compounds gave class prediction: TPR=0.67, FPR=0.00, and SOS prediction: TPR=0.80 and FPR=0.44 for the best Algorithm. Comparisons with standard ML models also show that our algorithm shows higher predictive performance for classification on external datasets. Overall, these models and algorithms (SOS predictor) give accurate substrate class and site (SOS) predictions for SULT-mediated Phase II metabolism and will be valuable to the drug discovery community in academia and industry. The SOS predictor is freely available for academic/non-profit research via the GitHub link.