基于机器学习的KRAS抑制剂活性预测模型研究

Research on machine learning-based activity prediction models for KRAS inhibitors

Kirsten大鼠肉瘤病毒癌基因同系物(Kirsten rat sarcoma viral oncogene homolog,KRAS)基因是最常见的突变癌基因之一,发现KRAS抑制剂对存在该基因突变的癌症患者具有潜在的治疗作用。本研究将机器学习应用于KRAS抑制剂的定量构效关系(quantitative structure-activity relationship,QSAR)模型,从ChEMBL、BindingDB、PubChem 3个数据库中收集了1857条KRAS小分子抑制剂的IC50和SMILES(simplified molecular input line entry system),采用3种不同的特征筛选方式结合随机森林、支持向量机、极端梯度提升机3种机器学习模型,构建了9个不同的分类器。结果表明,SVM模型结合互信息筛选显示出最佳性能:AUC_(test)=0.912,ACC_(test)=0.859,F1_(test)=0.890,并且在外部验证集上也表现出良好的预测性能(AUC_(Ext)=0.944,Recall_(Ext)=0.856,FPR_(Ext)=0.111)。该研究为使用人工智能方法在天然产物数据库中进行KRAS抑制剂筛选提供了新的技术路线。

Kirsten rat sarcoma viral oncogene homolog(KRAS)gene is one of the most commonly mutated oncogenes.It has been found that KRAS inhibitors have the potential therapeutic effect on cancer patients with this gene mutation.In this study,machine learning was applied to develop a QSAR(quantitative structure-activity relationship)model for KRAS small molecule inhibitors.A total of 1857data points of IC50 and SMILES(simplified molecular input line entry system)for KRAS inhibitors were collected from three databases:ChEMBL,BindingDB,and PubChem.And nine different classifiers were constructed using three different feature screening methods combined with three machine learning models,namely,random forest,support vector machine,and extreme gradient boosting machine.The results showed that the SVM model combined with mutual information feature selection exhibited the best performance:AUC_(test)=0.912,ACC_(test)=0.859,F1_(test)=0.890.Moreover,it also demonstrated good predictive performance on the external validation set(AUC_(Ext)=0.944,Recall_(Ext)=0.856,FPR_(Ext)=0.111).This study provides a new technical route for KRAS inhibitor screening in natural product databases using artificial intelligence methods.