Research on machine learning-based activity prediction models for KRAS inhibitors
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摘要:
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：AUCtest=0.912，ACCtest=0.859，F1test=0.890，并且在外部验证集上也表现出良好的预测性能(AUCExt=0.944，RecallExt=0.856，FPRExt=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。
Abstract: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: AUCtest=0.912, ACCtest=0.859, F1test=0.890. Moreover, it also demonstrated good predictive performance on the external validation set(AUCExt=0.944, RecallExt=0.856, FPRExt=0.111). This study provides a new technical route for KRAS inhibitor screening in natural product databases using artificial intelligence methods.
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Figure 2. Workflow of KRAS inhibitors activity prediction based on machine learning
ECFP4: Extended connectivity fingerprints; MACCS: Molecular access system; RF: Random forest; SVM: Support vector machine; XGBoost: Extreme gradient boosting; MI:Mutual information
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Figure 3. Schematic diagram of the QSAR model
SMILES: Simplified molecular input line entry system; QSAR: Quantitative structure-activity relationship
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Figure 4. Distribution of pIC50 values for KRAS inhibitors
A: Scatter plot of all compound pIC50 values; B: Histogram of pIC50 values distribution
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Figure 5. Spatial distributions of training set and test set
A: Two-dimensional results presentation; B: Three-dimensional results presentation
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Figure 7. Features selected by RF, SVM, and XGBoost respectively by five-fold cross-validation
A: Optimal number of features selected by MI; B: Optimal number of dimensions reduced by PCA
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Figure 8. Probability distributions of different metrics on different models and corresponding mean line charts
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Figure 9. Receiver operating characteristic (ROC) curves for 9 models under five-fold cross-validation
A:No screening of features;B:Screening of features based on MI;C: Dimensionality reduction of features by PCA
Table 1 Sample statistics for training and test sets
Data type Positive samples Negative samples Sum Ratio (+/–) Training set 990 495 1485 2.0 Test set 248 124 372 2.0 Total 1238 619 1857 2.0 
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Table 2 Training and test set results for 9 models
Features Model CV5 Training Test AUC ACC F1 AUC ACC F1 AUC ACC F1 None RF 0.890 0.837 0.882 1.000 0.990 0.992 0.900 0.849 0.894 SVM 0.890 0.850 0.888 0.999 0.974 0.981 0.900 0.847 0.888 XGBoost 0.890 0.829 0.874 0.999 0.987 0.990 0.892 0.833 0.881 MI RF 0.892 0.833 0.879 1.000 0.995 0.996 0.903 0.852 0.895 SVM 0.891 0.835 0.877 0.990 0.950 0.962 0.912 0.859 0.890 XGBoost 0.892 0.829 0.873 0.999 0.984 0.988 0.897 0.831 0.876 PCA RF 0.885 0.832 0.879 1.000 0.997 0.997 0.897 0.831 0.882 SVM 0.886 0.839 0.880 0.993 0.953 0.965 0.908 0.852 0.893 XGBoost 0.883 0.833 0.877 1.000 0.995 0.996 0.895 0.823 0.872
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