Activity Prediction of Small Molecule Inhibitors for Antirheumatoid Arthritis Targets Based on Artificial Intelligence,ACS Combinatorial Science

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Activity Prediction of Small Molecule Inhibitors for Antirheumatoid Arthritis Targets Based on Artificial Intelligence
ACS Combinatorial Science Pub Date : 2020-11-04 , DOI: 10.1021/acscombsci.0c00169
Guomeng Xing ₁ , Li Liang ₁ , Chenglong Deng ₁ , Yi Hua ₁ , Xingye Chen ₁ , Yan Yang ₁ , Haichun Liu ₁ , Tao Lu _{1,

2} , Yadong Chen ₁ , Yanmin Zhang ₁

Affiliation

Rheumatoid arthritis (RA) is a chronic autoimmune disease, which is compared to “immortal cancer” in industry. Currently, SYK, BTK, and JAK are the three major targets of protein tyrosine kinase for this disease. According to existing research, marketed and research drugs for RA are mostly based on single target, which limits their efficacy. Therefore, designing multitarget or dual-target inhibitors provide new insights for the treatment of RA regarding of the specific association between SYK, BTK, and JAK from two signal transduction pathways. In this study, machine learning (XGBoost, SVM) and deep learning (DNN) models were combined for the first time to build a powerful integrated model for SYK, BTK, and JAK. The predictive power of the integrated model was proved to be superior to that of a single classifier. In order to accurately assess the generalization ability of the integrated model, comprehensive similarity analysis was performed on the training and the test set, and the prediction accuracy of the integrated model was specifically analyzed under different similarity thresholds. External validation was conducted using single-target and dual-target inhibitors, respectively. Results showed that our model not only obtained a high recall rate (97%) in single-target prediction, but also achieved a favorable yield (54.4%) in dual-target prediction. Furthermore, by clustering dual-target inhibitors, the prediction performance of model in various classes were proved, evaluating the applicability domain of the model in the dual-target drug screening. In summary, the integrated model proposed is promising to screen dual-target inhibitors of SYK/JAK or BTK/JAK as RA drugs, which is beneficial for the clinical treatment of rheumatoid arthritis.

中文翻译：

基于人工智能的抗风湿性关节炎靶点小分子抑制剂活性预测

类风湿性关节炎（RA）是一种慢性自身免疫性疾病，在工业上被比作“不死的癌症”。目前，SYK、BTK和JAK是该病蛋白酪氨酸激酶的三大靶点。根据现有研究，已上市和研究的类风湿性关节炎药物大多基于单一靶点，这限制了其疗效。因此，设计多靶点或双靶点抑制剂为治疗 RA 提供了新的见解，即 SYK、BTK 和 JAK 之间来自两种信号转导途径的特定关联。本研究首次将机器学习（XGBoost、SVM）和深度学习（DNN）模型结合起来，构建了强大的SYK、BTK、JAK综合模型。集成模型的预测能力被证明优于单个分类器的预测能力。为了准确评估集成模型的泛化能力，对训练集和测试集进行了综合相似度分析，具体分析了不同相似度阈值下集成模型的预测精度。分别使用单靶点和双靶点抑制剂进行外部验证。结果表明，我们的模型不仅在单目标预测中获得了高召回率（97%），而且在双目标预测中也获得了良好的收益率（54.4%）。此外，通过聚类双靶点抑制剂，证明了模型在各类别中的预测性能，评估了模型在双靶点药物筛选中的适用范围。总之，

更新日期：2020-12-14

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