基于机器学习方法的JAK1抑制剂分类及SAR研究

Zhenwu Yang , Yujia Tian , Yue Kong , Yushan Zhu , Aixia Yan
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引用次数: 0

摘要

Janus kinase 1 (JAK1)是基因转录的关键调控因子,抑制JAK1可以干预包括类风湿关节炎和克罗恩病在内的许多疾病。在这项研究中,我们收集了包含2982个JAK1抑制剂的数据集,用MACCS指纹和Morgan指纹对分子进行了表征。采用支持向量机(SVM)、决策树(DT)、随机森林(RF)和极端梯度增强树(XGBoost)算法构建了16个传统的机器学习分类模型。此外,我们利用深度神经网络(DNN)开发了四个深度学习模型。采用RF指纹和Morgan指纹构建的最佳模型(model 3B)在测试集上的准确率(ACC)为93.6%,Mathews相关系数(MCC)为0.87。此外,基于随机森林模型的输出,我们对JAK1抑制剂进行了结构-活性关系(SAR)分析。通过分析两类指纹图谱的重要键,发现高活性JAK1抑制剂中频繁出现吡唑、吡咯三唑嘧啶和吡唑嘧啶等亚结构。
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Classification of JAK1 Inhibitors and SAR Research by Machine Learning Methods

Janus kinase 1 (JAK1) is a key regulator of gene transcription, inhibition of JAK1 is an intervention for many diseases including rheumatoid arthritis and Crohn's disease. In this study, we collected a dataset containing 2982 JAK1 inhibitors, characterized molecules by MACCS fingerprints and Morgan fingerprints. We used support vector machine (SVM), decision tree (DT), random forest (RF) and extreme gradient boosting tree (XGBoost) algorithms to build 16 traditional machine learning classification models. Additionally, we utilized deep neural networks (DNN) to develop four deep learning models. The best model (Model 3B) built by RF and Morgan fingerprints achieved the accuracy (ACC) of 93.6% and Mathews correlation coefficient (MCC) of 0.87 on the test set. Furthermore, we made structure–activity relationship (SAR) analyses for JAK1 inhibitors, based on the output from the random forest models. After analyzing the important keys of two types of fingerprints, it was observed that some substructures such as pyrazole, pyrrolotriazolopyrimidine and pyrazolopyrimidine appeared frequently in highly active JAK1 inhibitors.

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来源期刊
Artificial intelligence in the life sciences
Artificial intelligence in the life sciences Pharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
CiteScore
5.00
自引率
0.00%
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0
审稿时长
15 days
期刊最新文献
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