Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence.
Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence.
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DOI：
10.3390/biom11060870
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发表时间：
2021-06-11
期刊：
影响因子：
5.5
通讯作者：
Xie XQ
中科院分区：
文献类型：
--
作者：
Hou T;Bian Y;McGuire T;Xie XQ
G蛋白偶联受体（GPCR）是细胞表面受体中最大和最多样化的一组，对各种细胞外信号做出反应。近年来，GPCR的变构调节已成为开发靶点选择性治疗的有希望的方法。此外，新的GPCR变构调节剂的发现将有助于进一步了解GPCR细胞信号转导机制。以高效和有效的方式准确区分不同GPCR基团的调节剂是关键的，但也是具有挑战性的。在这项研究中，我们专注于11类分类任务，10个GPCR亚型类和一个随机的化合物类。我们使用了包含34，434种化合物的数据集，所述化合物具有从经典GPCR家族A、B和C收集的变构调节剂，以及随机药物样化合物。六种类型的机器学习模型，包括支持向量机，朴素贝叶斯，决策树，随机森林，逻辑回归和多层感知器，使用不同的特征组合进行训练，包括分子描述符，原子对指纹，MACCS指纹和ECFP 6指纹。对具有不同特征组合的训练机器学习模型的性能进行了仔细的研究和讨论。据我们所知，这是GPCR变构调节剂的多类分类的第一项工作。我们相信，在这项研究中开发的分类模型可以作为简单和准确的工具，发现和开发GPCR变构调节剂。
G-protein-coupled receptors (GPCRs) are the largest and most diverse group of cell surface receptors that respond to various extracellular signals. The allosteric modulation of GPCRs has emerged in recent years as a promising approach for developing target-selective therapies. Moreover, the discovery of new GPCR allosteric modulators can greatly benefit the further understanding of GPCR cell signaling mechanisms. It is critical but also challenging to make an accurate distinction of modulators for different GPCR groups in an efficient and effective manner. In this study, we focus on an 11-class classification task with 10 GPCR subtype classes and a random compounds class. We used a dataset containing 34,434 compounds with allosteric modulators collected from classical GPCR families A, B, and C, as well as random drug-like compounds. Six types of machine learning models, including support vector machine, naïve Bayes, decision tree, random forest, logistic regression, and multilayer perceptron, were trained using different combinations of features including molecular descriptors, Atom-pair fingerprints, MACCS fingerprints, and ECFP6 fingerprints. The performances of trained machine learning models with different feature combinations were closely investigated and discussed. To the best of our knowledge, this is the first work on the multi-class classification of GPCR allosteric modulators. We believe that the classification models developed in this study can be used as simple and accurate tools for the discovery and development of GPCR allosteric modulators.
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DOI：
10.1021/ci010132r
发表时间：
2002-11-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Durant, JL;Leland, BA;Nourse, JG
通讯作者：
Nourse, JG
影响因子：
62.1
作者：
Lindsley CW;Emmitte KA;Hopkins CR;Bridges TM;Gregory KJ;Niswender CM;Conn PJ
通讯作者：
Conn PJ
影响因子：
5.6
作者：
Ma C;Wang L;Yang P;Myint KZ;Xie XQ
通讯作者：
Xie XQ
DOI：
10.1038/nrd.2017.178
发表时间：
2017-12
期刊：
Nature reviews. Drug discovery
影响因子：
--
作者：
Hauser AS;Attwood MM;Rask-Andersen M;Schiöth HB;Gloriam DE
通讯作者：
Gloriam DE
DOI：
10.1038/nrd4308
发表时间：
2014-09
期刊：
Nature reviews. Drug discovery
影响因子：
--
作者：
通讯作者：
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