GPCRLigNet: rapid screening for GPCR active ligands using machine learning

Remington Jacob M.; McKay Kyle T.; Beckage Noah B.Ferrell Jonathon B.Schneebeli Severin T. T.Li Jianing

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GPCRLigNet: rapid screening for GPCR active ligands using machine learning

机译：GPCRLigNet：使用机器学习快速筛选 GPCR 活性配体

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Molecules with bioactivity towards G protein-coupled receptors represent a subset of the vast space of small drug-like molecules. Here, we compare machine learning models, including dilated graph convolutional networks, that conduct binary classification to quickly identify molecules with activity towards G protein-coupled receptors. The models are trained and validated using a large set of over 600,000 active, inactive, and decoy compounds. The best performing machine learning model, dubbed GPCRLigNet, was a surprisingly simple feedforward dense neural network mapping from Morgan fingerprints to activity. Incorporation of GPCRLigNet into a high-throughput virtual screening workflow is demonstrated with molecular docking towards a particular G protein-coupled receptor, the pituitary adenylate cyclase-activating polypeptide receptor type 1. Through rigorous comparison of docking scores for molecules selected with and without using GPCRLigNet, we demonstrate an enrichment of potentially potent molecules using GPCRLigNet. This work provides a proof of principle that GPCRLigNet can effectively hone the chemical search space towards ligands with G protein-coupled receptor activity.

机译：对G蛋白偶联受体具有生物活性的分子代表了小药物样分子的广阔空间的一个子集。在这里，我们比较了机器学习模型，包括膨胀图卷积网络，这些模型进行二元分类以快速识别对G蛋白偶联受体具有活性的分子。这些模型使用超过 600,000 种活性、非活性和诱饵化合物进行训练和验证。性能最好的机器学习模型，被称为GPCRLigNet，是一个令人惊讶的简单前馈密集神经网络映射，从摩根指纹到活动。将GPCRLigNet整合到高通量虚拟筛选工作流程中，证明了分子对接到特定的G蛋白偶联受体，垂体腺苷酸环化酶激活多肽受体1型。通过严格比较使用和不使用GPCRLigNet选择的分子的对接分数，我们证明了使用GPCRLigNet富集潜在有效分子。这项工作证明了GPCRLigNet可以有效地将化学搜索空间磨练到具有G蛋白偶联受体活性的配体。

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来源
《Journal of Computer-Aided Molecular Design》 |2023年第3期|147-156|共10页
作者
Remington Jacob M.; McKay Kyle T.; Beckage Noah B.Ferrell Jonathon B.Schneebeli Severin T. T.Li Jianing;
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作者单位

Univ Vermont;

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原文格式 PDF
正文语种英语
中图分类电子计算机在化学中的应用;结构化学;
关键词
G protein-coupled receptor; Ligand; Machine Learning; Neural Network; Molecular Fingerprint; Molecular Docking; TRANSMEMBRANE BINDING POCKETS; DRUG DISCOVERY; DESIGN; DATABASE;

机译：G蛋白偶联受体;配体;机器学习;神经网络;分子指纹图谱;分子对接;跨膜结合袋;药物发现;设计;数据库;

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GPCRLigNet: rapid screening for GPCR active ligands using machine learning

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