GPCRVS 上线：结合深度学习与梯度提升检测靶点选择性分子骨架
GPCRVS in the detection of target-selective molecular scaffolds based on deep learning and gradient boosting
GPCRVS 是一个面向 GPCR 的虚拟筛选网络服务，用于识别具有靶点选择性的类药化合物，结合基于深度学习和梯度提升的配体方法以及多靶点、基于结构的虚拟筛选。在二分类任务（GPCR 配体与非 GPCR 配体）中 F1 为 0.875；多分类预测药物靶点和靶点类别时精确率分别为 0.421 和 0.914。对 ChEMBL 数据稀疏的受体（如活性态 CCRL2、CXCR5、CCR6、CCR7、CCR8、CCR9、CCR10 及非活性态 CCR7），作者用基于受体序列同源性的混合训练集训练模型，例如 CCR8 活性态模型准确率为 85.5%。
来源：Bioinformatics Advances · doi.org
Motivation
High drug selectivity minimizes unintended interactions that cause adverse effects, decreases drug potency, and are failure-prone factors in many clinical trials due to a poor balance between drug efficacy and toxicity. Sharing similar structural folds and modes of activation, G protein-coupled receptors may represent difficult drug targets in terms of optimization of pharmacodynamic profiles towards drug target selectivity. Here, we describe GPCRVS, a web service for GPCR virtual screening, designed to detect selective drug-like compounds. GPCRVS combines machine learning for ligand-based approaches using deep learning and gradient boosting, together with multi-target, structure-based virtual screening.
Results
GPCRVS performed well in binary classification (GPCR ligand vs non-GPCR ligand) with an F1 score of 0.875 and in multiclass classification for predicting drug target and drug target class with precision of 0.421 and 0.914, respectively, demonstrating its ability to detect active compounds with improved pharmacological characteristics. For receptors with sparse ChEMBL datasets such as active-state CCRL2, CXCR5, CCR6, CCR7, CCR8, CCR9, CCR10, and inactive-state CCR7, mixed training datasets based on receptor sequence homology were used for the ML model training with resulting accuracy of 85.5% obtained, e.g., for the CCR8-active ML model. In addition, GPCRVS detected target-selective, distinct molecular scaffolds with the LightGBM module such as fluorinated fragments in CCR6 inhibitors, or nitrogen-rich heterocyclic fragments in CXCR3 inhibitors.
Availability and implementation
GPCRVS is available on: https://gpcrvs.chem.uw.edu.pl.