药物重新定位
卷积神经网络
计算机科学
药物发现
重新调整用途
人工智能
结合位点
集合(抽象数据类型)
相似性(几何)
机器学习
人工神经网络
深度学习
GSM演进的增强数据速率
计算生物学
数据挖掘
模式识别(心理学)
生物信息学
化学
生物
药品
生态学
生物化学
图像(数学)
药理学
程序设计语言
作者
Oliver B. Scott,Jing Gu,A. W. Edith Chan
标识
DOI:10.1021/acs.jcim.2c00832
摘要
The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein-ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method's promise for lead hopping within or outside a protein target, directly based on binding site information.
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