生成语法
计算机科学
化学空间
生成模型
计算生物学
约束(计算机辅助设计)
药物发现
相似性(几何)
虚拟筛选
人工智能
对接(动物)
化学
生物系统
生物
数学
生物化学
医学
图像(数学)
护理部
几何学
作者
Mingyuan Xu,Ting Ran,Hongming Chen
标识
DOI:10.1021/acs.jcim.0c01494
摘要
De novo molecule design through the molecular generative model has gained increasing attention in recent years. Here, a novel generative model was proposed by integrating the three-dimensional (3D) structural information of the protein binding pocket into the conditional RNN (cRNN) model to control the generation of drug-like molecules. In this model, the composition of the protein binding pocket is effectively characterized through a coarse-grain strategy and the 3D information of the pocket can be represented by the sorted eigenvalues of the Coulomb matrix (EGCM) of the coarse-grained atoms composing the binding pocket. In current work, we used our EGCM method and a previously reported binding pocket descriptor, DeeplyTough, to train cRNN models and evaluated their performance. It has been shown that the model trained with the constraint of protein environment information has a clear tendency on generating compounds with higher similarity to the original X-ray-bound ligand than the normal RNN model and also better docking scores. Our results demonstrate the potential application of the controlled generative model for the targeted molecule generation and guided exploration on the drug-like chemical space.
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