计算机科学
卷积神经网络
虚拟筛选
人工智能
分子描述符
分子动力学
分子图形学
财产(哲学)
人工神经网络
可视化
网(多面体)
网格
深度学习
模式识别(心理学)
生物系统
数量结构-活动关系
机器学习
几何学
计算机图形学
化学
计算化学
数学
认识论
生物
哲学
作者
Chunyan Li,Jianmin Wang,Zhangming Niu,Junfeng Yao,Xiangxiang Zeng
摘要
Geometry-based properties and characteristics of drug molecules play an important role in drug development for virtual screening in computational chemistry. The 3D characteristics of molecules largely determine the properties of the drug and the binding characteristics of the target. However, most of the previous studies focused on 1D or 2D molecular descriptors while ignoring the 3D topological structure, thereby degrading the performance of molecule-related prediction. Because it is very time-consuming to use dynamics to simulate molecular 3D conformer, we aim to use machine learning to represent 3D molecules by using the generated 3D molecular coordinates from the 2D structure.We proposed Drug3D-Net, a novel deep neural network architecture based on the spatial geometric structure of molecules for predicting molecular properties. It is grid-based 3D convolutional neural network with spatial-temporal gated attention module, which can extract the geometric features for molecular prediction tasks in the process of convolution. The effectiveness of Drug3D-Net is verified on the public molecular datasets. Compared with other deep learning methods, Drug3D-Net shows superior performance in predicting molecular properties and biochemical activities.https://github.com/anny0316/Drug3D-Net.Supplementary data are available online at https://academic.oup.com/bib.
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