College of Information Science and Engineering, Ocean University of China, Qingdao, China.
Molecules. 2019 Sep 17;24(18):3383. doi: 10.3390/molecules24183383.
Molecular toxicity prediction is one of the key studies in drug design. In this paper, a deep learning network based on a two-dimension grid of molecules is proposed to predict toxicity. At first, the van der Waals force and hydrogen bond were calculated according to different descriptors of molecules, and multi-channel grids were generated, which could discover more detail and helpful molecular information for toxicity prediction. The generated grids were fed into a convolutional neural network to obtain the result. A Tox21 dataset was used for the evaluation. This dataset contains more than 12,000 molecules. It can be seen from the experiment that the proposed method performs better compared to other traditional deep learning and machine learning methods.
分子毒性预测是药物设计的关键研究之一。本文提出了一种基于分子二维网格的深度学习网络来预测毒性。首先，根据分子的不同描述符计算范德华力和氢键，并生成多通道网格，这可以发现更多细节和有助于毒性预测的分子信息。生成的网格被输入到卷积神经网络中以获得结果。使用 Tox21 数据集进行评估。该数据集包含超过 12000 个分子。从实验中可以看出，与其他传统的深度学习和机器学习方法相比，所提出的方法表现更好。
