Journal Article

Toxicity Prediction Method Based on Multi-Channel Convolutional Neural Network

Li, Zhen, Yuan, Qing, Wei, Zhiqiang, Zhang, Shugang, Guan, Xu, Wang, Shuang, Jiang, Mingjian

2019

Overview

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.

• Publisher: MDPI AG,MDPI
• Subject: Algorithms / Artificial intelligence / Binding sites / convolutional neural networks / Data Interpretation, Statistical / Datasets / Decision trees / Deep Learning / Discriminant analysis / Experiments / Machine learning / Molecular Structure / Neural networks / Neural Networks, Computer / Organic chemicals / Performance evaluation / Quantitative Structure-Activity Relationship / Support vector machines / Tox21 / Toxicity / toxicity prediction