Journal
MOLECULES
Volume 24, Issue 18, Pages -Publisher
MDPI
DOI: 10.3390/molecules24183383
Keywords
deep learning; Tox21; toxicity prediction; convolutional neural networks
Funding
- National Nature Science Foundation of China [61602430]
- Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) [2018SDKJ0402]
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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.
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