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Deep Learning for Toxicity and Disease Prediction

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Front. Genet. | doi: 10.3389/fgene.2018.00585

Prediction of Druglikeness Using Deep Autoencoder Neural Networks

 Qiwan Hu1, Mudong Feng1,  Luhua Lai1 and  Jianfeng Pei1*
  • 1Peking University, China

Due to diverse reasons, most drug candidates can not eventually become marketed drugs. Developing reliable computational methods for prediction of druglikeness of candidate compounds is of vital importance to improve the success rate of drug discovery and development. In this study, we used deep autoencoder neural networks to construct druglikeness classification models. We collected datasets of drugs (represented by ZINC World Drug), bioactive molecules (represented by MDDR and WDI), and common molecules (represented by ZINC All Purchasable and ACD). Compounds were encoded with MOLD2 two-dimensional structure descriptors. The classification accuracies of druglike/non-druglike model are 91.04% on WDI/ACD databases, and 91.20% on MDDR/ZINC, respectively. The performance of the models outperforms previously reported models. In addition, we develop a drug/non-druglike model (ZINC World Drug vs ZINC All Purchasable), which distinguishes drugs and common compounds, with a classification accuracy of 96.99%. Our work shows that by using high-latitude molecular descriptors, we can apply deep learning technology to establish state-of-the-art druglikeness prediction models.

Keywords: Druglikeness, Zinc, MDDR, Deep leaning, Auto-Encoder (AE)

Received: 31 Aug 2018; Accepted: 09 Nov 2018.

Edited by:

Ping Gong, Engineer Research and Development Center (ERDC), United States

Reviewed by:

Yun Tang, East China University of Science and Technology, China
Shengyong Yang, Sichuan University, China  

Copyright: © 2018 Hu, Feng, Lai and Pei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: PhD. Jianfeng Pei, Peking University, Beijing, 100871, Beijing Municipality, China,