De Novo Molecular Design of Caspase-6 Inhibitors by GRU-Based Recurrent Neural Network Combined with Transfer Learning Approach release_3itx7voiqncz3oyh4m3xkmupfa

by Shuheng Huang, Hu Mei, LaiChun Lu, Tingting Shi, Linxin Chen, Zuyin Kuang, Yu Heng, Lei Xu, Xianchao Pan

Released as a post by Research Square.

(2020)

Abstract

<jats:title>Abstract</jats:title> Due to the potencies in the treatments of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attentions. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel caspase-6 candidate inhibitors. Herein, gated recurrent unit (GRU)-based recurrent neural network (RNN) combined with transfer learning was used to build the molecular generative model of caspase-6 inhibitors. The results showed that the GRU-based RNN model can learn accurately the SMILES grammars of about 2.4 million chemical molecules including ionic and isomeric compounds, and can generate potential caspase-6 inhibitors after transfer learning of the known 433 caspase-6 inhibitors. Based on the novel molecules derived from the molecular generative model, an optimal machine learning model and Surflex-dock were further employed for predicting and ranking the inhibitory activities. Three potential caspase-6 inhibitors with different scaffolds were selected as the most promising candidates for further researches. In general, this paper provides an efficient combinational strategy for de novo molecular design of caspase-6 inhibitors.
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