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

Pharmaceuticals

Xianchao Pan¹  Hu Mei²  Lei Xu²  Zuyin Kuang²  Minyao Qiu²  Laichun Lu²  Shuheng Huang²  Xiaoqi Liang²  Yu Heng² 

[1] Department of Medicinal Chemistry, School of Pharmacy, Southwest Medical University, Luzhou 646000, China;Key Laboratory of Biorheological Science and Technology (Ministry of Education), College of Bioengineering, Chongqing University, Chongqing 400044, China;

关键词: gated recurrent unit;    recurrent neural network;    machine learning;    transfer learning;    caspase-6;    inhibitor;   

DOI  :  10.3390/ph14121249

来源: DOAJ

【 摘 要 】

Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. 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, a gated recurrent unit (GRU)-based recurrent neural network (RNN) combined with transfer learning was used to build a molecular generative model of caspase-6 inhibitors. The results showed that the GRU-based RNN model can accurately learn 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 logistic regression model and Surflex-dock were employed for predicting and ranking the inhibitory activities. According to the prediction results, three potential caspase-6 inhibitors with different scaffolds were selected as the promising candidates for further research. In general, this paper provides an efficient combinational strategy for de novo molecular design of caspase-6 inhibitors.

【 授权许可】

Unknown