De novo molecular design of SARS-COV-2 inhibitors based on a GRU network with BPE algorithm and transfer learning

Tao Xu; Lingyun Luo; Cheng Chen

doi:10.1117/12.2687020

21 July 2023 De novo molecular design of SARS-COV-2 inhibitors based on a GRU network with BPE algorithm and transfer learning

Tao Xu, Lingyun Luo, Cheng Chen

Author Affiliations +

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127173A (2023) https://doi.org/10.1117/12.2687020
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

SARS-CoV-2 inhibitor plays an important role in COVID-19 preclinical drug discovery. As the existing SARS-COV-2 inhibitors showed more or less deficiencies, it is urgent to develop new SARS-COV-2 candidate inhibitors. De Novo Molecular Design plays a very important role in drug discovery. Most of the existing method use SMILES (Simplified Molecular Input Line Entry System) as the input of deep learning models. One popular way is utilizing deep learning models to automatically generate candidate drug molecules, and most of the existing models use SMILES as the input. In this study, we embed SMILES using a sub-word algorithm named BPE (Byte Pair Encoding) instead of One-Hot. First of all, the sub-word algorithm BPE learns a vocabulary of high frequency SMILES substrings from a large SMILES dataset, SMILES are then tokened according to the vocabulary learned by the BPE algorithm. Results show that the BPE algorithm can effectively learn the SMILES grammars and can help our generative model generate potential SARS-COV-2 inhibitors after transfer learning using the known 1253 SARS-COV-2 inhibitors. Generally, this paper provides an effectively method for de novo molecular design of SARS-COV-2 inhibitors.

Citation Download Citation

Tao Xu, Lingyun Luo, and Cheng Chen "De novo molecular design of SARS-COV-2 inhibitors based on a GRU network with BPE algorithm and transfer learning", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127173A (21 July 2023); https://doi.org/10.1117/12.2687020

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KEYWORDS

Molecules

Education and training

Machine learning

Design and modelling

Deep learning

Statistical modeling

Drug discovery

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