Piao Shengmin, Jonghwan Choi, Sangmin Seo, Kyeonghun Kim, Sanghyun Park

KIPS Transactions on Software and Data Engineering, Vol. 11, No. 1, pp. 11-18, Jan. 2022
https://doi.org/10.3745/KTSDE.2022.11.1.11,   PDF Download:   Full Text
Keywords: De Novo Drug Design, SMILES, Deep Learning, self-attention, Variational AutoEncoder
Abstract

De novo drug design is the process of developing new drugs that can interact with biological targets such as protein receptors. Traditional process of de novo drug design consists of drug candidate discovery and drug development, but it requires a long time of more than 10 years to develop a new drug. Deep learning-based methods are being studied to shorten this period and efficiently find chemical compounds for new drug candidates. Many existing deep learning-based drug design models utilize recurrent neural networks to generate a chemical entity represented by SMILES strings, but due to the disadvantages of the recurrent networks, such as slow training speed and poor understanding of complex molecular formula rules, there is room for improvement. To overcome these shortcomings, we propose a deep learning model for SMILES string generation using variational autoencoders with self-attention mechanism. Our proposed model decreased the training time by 1/26 compared to the latest drug design model, as well as generated valid SMILES more effectively

Statistics
Cite this article