@article{M3E8793C9,
title = "De Novo Drug Design Using Self-Attention Based Variational Autoencoder",
journal = "The Transactions of the Korea Information Processing Society",
year = "2022",
issn = "null",
doi = "https://doi.org/10.3745/KTSDE.2022.11.1.11",
author = "Piao Shengmin/Jonghwan Choi/Sangmin Seo/Kyeonghun Kim/Sanghyun Park",
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"
}