Abstract
Deep generative models for molecular generation have accelerated the development of de novo drug design by introducing how to generate novel molecular structures expressed in simplified molecular-input line-entry system (SMILES) or molecular graph formats. Numerous drug design studies have proposed combinations of variational autoencoder (VAE) and autoregressive generators such as recurrent neural networks (RNNs) to generate SMILES strings. However, RNN-VAE has one notorious issue, called posterior collapse, in which different latent vectors produce indistinguishable molecular distributions. In this study, we proposed a Gumbel-Softmax-based generative model, MolBit, and a genetic algorithm-based molecular property optimization method. We confirmed that the proposed model avoided the posterior collapse problem and outperformed the existing drug design models with SMILES.
Original language | English |
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Title of host publication | Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 |
Editors | Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 364-367 |
Number of pages | 4 |
ISBN (Electronic) | 9781665401265 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States Duration: 2021 Dec 9 → 2021 Dec 12 |
Publication series
Name | Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 |
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Conference
Conference | 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 21/12/9 → 21/12/12 |
Bibliographical note
Funding Information:This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (IITP-2017-0-00477, (SW starlab) Research and development of the high performance in-memory distributed DBMS based on flash memory storage in IoT environment). 978-1-6654-0126-5/21/$31.00 ©2021 IEEE
Publisher Copyright:
© 2021 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
- Biomedical Engineering
- Health Informatics
- Information Systems and Management