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Adversarial deep evolutionary learning for drug design

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1109/CIBCB49929.2021.9562949
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Affiliation
  1. National Research Council of Canada. Digital Technologies
FormatText, Article
Conference2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Oct. 13-15, 2021, Melbourne, Australia [Held Virtually]
Physical description9 p.
Subjecttraining; drugs; sociology; machine learning; data structures; data models; statistics
Abstract
The design of a new therapeutic agent is a time-consuming and expensive process. The rise of machine intelligence provides a grand opportunity of expeditiously discovering novel drug candidates through smart search in the vast molecular structural space. In this paper, we propose a new approach called adversarial deep evolutionary learning (ADEL) to search for novel molecules in the latent space of an adversarial generative model and keep improving the latent representation space. In ADEL, a custom-made adversarial autoencoder (AAE) model is developed and trained under a deep evolutionary learning (DEL) process. This involves an initial training of the AAE model, followed by an integration of multi-objective evolutionary optimization in the continuous latent representation space of the AAE rather than the discrete structural space of molecules. By using the AAE, an arbitrary distribution can be provided to the training of AAE such that the latent representation space is set to that distribution. This allows for a starting latent space from which new samples can be produced. Throughout the process of learning, new samples of high-quality are generated after each iteration of training and then added back into the full dataset. Therefore, allowing for a more comprehensive procedure of understanding the data structure. This combination of evolving data and continuous learning not only enables improvement in the generative model, but the data as well. By comparing ADEL to the previous work in DEL, we see that ADEL can obtain better property distributions.
Publication date
PublisherIEEE
In
  • IEEE CIBCB 2021: Conference on Computational Intelligence in Bioinformatics and Computational Biology (13 October 2021).
Other version
  • Abouchekeir, Sheriff, Andrew Vu, Muhetaer Mukaidaisi, Karl Grantham, Alain Tchagang, and Yifeng Li. ‘Adversarial Deep Evolutionary Learning for Drug Design’. Biosystems 222 (December 2022): 104790. https://doi.org/10.1016/j.biosystems.2022.104790.
LanguageEnglish
Peer reviewedYes
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Record identifierf15a9f1f-f7d0-4584-aa74-e6c95dc453ea
Record created2021-11-09
Record modified2022-10-25
Date modified: