IEEE Computational Intelligence Magazine - May 2022 - 14
Deep
Evolutionary
Learning for
Molecular
Design
Karl Grantham and Muhetaer Mukaidaisi
Brock University, CANADA
Hsu Kiang Ooi, Mohammad Sajjad Ghaemi,
and Alain Tchagang
National Research Council Canada, CANADA
Yifeng Li
Brock University, CANADA
Abstract-In this paper, a prototypical deep evolutionary
learning (DEL) process is proposed to integrate deep generative
model and multi-objective evolutionary computation for
molecular design. Our approach enables (1) evolutionary operations
in the latent space of the generative model, rather than
the structural space, to generate promising novel molecular
structures for the next evolutionary generation, and (2) generative
model fine-tuning using newly generated high-quality
samples. Thus, DEL implements a data-model co-evolution
concept which improves both sample population and generative
model learning. Experiments on public datasets indicate
Digital Object Identifier 10.1109/MCI.2022.3155308
Date of current version: 12 April 2022
14 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2022
that the sample population obtained by DEL exhibits
improvement on property distributions, and dominates samples
generated by other baseline molecular optimization algorithms.
Furthermore, comparisons with a range of deep
generative models show that DEL is beneficial for improving
sample populations.
A
I. Introduction
drug is a molecule that binds to a target (e.g., protein)
to inhibit or activate specific pathways in pathogens or
host cells that cause abnormal phenotype. Drug discovery
and development is a costly and time-consuming
process, which is compounded by personalized
medicine development for cancer or other complex and rare
diseases. Computational drug discovery has been shown to
accelerate the whole discovery process using simulations and
machine intelligence.
From our viewpoint, drug discovery can be formulated
into two problems, molecular generation and molecular
design, depending on the situational need. In a molecular
generation problem, an unsupervised (stochastic) generative
model ()xpi
parameterized by i is first learned from
Corresponding author: Yifeng Li (e-mail: yli2@brocku.ca).
1556-603X/22©2022 Canadian Crown Copyright
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IEEE Computational Intelligence Magazine - May 2022
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