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Adversarial deep evolutionary learning for drug design.

Sheriff Abouchekeir1, Andrew Vu1, Muhetaer Mukaidaisi1

  • 1Department of Computer Science, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, L2S 3A1, Ontario, Canada.

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|October 13, 2022
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Summary

We introduce adversarial deep evolutionary learning (ADEL) for efficient drug discovery. This machine learning approach rapidly identifies novel drug candidates by optimizing molecular structures in a learned latent space.

Keywords:
Adversarial autoencoderAdversarial deep evolutionary learningDeep evolutionary learningDrug designMulti-objective optimizationVirtual screening

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Area of Science:

  • Computational chemistry
  • Machine learning
  • Drug discovery

Background:

  • Drug design is costly and time-consuming.
  • Machine intelligence offers potential for accelerated drug candidate discovery.
  • Vast molecular structural space presents a challenge for traditional methods.

Purpose of the Study:

  • To propose a novel approach, adversarial deep evolutionary learning (ADEL), for efficient drug candidate discovery.
  • To leverage machine intelligence for smart search within molecular structural space.
  • To improve the latent representation space of generative models.

Main Methods:

  • Developed and trained a custom adversarial autoencoder (AAE) model within a deep evolutionary learning (DEL) framework.
  • Integrated multi-objective evolutionary optimization in the continuous latent space of the AAE.
  • Utilized the AAE to set the latent representation space to an arbitrary distribution for novel sample generation.
  • Implemented a continuous learning process where newly generated high-quality samples augment the dataset.

Main Results:

  • ADEL demonstrated improved property distributions compared to previous deep evolutionary learning methods.
  • The approach successfully generated high-quality molecular structures.
  • The generated molecules are suitable for subsequent virtual and experimental screening.

Conclusions:

  • ADEL offers an effective strategy for accelerating the discovery of novel therapeutic agents.
  • The combination of evolving data and continuous learning enhances both the generative model and the dataset.
  • This method facilitates the design of high-quality molecular structures for drug development.