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Combining Multi-objective Evolutionary Algorithms with Deep Generative Models Towards Focused Molecular Design

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Applications of Evolutionary Computation (EvoApplications 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12694))

Abstract

Recent advances in applying deep generative learning to molecular design have led to a large number of novel approaches to the targeted generation of molecules towards specific features and applications. In this work, we expand on the latent space navigation approach, where molecules are optimized by operating in their latent representation inside a deep auto-encoder, by introducing multi-objective evolutionary algorithms (MOEAs), and benchmarking the proposed framework on several objectives from recent literature. Using several case studies from literature, we show that our proposed method is capable of controlling abstract chemical properties, is competitive with other state-of-the-art methods and can perform relevant tasks such as optimizing a predefined molecule while maintaining a similarity threshold. Also, MOEAs allow to generate molecules with a good level of diversity, which is a desired feature.

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme (grant agreement number 814408).

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Authors and Affiliations

  1. Centre of Biological Engineering, University of Minho - Campus Gualtar, Braga, Portugal

    Tiago Sousa, João Correia, Vitor Pereira & Miguel Rocha

Authors
  1. Tiago Sousa
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  2. João Correia
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  3. Vitor Pereira
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  4. Miguel Rocha
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Corresponding author

Correspondence to Vitor Pereira .

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Editors and Affiliations

  1. ETSIIT-CITIC, University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

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Sousa, T., Correia, J., Pereira, V., Rocha, M. (2021). Combining Multi-objective Evolutionary Algorithms with Deep Generative Models Towards Focused Molecular Design. In: Castillo, P.A., Jiménez Laredo, J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science(), vol 12694. Springer, Cham. https://doi.org/10.1007/978-3-030-72699-7_6

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  • DOI: https://doi.org/10.1007/978-3-030-72699-7_6

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  • Print ISBN: 978-3-030-72698-0

  • Online ISBN: 978-3-030-72699-7

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