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SpotGAN: A Reverse-Transformer GAN Generates Scaffold-Constrained Molecules with Property Optimization

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Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14169))

Abstract

Generating molecules with a given scaffold is a challenging task in drug-discovery. Scaffolds impose strict constraints on the generation of molecules. Moreover, the order of the simplified molecular-input line-entry system (SMILES) strings changes substantially during sequence expansion. This study presents a scaffold-constrained, property-optimized transformer GAN (SpotGAN) to solve these issues. SpotGAN employs a decoration generator that fills decorations into a given scaffold using a transformer-decoder variant. The discriminator is a transformer-encoder variant with a global receptive field that improves the realism of the generated molecules. The chemical properties are optimized through reinforcement learning (RL), affording molecules with high property scores. Additionally, an extension of SpotGAN, called SpotWGAN, is proposed to optimize and stabilize the training process leveraging the Wasserstein distance and mini-batch discrimination. Experimental results show the usefulness of the proposed model on scaffold-constrained molecular-generation tasks in terms of the drug-likeness, solubility, synthesizability, and bioactivity of the generated molecules(\(^1\) Our code is available at: https://github.com/naruto7283/SpotGAN).

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Notes

  1. 1.

    Appendices are available at https://yamanishi.cs.i.nagoya-u.ac.jp/spotgan/.

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Acknowledgements

This research was supported by the International Research Fellow of Japan Society for the Promotion of Science (Postdoctoral Fellowships for Research in Japan [Standard]), AMED under Grant Number JP21nk0101111, and JSPS KAKENHI [grant number 20H05797]. The authors are grateful to Dr. Kazuma Kaitoh for fruitful discussion.

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Authors and Affiliations

  1. Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, 464-8602, Japan

    Chen Li & Yoshihiro Yamanishi

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  1. Chen Li
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  2. Yoshihiro Yamanishi
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Correspondence to Chen Li .

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Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Danai Koutra

  2. University of Vienna, Vienna, Austria

    Claudia Plant

  3. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  4. Politecnico di Torino, Turin, Italy

    Elena Baralis

  5. CENTAI, Turin, Italy

    Francesco Bonchi

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Li, C., Yamanishi, Y. (2023). SpotGAN: A Reverse-Transformer GAN Generates Scaffold-Constrained Molecules with Property Optimization. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14169. Springer, Cham. https://doi.org/10.1007/978-3-031-43412-9_19

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  • DOI: https://doi.org/10.1007/978-3-031-43412-9_19

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