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Inverse mapping of quantum properties to structures for chemical space of small organic molecules

Author

Listed:
  • Alessio Fallani

    (University of Luxembourg)

  • Leonardo Medrano Sandonas

    (University of Luxembourg
    TU Dresden)

  • Alexandre Tkatchenko

    (University of Luxembourg)

Abstract

Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify chemical compounds with tailored properties. While quantum-mechanical (QM) methods, coupled with machine learning, already offer a direct mapping from 3D molecular structures to their properties, effective methodologies for the inverse mapping in chemical space remain elusive. We address this challenge by demonstrating the possibility of parametrizing a chemical space with a finite set of QM properties. Our proof-of-concept implementation achieves an approximate property-to-structure mapping, the QIM model (which stands for “Quantum Inverse Mapping”), by forcing a variational auto-encoder with a property encoder to obtain a common internal representation for both structures and properties. After validating this mapping for small drug-like molecules, we illustrate its capabilities with an explainability study as well as by the generation of de novo molecular structures with targeted properties and transition pathways between conformational isomers. Our findings thus provide a proof-of-principle demonstration aiming to enable the inverse property-to-structure design in diverse chemical spaces.

Suggested Citation

  • Alessio Fallani & Leonardo Medrano Sandonas & Alexandre Tkatchenko, 2024. "Inverse mapping of quantum properties to structures for chemical space of small organic molecules," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50401-1
    DOI: 10.1038/s41467-024-50401-1
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    References listed on IDEAS

    as
    1. Kristof T. Schütt & Farhad Arbabzadah & Stefan Chmiela & Klaus R. Müller & Alexandre Tkatchenko, 2017. "Quantum-chemical insights from deep tensor neural networks," Nature Communications, Nature, vol. 8(1), pages 1-8, April.
    2. Niklas W. A. Gebauer & Michael Gastegger & Stefaan S. P. Hessmann & Klaus-Robert Müller & Kristof T. Schütt, 2022. "Inverse design of 3d molecular structures with conditional generative neural networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
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    4. Michael Moret & Irene Pachon Angona & Leandro Cotos & Shen Yan & Kenneth Atz & Cyrill Brunner & Martin Baumgartner & Francesca Grisoni & Gisbert Schneider, 2023. "Leveraging molecular structure and bioactivity with chemical language models for de novo drug design," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Anastasiia V. Sadybekov & Vsevolod Katritch, 2023. "Computational approaches streamlining drug discovery," Nature, Nature, vol. 616(7958), pages 673-685, April.
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