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

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 th...

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Vydané v:Nature communications Ročník 15; číslo 1; s. 6061 - 14
Hlavní autori: Fallani, Alessio, Medrano Sandonas, Leonardo, Tkatchenko, Alexandre
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 18.07.2024
Nature Publishing Group
Nature Portfolio
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639/638/440/94
639/638/563/606
639/705/1042
639/766/94
Artificial intelligence
Chemical compounds
Coders
Design
Design parameters
Equilibrium
Humanities and Social Sciences
Isomers
Learning algorithms
Machine learning
Mapping
Materials science
Molecular properties
Molecular structure
multidisciplinary
Organic chemistry
Science
Science (multidisciplinary)
ISSN:2041-1723, 2041-1723
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Shrnutí: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. A mapping linking a desired molecular property to a 3D structure would facilitate molecular design. Here, the authors parameterize the chemical space of small organic molecules using quantum properties via machine learning, providing insights into targeted molecular design.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-024-50401-1