ArticleNature communications2024
Inverse mapping of quantum properties to structures for chemical space of small organic molecules.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction.Digital discovery · 2026Article
- Artificial intelligence-driven approaches for materials design and discovery.Nature materials · 2026Review
- Speed up Multi-Scale Force-Field Parameter Optimization by Substituting Molecular Dynamics Calculations with a Machine Learning Surrogate Model.Chemphyschem : a European journal of chemical physics and physical chemistry · 2025Article
- Growth vs Diversity: A Time-Evolution Analysis of the Chemical Space.Journal of chemical information and modeling · 2025Article
- A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes.JACS Au · 2025Article
- Data Generation for Machine Learning Interatomic Potentials and Beyond.Chemical reviews · 2024Review
- Inverse mapping of quantum properties to structures for chemical space of small organic molecules.Nature communications · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.