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

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 structur

SUBMITTER: Fallani A 

PROVIDER: S-EPMC11258234 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature