Reverse engineering molecules from fingerprints through deterministic enumeration and generative models.

ABSTRACT: Reverse engineering in molecular design aims to identify optimal structures based on activities, or properties, computed through molecular descriptors like fingerprints. This task is known to be particularly difficult for the widely used Extended-Connectivity Fingerprints (ECFPs), due to significant loss of structural information during vectorization. While recent artificial intelligence-based works have raised awareness about the privacy risks associated with ECFP-based data sharing, we contribute a more conclusive demonstration by introducing a deterministic algorithm that reconstructs molecular structures from ECFPs. Using MetaNetX and eMolecules as databases of natural compounds and commercially available chemicals, the deterministic algorithm benchmarks a Transformer-based generative

SUBMITTER: Meyer P 

PROVIDER: S-EPMC12522240 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature