Mining Patents with Large Language Models Elucidates the Chemical Function Landscape.

ABSTRACT: The fundamental goal of small molecule discovery is to generate chemicals with target functionality. While this often proceeds through structure-based methods, we set out to investigate the practicality of orthogonal methods that leverage the extensive corpus of chemical literature. We hypothesize that a sufficiently large text-derived chemical function dataset would mirror the actual landscape of chemical functionality. Such a landscape would implicitly capture complex physical and biological interactions given that chemical function arises from both a molecule's structure and its interacting partners. To evaluate this hypothesis, we built a Chemical Function (CheF) dataset of patent-derived functional labels. This dataset, comprising 631K molecule-function pairs, was created using an LLM

SUBMITTER: Kosonocky CW 

PROVIDER: S-EPMC10775343 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature