Mining Patents with Large Language Models Elucidates the Chemical Function Landscape

TL;DR

This paper introduces a large dataset of chemical functions derived from patents using large language models, demonstrating its potential to identify drugs and explore chemical functionalities beyond traditional structure-based methods.

Contribution

The study creates and validates a large, text-derived chemical function dataset (CheF) that captures the functional landscape of molecules, enabling functional predictions from structure.

Findings

CheF dataset contains 631K molecule-function pairs.

The dataset reflects a coherent semantic landscape aligned with chemical structure.

Functional landscape can predict target functionality from molecular structure.

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- and embedding-based method to obtain functional labels for approximately 100K molecules from their corresponding 188K unique patents. We carry out a series of analyses demonstrating that the CheF dataset contains a semantically coherent textual representation of the functional landscape congruent with chemical structural relationships, thus approximating the actual chemical function landscape. We then demonstrate that this text-based functional landscape can be leveraged to identify drugs with target functionality using a model able to predict functional profiles from structure alone. We believe that functional label-guided molecular discovery may serve as an orthogonal approach to traditional structure-based methods in the pursuit of designing novel functional molecules.