Mining patents with large language models elucidates the chemical function landscape†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-05-07 DOI:10.1039/D4DD00011K
Clayton W. Kosonocky, Claus O. Wilke, Edward M. Marcotte and Andrew D. Ellington
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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 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 631 K molecule–function pairs, was created using an LLM- and embedding-based method to obtain 1.5 K unique functional labels for approximately 100 K randomly selected molecules from their corresponding 188 K 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 through several examples 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 alternative approach to traditional structure-based methods in the pursuit of designing novel functional molecules.

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利用大型语言模型挖掘专利,阐明化学功能格局
小分子发现的基本目标是产生具有目标功能的化学物质。虽然这通常是通过基于结构的方法实现的,但我们着手研究利用大量化学文献的方法的实用性。我们假设,一个足够大的文本化学功能数据集将反映化学功能的实际情况。鉴于化学功能源于分子的结构及其相互作用的伙伴,这样的景观将隐含地捕捉到复杂的物理和生物相互作用。为了评估这一假设,我们建立了一个化学功能(CheF)数据集,其中包含来自专利的功能标签。该数据集由 631K 个分子-功能对组成,采用基于 LLM 和嵌入的方法创建,从相应的 188K 个唯一专利中随机选取约 100K 个分子,获得 1.5K 个唯一的功能标签。我们进行的一系列分析表明,CheF 数据集包含与化学结构关系一致的功能图谱的语义连贯文本表示,因此近似于实际的化学功能图谱。然后,我们通过几个例子证明,可以利用这种基于文本的功能图谱,通过一个能够仅从结构预测功能概况的模型来识别具有靶向功能的药物。我们相信,在设计新型功能分子的过程中,功能标签引导的分子发现可以作为传统的基于结构方法的替代方法。
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