标题： Assay2Mol：基于大语言模型的药物设计使用BioAssay上下文 显示英文标题

标题： Assay2Mol: large language model-based drug design using BioAssay context

摘要： 科学数据库聚合了大量定量数据以及描述性文本。 在生物化学中，分子筛选试验评估候选分子对疾病靶点的功能反应。 描述这些靶点运作的生物机制、实验筛选协议和其他试验属性的非结构化文本为新的药物发现活动提供了丰富的信息，但由于其非结构化格式，尚未被充分利用。 我们提出了Assay2Mol，一种基于大型语言模型的工作流程，可以利用现有的生物化学筛选试验进行早期阶段的药物发现。 Assay2Mol检索涉及与新靶点相似的靶点的现有试验记录，并使用上下文学习生成候选分子。 Assay2Mol优于最近生成目标蛋白结构候选配体分子的机器学习方法，同时促进了更具合成性的分子生成。 显示英文摘要

摘要： Scientific databases aggregate vast amounts of quantitative data alongside descriptive text. In biochemistry, molecule screening assays evaluate the functional responses of candidate molecules against disease targets. Unstructured text that describes the biological mechanisms through which these targets operate, experimental screening protocols, and other attributes of assays offer rich information for new drug discovery campaigns but has been untapped because of that unstructured format. We present Assay2Mol, a large language model-based workflow that can capitalize on the vast existing biochemical screening assays for early-stage drug discovery. Assay2Mol retrieves existing assay records involving targets similar to the new target and generates candidate molecules using in-context learning with the retrieved assay screening data. Assay2Mol outperforms recent machine learning approaches that generate candidate ligand molecules for target protein structures, while also promoting more synthesizable molecule generation.