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化学语言模型链接器:将文本和分子与模块化适配器混合
Yifan Deng1,2, Spencer S Ericksen3, Anthony Gitter1,2,4
1Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.
Journal of chemical information and modeling
|August 21, 2025
概括
我们介绍ChemLML, 一种轻量级的适配方法, 这种方法有效地利用预先训练的模型,优于从头开始的训练,并使药物发现等实际应用成为可能.
科学领域:
- 计算化学
- 人工智能
- 药物发现
背景情况:
- 大型语言模型 (LLM) 和多模式模型提供了从文本描述中生成新分子的潜力.
- 目前的多式联网模式往往需要从头开始进行培训,这在计算上是昂贵的,并且限制了可扩展性.
- 现有的方法不能有效地利用高质量的预训练模型来完成这项任务.
研究的目的:
- 提出一种名为化学语言模型链接器 (ChemLML) 的基于轻量级适配器的新策略,用于从文本中生成条件分子.
- 允许使用各种预训练的文本模型来生成分子,而无需进行广泛的再训练.
- 研究分子表示 (SMILES与SELFIES) 对生成性能的影响.
主要方法:
- 开发了基于适配器的ChemLML策略,该策略将预训练的文本和分子域模型连接起来.
- 训练了相对较少的适配器参数,以定制现有文本模型用于分子生成.
- 使用过的PubChem数据集评估性能,并比较SMILES和SELFIES表示.
- 生成的候选蛋白抑制剂和膜透分子用于实践演示.
主要成果:
- ChemLML有效地将预训练的单域模型用于条件分子生成.
- 分子表示的选择显著影响生成性能,SMILES通常是可取的.
- 确定了标准PubChem数据集的评估问题,并提供了精确的测试集.
- 成功生成具有潜在治疗和药物动力学特性的候选分子.
结论:
- 通过利用预训练模型,ChemLML提供了一种高效且可扩展的文本到分子生成方法.
- 分子表示的选择对于成功的条件生成至关重要.
- 开发的方法和数据集有助于在药物发现和分子设计中的实际应用.
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