使用化学语言模型将分子核心,替代剂和组合转化为结构多样化的化合物
Lisa Piazza1, Sanjana Srinivasan2, Tiziano Tuccinardi3
1Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115, Bonn, Germany; Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126, Pisa, Italy.
European journal of medicinal chemistry
|April 13, 2025
概括
新的化学语言模型 (CLMs) 产生了新的,多样化的分子结构,没有预定义的规则. 这些模型通过创造独特的化合物和已知的生物活性分子的类似物来显示药物发现的前景.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 机器学习在药物发现中的作用
背景情况:
- 传统的方法难以产生结构多样化的化学化合物.
- 化学语言模型 (CLMs) 为分子设计提供了一种新的方法.
研究的目的:
- 开发和评估基于变压器的CLM,用于生成多种化学结构.
- 评估CLM在没有明确的结构规则的情况下学习碎片到化合物映射的能力.
主要方法:
- 培训基于变压器的CLM在碎片到化合物映射上.
- 挑战CLM变体,从核心碎片和替代剂产生有效的化合物.
- 分析生成的化合物的语法忠实性和化学多样性.
主要成果:
- CLM表现出高的语法忠实性,并产生结构新的化合物.
- 一个模型变体处理核心/替代剂组合显示了对有效候选生成的偏好.
- 生成的化合物表现出高度的化学多样化和新的拓学,具有众多已知的生物活性分子的类似物.
结论:
- 基于变压器的CLM可以有效地产生多样化和新的化学结构.
- 这些模型具有加速制药研究和药物发现的巨大潜力.
- 该方法和数据是公开可用的,以促进进一步的研究.
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