可能性的生成变压器语言模型用于分子的生成设计
Lai Wei1, Nihang Fu1, Yuqi Song1
1Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, 29201, USA.
Journal of cheminformatics
|September 25, 2023
概括
我们介绍了生成分子变压器 (GMTransformer),这是一种用于设计有机分子的新型AI模型. 这种可解释,数据高效的模型学习分子语法,以产生高质量的新分子.
科学领域:
- 计算化学和化学信息学
- 人工智能在药物发现中的作用
- 机器学习用于分子设计
背景情况:
- 自主监督的神经语言模型越来越多地用于化学和生物学中的生成设计.
- 现有的分子设计深度学习模型通常需要大量的数据集,并且缺乏可解释性.
- 挑战包括理解设计逻辑和提高生成模型中的数据效率.
研究的目的:
- 开发一种新的概率神经网络模型,用于分子的生成设计.
- 创建一个可解释,数据效率高,能够生成高质量的分子的模型.
- 为了使导向分子修改能够基于学习化学原理的解释.
主要方法:
- 开发了生成分子变压器 (GMTransformer),一个概率神经网络.
- 调整了一个空白填写语言模型,最初用于文字处理,以学习"分子语法".
- 在MOSES数据集上对新性和支架多样性的模型性能进行评估.
主要成果:
- 与现有的基线相比,GMTransformer实现了高新性和脚手架 (Scaf) 度量.
- 与黑盒模型相比,在数据效率和可解释性方面有明显的优势.
- 概率生成步骤允许指导分子修改,并提供解释.
结论:
- 转基因转换器为生成分子设计提供了一种强大而可解释的方法.
- 该模型有效地学习了潜在的分子语法,使高质量和新型分子生成成为可能.
- 它为修改提供解释的能力为分子修补和发现开辟了新的途径.
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