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FG-BERT:一种基于功能组的分子表示学习框架,用于属性预测和自我监督

Biaoshun Li¹, Mujie Lin¹, Tiegen Chen²

¹Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.

Briefings in bioinformatics

|November 6, 2023

概括

我们开发了FG-BERT,这是一种用于预测分子性质的深度学习框架. 这种人工智能模型在药物发现任务中表现出色,提供高性能和可解释性,而不需要手动功能工程.

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
深度学习用于分子建模.

背景情况:

准确的分子性质预测对于设计新型生物活性分子和功能材料至关重要.
现有的方法通常需要广泛的特征工程或缺乏可解释性.
深度学习为学习复杂的分子表示提供了潜力.

研究的目的:

介绍FG-BERT,这是一个用于分子性质预测的新型自主监督深度学习框架.
从功能组直接学习有意义的分子表示.
为分子设计任务提供高性能和可解释的模型.

主要方法:

开发了一个自主监督的深度学习框架,命名为功能组双向编码器表示从变压器 (FG-BERT).
在约145万个未标记的类似药物的分子上预先训练FG-BERT.
为各种分子性质预测任务微调预训练的FG-BERT.
在FG-BERT中利用注意力机制,以提高可解释性.

主要成果:

与最先进的机器学习和深度学习方法相比,FG-BERT在44个基准数据集中表现出卓越的性能.
该模型在预测与物理化学,生物物理学和生理学相关的属性方面取得了很高的准确性.
注意力机制突出了关键的功能组特征与目标属性相关,增强了模型的可解释性.

结论:

FG-BERT为开发分子发现的最先进模型提供了一个开箱即用的框架,特别是用于药物开发.
该框架消除了对人工特征工程的需求,简化了预测过程.
FG-BERT提供了很好的解释性,有助于理解房地产预测的基础.

关键词:

FG-BERT 在这里.深度学习是一种深度学习.分子性质预测分子性质预测分子表征的分子表示.自主监督学习学习

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