ABIET:一种可解释的变压器,用于识别生物活性分子中的功能组.
Tiago O Pereira1, Maryam Abbasi2, Joel P Arrais1
1Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Univ Coimbra, Coimbra, Portugal.
Computers in biology and medicine
|February 2, 2025
概括
我们开发了ABIET,这是一种可解释的AI工具,它使用变压器模型来确定分子中的关键功能组,用于药物发现. 这提高了透明度,并有助于设计更有效的药物.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 分子建模分子建模
背景情况:
- 深度学习,特别是变压器模型,在药物发现方面表现有前途,但缺乏可解释性.
- 识别功能组等关键分子区域对于理解药物向相互作用至关重要.
- 目前用于化学AI模型的可解释性方法通常是不够的.
研究的目的:
- 引入ABIET (基于注意力的重要估计工具),这是一个可解释的AI模型,用于识别药物分子中的关键功能组.
- 提高基于变压器的模型在预测药物向相互作用方面的透明度.
- 为改进分子设计和结构-活性关系 (SAR) 分析提供见解.
主要方法:
- 利用了在分子的SMILES表示上训练的变压器编码器架构.
- 利用注意力权重来评估分子子区域的重要性.
- 开发了处理注意力得分的特定策略,包括双向交互和基于层的提取.
主要成果:
- ABIET通过赋予它们更高的重要性分数,成功地将功能组与非功能组原子区分开来.
- 在多种药物向数据集 (VEGFR2,AA2A,GSK3,JNK3,DRD2) 上的实验验证表明了模型的稳定性和可解释性.
- 对比分析显示,ABIET的表现优于现有的基于梯度和基于扰动的可解释性方法.
结论:
- 在药物发现中,ABIET显著提高了深度学习模型的可解释性.
- 该工具为结构-活性关系提供了宝贵的见解,有助于向药物开发.
- 这项工作为更加透明和可靠的AI驱动分子设计铺平了道路.
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