XMolCap:通过多模式融合和可解释的图形神经网络推进分子标题
IEEE journal of biomedical and health informatics
|May 23, 2025
概括
我们开发了XMolCap,这是一个可解释的分子标题框架,使用多式联络数据来加速药物发现. 它通过准确,可解释的描述增强了对分子性质和相互作用的理解.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 人工智能的人工智能是人工智能.
背景情况:
- 大型语言模型 (LLM) 推进了计算生物学,但当前的分子标题化方法未充分利用数据模式,缺乏可解释性.
- 整合各种分子数据 (图像,SMILES,图表) 对于改善药物发现至关重要.
- 现有的方法往往无法为其预测提供明确的解释.
研究的目的:
- 介绍XMolCap,一个新的可解释的分子标题框架.
- 集成多种分子数据类型,以加强药物发现.
- 提供可解释和准确的分子描述.
主要方法:
- 开发了XMolCap,使用堆叠的多式联接融合机制.
- 采用基于BioT5的编码解码器架构,使用专门的模型 (SwinOCSR,SciBERT,GIN-MoMu).
- 集成的分子图像,SMILES字符串和基于图形的结构用于特征提取.
主要成果:
- 在L+M-24和ChEBI-20基准数据集上实现了最先进的性能.
- 在分子标题中表现优于几个强大的基线模型.
- 通过基于图形的解释生成了详细的,功能组意识的和属性特定的解释.
结论:
- XMolCap提供准确和可解释的分子描述,推动药物发现.
- 该框架增强了对分子性质和相互作用的理解.
- 为了可复制性和潜在的临床/制药应用,公开提供.
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