GSST:多模式图-SMILES融合与软SMILES令牌用于分子性质预测
IEEE journal of biomedical and health informatics
|October 6, 2025
概括
这项研究介绍了GSST,一种用于药物发现的多式预培训框架. 通过整合图形和SMILES表示,GSST增强了分子性质预测,提高了有限数据的准确性.
科学领域:
- 计算化学是一种计算化学.
- 药物发现信息学 药物发现信息学
- 机器学习用于化学.
背景情况:
- 由于数据稀疏,单模分子表示限制了药物发现中的概括性.
- 从单个模式的不完整表征阻碍了准确的分子性质预测.
研究的目的:
- 提出GSST,一个新的多式联运预培训框架,以克服单式联运表示的局限性.
- 为了提高分子性质预测和活性悬崖评估在药物发现.
主要方法:
- 开发了GSST,一个多式预培训框架,集成基于图形和SMILES序列表示.
- 引入了G2S-Former模块用于交叉模式交互,将图形拓注入软的SMILES令牌中.
- 利用可学习的软 SMILES 代币来实现有效的模式融合.
主要成果:
- 在MoleculeNet和MoleculeACE基准指标上,GSST的表现始终超过了最先进的方法.
- 在分子性质预测和活动悬崖评估方面表现出卓越的表现.
- 突出了多式调整在捕捉共享的分子模式的有效性.
结论:
- GSST有效地解决了分子表示中的数据稀疏性和模式不完整性.
- 多式调整对于通过改进分子性质预测来推进药物发现至关重要.
- GSST提供了一个可扩展的,高吞吐量方法,具有药物发现的巨大潜力.
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