MMPCS:基于一致性信息和特定信息的多视图分子预训练.
Chenyang Xie1, Yingying Song1, Song He2
1School of Informatics, Xiamen University, Xiamen, 361005, China.
Bioinformatics (Oxford, England)
|January 14, 2026
概括
我们开发了MMPCS,一种新的多视图分子预训练方法,通过分离一致性和特定信息来改善分子表示学习. 这种方法提高了药物发现任务的性能,包括属性预测和药物重新用途.
科学领域:
- 化学信息学 化学信息学
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 分子表示学习自动化器具有用于化学信息学和药物发现的特征提取.
- 整合多个分子数据视图 (SMILES,2D图形,3D形态) 仍然是高级表示的一个挑战.
研究的目的:
- 提出一种新的多视图分子预训练方法,MMPCS,以应对整合不同分子数据视图的挑战.
- 为了明确地将分子表示分成一致性和视图特定信息,以提高下游任务性能.
主要方法:
- 用于2D分子图的利用图形同型网络和用于SMILES序列的RoBERTa.
- 分解分子嵌入到共享一致性和视图特定的组件中,通过自动编码器对齐.
- 结合一致性和视图特定表示,用于任务意识的预测.
主要成果:
- 在16种最先进的分子性质预测 (分类和回归) 方法中,MMPCS实现了优异的平均性能.
- 在预测药物标结合亲和力和癌症药物反应方面表现出卓越的结果.
- 关于SARS-CoV-2 Omicron变异的案例研究突出了药物重新用途的潜力.
结论:
- MMPCS有效地整合了多视图分子数据,产生了改进的表示.
- 该方法在药物发现和开发中显示出强度和广泛适用性.
- MMPCS为各种化学信息应用提供了精确和任务意识的预测.
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