从SMILES到增强的分子性质预测:一个统一的多模式框架,预测了3D符合性和对比性学习技术
Long D Nguyen1, Quang H Nguyen1, Quang H Trinh1
1School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi 100000, Vietnam.
Journal of chemical information and modeling
|December 6, 2024
概括
这项研究引入了一种使用SMILES和3D对应器的新型多模式分子性质预测框架. 该模型通过提高各种分子任务的预测准确度来增强药物发现.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 机器学习在药物发现中的作用
背景情况:
- 准确的分子性质预测对于有效的药物发现至关重要.
- 传统的方法通常依赖于有限的分子表示.
- 整合多样化的分子数据可以提高预测能力.
研究的目的:
- 开发一种用于分子性质预测的新型多式联络框架.
- 为了利用 1D SMILES 字符串和 3D 调整器数据.
- 为了提高药物发现应用的预测准确性和模型稳定性.
主要方法:
- 一个多式模式框架,集成SMILES (顺序) 和3D对应器 (空间) 表示.
- 与InfoNCE损失的对比学习用于嵌入对齐.
- 特定任务的损失 (回归的ConR,分类的SupCon).
- 特性分布平滑 (FDS) 解决数据不平衡的问题.
主要成果:
- 该框架在多个案例研究中表现出优于最先进的方法的性能.
- ConR和FDS显著改善了回归任务的性能.
- SupCon增强了分类性能. SupCon增强了分类性能.
- 在SARS-CoV-2药物对接,MoleculeNet基准和酶抑制剂预测中的成功应用.
结论:
- 拟议的多式联络模型对于各种分子性质预测任务具有灵活性和稳定性.
- 整合SMILES和3D调整器数据提供了显著的优势.
- 该框架显示了加速药物发现和分子分析的有希望的应用.
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