RIGR:共振不变图表表示用于分子性质预测
Akshat Shirish Zalte1, Hao-Wei Pang1, Anna C Doner1
1Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.
Journal of chemical information and modeling
|October 8, 2025
概括
本研究引入了分子的共振不变图表 (RIGR),解决了机器学习模型中由共振结构引起的预测不一致性. 在较少的特征下,RIGR提供了可比或更好的性能,提高了分子性质预测的准确性.
科学领域:
- 计算化学是一种计算化学.
- 机器学习用于药物发现.
- 分子图表表示的分子图表表示.
背景情况:
- 机器学习模型用于分子性质预测通常使用SMILES字符串,这些字符串与呈现共振的分子进行斗争.
- 响应方差导致像Chemprop这样的框架中的不一致预测,阻碍了准确的分子性质分析.
- 现有的方法缺乏统一的方法来处理单个分子的多种共振形式.
研究的目的:
- 开发一种对共振结构不变的新型分子表示.
- 提高基于机器学习的分子性质预测的准确性和一致性.
- 为各种化学任务提供一个一般的图表特色化方案.
主要方法:
- 介绍分子的共振不变图表表示 (RIGR).
- 在定向消息传递神经网络 (D-MPNN) 架构中实现RIGR.
- 对包括激素和闭分子在内的大型数据集进行评估,将RIGR与Chemprop特征器进行比较.
主要成果:
- RIGR确保所有共振结构映射到单个表示,消除预测变异.
- 在使用比Chemprop.com少60%的功能时,RIGR特征器实现了可比或更高的预测性能.
- 像共振形式的数据增强等替代方法显示出局限性.
结论:
- RIGR有效地解决了在分子性质预测中的共振变异的挑战.
- 对于机器学习模型,RIGR提供了一个更高效,更强大的特色化方案.
- 在Chemprop中,RIGR作为开源选项可用,证明了它在化学研究中的广泛适用性.
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