在计算激活屏障上对化学反应的机器可读载体进行基准测试
Puck van Gerwen1,2, Ksenia R Briling1, Yannick Calvino Alonso1
1Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland clemence.corminboeuf@epfl.ch.
概括
预测计算激活障碍对于自动反应网络探索至关重要. 本研究对各种计算方法进行了基准测试,包括基于图形和3D结构模型,以确定障碍预测的最有效方法.
科学领域:
- 计算化学计算化学
- 预测化学反应的方法
- 机器学习在化学中的应用
背景情况:
- 化学反应网络的自动探索需要对计算激活障碍的准确预测.
- 为此,已开发出多种计算方法,包括基于图形和3D结构的方法.
- 对于实验性质预测的现有表示也可能适用于障碍预测.
研究的目的:
- 为了对各种计算方法的性能进行基准和比较,以预测计算激活障碍.
- 在各种化学数据集上评估不同分子表示和机器学习模型的有效性.
主要方法:
- 已建立的方法的基准测试:摩根指纹,定向射频 (DRFP),基于CGR的Chemprop,SLATM,B2R2和EquiReact.
- 语言模型 (BERT) 与反应指纹 (RXNFP) 结合的应用.
- 在三个不同的化学反应数据集中进行评估.
主要成果:
- 在所选数据集上对所有测试方法的比较性能分析.
- 识别激活障碍预测的不同方法的优点和弱点.
- 了解方法在不同类型的化学反应中的可转移性.
结论:
- 该研究提供了对预测计算激活障碍的最先进方法的全面比较.
- 结果将指导选择最佳的计算策略,以加速反应网络的探索.
- 这项工作有助于自动化化学发现和设计的进步.
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