Machine Learning (ML) techniques have found applications in estimating
chemical kinetics properties. With the accumulated drug molecules identified
through "AI4drug discovery", the next imperative lies in AI-driven design for
high-throughput chemical synthesis processes, with the estimation of properties
of unseen reactions with unexplored molecules. To this end, the existing ML
approaches for kinetics property prediction are required to be
Out-Of-Distribution (OOD) generalizable. In this paper, we categorize the OOD
kinetic property prediction into three levels (structure, condition, and
mechanism), revealing unique aspects of such problems. Under this framework, we
create comprehensive datasets to benchmark (1) the state-of-the-art ML
approaches for reaction prediction in the OOD setting and (2) the
state-of-the-art graph OOD methods in kinetics property prediction problems.
Our results demonstrated the challenges and opportunities in OOD kinetics
property prediction. Our datasets and benchmarks can further support research
in this direction.

利用机器学习（ML）技术，本研究探索了化学动力学性质的估计。通过 “AI4drug discovery” 发现的药物分子为基础，下一步的关键是以人工智能驱动的设计高通量化学合成过程，并估计未知反应和未探索分子的性质。为此，对于动力学性质预测的现有 ML 方法需要具备 Out-Of-Distribution（OOD）的可推广性。本文将 OOD 动力学性质预测分为三个级别（结构，条件和机制），揭示了这些问题的独特方面。在这个框架下，我们创建了全面的数据集，用于评估（1）在 OOD 设置中用于反应预测的最新 ML 方法，以及（2）用于动力学性质预测问题的最新图形 OOD 方法。我们的结果展示了 OOD 动力学性质预测中的挑战和机遇。我们的数据集和基准可以进一步支持这一研究方向。