Towards out-of-distribution generalizable predictions of chemical
kinetics properties
- URL: http://arxiv.org/abs/2310.03152v2
- Date: Mon, 4 Dec 2023 20:12:42 GMT
- Title: Towards out-of-distribution generalizable predictions of chemical
kinetics properties
- Authors: Zihao Wang, Yongqiang Chen, Yang Duan, Weijiang Li, Bo Han, James
Cheng, Hanghang Tong
- Abstract summary: Out-Of-Distribution (OOD) kinetic property prediction is required to be generalizable.
In this paper, we categorize the OOD kinetic property prediction into three levels (structure, condition, and mechanism)
We create comprehensive datasets to benchmark the state-of-the-art ML approaches for reaction prediction in the OOD setting and the state-of-the-art graph OOD methods in kinetics property prediction problems.
- Score: 61.15970601264632
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Machine Learning (ML) techniques have found applications in estimating
chemical kinetic 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.
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