Descriptor-Based Pre-Training Improves Reaction Property Prediction
Year: 2026
Type: preprint
Abstract: Pre-training graph neural networks on large unlabeled datasets has substantially improved molecular property prediction, yet pre-training approaches for reaction property prediction remain underexplored. We introduce CheMeleon-Rxn, a pre-trained reaction graph neural network that adapts the CheMeleon framework to condensed graphs of reaction (CGR) and demonstrates that descriptor-regression is an effective pre-training strategy for low-data reaction property prediction. An O(10M)-parameter D-MPNN encoder was pre-trained on ∼2 million reactions by regressing onto dense reaction descriptors pooled from classical molecular descriptors, then fine-tuned on small reaction property prediction datasets. Across regression tasks spanning gasphase activation energies, reaction enthalpies, experimental yields, and rate coefficients, CheMeleon-Rxn is best or statistically tied-for-best on seven of eight tasks. It outperforms the same encoder trained from scratch, as well as classical-descriptor, fingerprint, and pre-trained-fingerprint baselines. We further tested the pre-training strategy across various graph neural network architectures and found that its benefit holds for other edge-aware backbones such as GINE, and that it is robust to the choice of descriptor set, pooling operation, and other pre-training hyperparameters. We visualized and analyzed the CheMeleon-Rxn embeddings, which reveal chemically coherent structure in the learned reaction representation space. The CheMeleon-Rxn code has been released publicly (https://github.com/MSDLLCpapers/chemeleon-rxn) to encourage adoption and extension by the community. The model weights will be released upon publication.
Source: ChemRxiv
Authors: Akshat Shirish Zalte, Cheng Fang, Yiting Zheng, Essam Metwally, Alan Cheng, Alec Glisman, Hao-Wei Pang
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7212293459