Flow matching for reaction pathway generation

TL;DR

This paper introduces MolGEN, a deterministic flow-matching framework for reaction pathway generation that improves accuracy and efficiency over diffusion models, enabling faster and more reliable molecular and reaction generation.

Contribution

The paper presents MolGEN, a novel flow-matching approach that replaces stochastic diffusion models, offering better accuracy, controllability, and efficiency in molecular and reaction pathway generation.

Findings

MolGEN surpasses existing models in TS geometry accuracy.

MolGEN achieves sub-second inference times for reaction generation.

MolGEN produces more valid and intended TSs with fewer quantum-chemistry evaluations.

Abstract

Elucidating reaction mechanisms hinges on efficiently generating transition states (TSs), products, and complete reaction networks. Recent generative models, such as diffusion models for TS sampling and sequence-based architectures for product generation, offer faster alternatives to quantum-chemistry searches. But diffusion models remain constrained by their stochastic differential equation (SDE) dynamics, which suffer from inefficiency and limited controllability. We show that flow matching, a deterministic ordinary differential (ODE) formulation, can replace SDE-based diffusion for molecular and reaction generation. We introduce MolGEN, a conditional flow-matching framework that learns an optimal transport path to transport Gaussian priors to target chemical distributions. On benchmarks used by TSDiff and OA-ReactDiff, MolGEN surpasses TS geometry accuracy and barrier-height prediction while reducing sampling to sub-second inference. MolGEN also supports open-ended product generation with competitive top-k accuracy and avoids mass/electron-balance violations common to sequence models. In a realistic test on the $\gamma$-ketohydroperoxide decomposition network, MolGEN yields higher fractions of valid and intended TSs with markedly fewer quantum-chemistry evaluations than string-based baselines. These results demonstrate that deterministic flow matching provides a unified, accurate, and computationally efficient foundation for molecular generative modeling, signaling that flow matching is the future for molecular generation across chemistry.