arXiv Machine Learning
Aug 27

Data-driven Effective Modeling of Stochastic Chemical Reaction Networks

The paper introduces a data-driven effective model for stochastic chemical reaction networks that bypasses the high computational cost of the Stochastic Simulation Algorithm (SSA). By approximating the finite-time transition kernel of the SSA-induced continuous-time Markov chain with a generative machine learning model, the method operates on a user-defined coarse time step independent of microscopic reaction events. Using a conditional normalizing flow as the stochastic propagator, the trained model recursively generates statistically consistent trajectories, achieving significant computational savings while maintaining accuracy, as demonstrated through numerous numerical examples.

By Yuan Chen, Weize Mao, Dongbin Xiu
arXiv AI
Aug 28

Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation

MAELLE is a mechanistic reaction prediction framework that models chemical reactions as discrete flow matching over graph-structured electron occupation vectors. It formulates the reactant-to-product mapping as a Continuous-time Markov Chain on electron sites and uses Optimal Transport to generate mechanistically interpretable edit trajectories without elementary step annotations. The method achieves competitive accuracy on the USPTO-480K benchmark, remains robust in out-of-distribution scenarios, and can recover mechanistic pathways that align with known chemistry and predict side products.

By Nguyen Xuan-Vu, Octavian Susanu, Daniel Armstrong, Philippe Schwaller