Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation
Chemical reactions are fundamentally transformations in electron space, yet most machine learning approaches model them either through \textit{de novo} generation of product molecules or through heuristic graph edits that operate directly on molecular topology. We introduce MAELLE (\textbf{M}ech\textbf{A}nistic \textbf{E}dit f\textbf{L}ow-matching on e\textbf{L}ectron r\textbf{E}arrangements), which instead models reactions as discrete flow matching over electron occupation vectors. Concretely, we formulate the reactant-to-product mapping as a Continuous-time Markov Chain (CTMC) over the g
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- LinkedLinked via arxiv author · 85%Nguyen Xuan-Vu →
“Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation”
- LinkedLinked via arxiv author · 85%Octavian Susanu →
“Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation”
- LinkedLinked via arxiv author · 85%Daniel Armstrong →
“Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation”
- LinkedLinked via arxiv author · 85%Philippe Schwaller →
“Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation”