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ReactEmbed: A Plug-and-Play Module for Unifying Protein-Molecule Representations Guided by Biochemical Reaction Networks

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State-of-the-art models represent proteins and molecules in separate embedding manifolds, limiting the modeling of systemic biological processes. We introduce ReactEmbed, a lightweight, plug-and-play module that bridges this gap. ReactEmbed leverages biochemical reaction networks as a source of functional context, based on the principle that co-participation in reactions defines a shared functional scope. The module aligns frozen embeddings from models like ESM-3 and MolFormer into a unified space using a weighted reaction graph and a specialized sampling strategy. This process enriches unimodal embeddings and enables strong performance on cross-domain benchmarks. ReactEmbed offers a practical method to unify biological representations without costly retraining. The code and database are available for open use\footnote{https://github.com/amitaysicherman/ReactEmbeded}.

Amitay Sicherman, Kira Radinsky• 2025

Related benchmarks

TaskDatasetResultRank
RegressionFreeSolv
RMSE2.85
45
ClassificationBBBP
ROC-AUC0.6522
39
Graph RegressionCEP
RMSE1.62
19
ClassificationDrugbank
AUC85.53
17
RegressionBindingDB
RMSE1.17
17
ClassificationGO-CC
AUC82.32
12
ClassificationHumanPPI
AUC94.88
12
ClassificationYeastPPI
AUC67.68
12
RegressionStability
RMSE0.43
12
RegressionPPIAffinity
RMSE3.02
12
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