ReactEmbed: A Plug-and-Play Module for Unifying Protein-Molecule Representations Guided by Biochemical Reaction Networks
About
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}.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Regression | FreeSolv | RMSE2.85 | 45 | |
| Classification | BBBP | ROC-AUC0.6522 | 39 | |
| Graph Regression | CEP | RMSE1.62 | 19 | |
| Classification | Drugbank | AUC85.53 | 17 | |
| Regression | BindingDB | RMSE1.17 | 17 | |
| Classification | GO-CC | AUC82.32 | 12 | |
| Classification | HumanPPI | AUC94.88 | 12 | |
| Classification | YeastPPI | AUC67.68 | 12 | |
| Regression | Stability | RMSE0.43 | 12 | |
| Regression | PPIAffinity | RMSE3.02 | 12 |