ReactEmbed: A Plug-and-Play Module for Unifying Protein-Molecule Representations Guided by Biochemical Reaction Networks
arXiv:2501.18278v3 Announce Type: replace Abstract: 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 special
The increasing sophistication of AI models and the demand for holistic biological understanding are driving the development of integrated representations for complex systems.
Unified representations of proteins and molecules are crucial for advancing drug discovery, materials science, and our fundamental understanding of biological processes.
Biological AI models can now integrate protein and small molecule data more effectively, potentially accelerating the design and prediction of new biological functionalities.
- · Biotech companies
- · Pharmaceutical R&D
- · AI in life sciences
- · Synthetic biology research
- · Traditional drug discovery methods
- · Fragmented biological data analysis
Improved accuracy and efficiency in predicting molecular interactions and biological pathways.
Faster development cycles for novel therapeutics and biomaterials enabled by more predictive models.
The democratization of complex biological engineering as AI tools become more integrated and accessible to a wider range of researchers.
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