arXiv:2512.08508v2 Announce Type: replace-cross Abstract: Identifying enzymes that catalyze target biochemical reactions is a key step in computational enzyme discovery and biocatalyst design. Recent representation-learning methods formulate this problem as enzyme--reaction matching, where paired enzymes and reactions are embedded into a shared space. However, most existing approaches primarily rely on pairwise enzyme--reaction supervision and make limited use of the relationships within reaction sets or enzyme families. This work introduces a multi-alignment contrastive learning framework for
The continuous advancements in representation learning and AI are enabling more sophisticated approaches to biological problems like enzyme-reaction matching, driven by increasing computational power and data availability.
Improved enzyme-reaction retrieval accelerates biocatalyst design and enzyme discovery, critical for pharmaceuticals, industrial biotechnology, and sustainable manufacturing, impacting sectors from medicine to materials science.
The introduction of multi-alignment contrastive learning moves beyond simple pairwise matching, enabling a more comprehensive understanding of enzyme-reaction relationships and potentially more efficient discovery processes.
- · Biotechnology companies
- · Pharmaceutical R&D
- · Computational biology researchers
- · Industrial enzyme producers
- · Traditional high-throughput screening methods
- · Companies reliant on slow enzyme discovery
Faster and more precise identification of enzymes for specific catalytic functions becomes possible.
This could lead to the development of new, more efficient industrial processes and novel therapeutic enzymes.
The enhanced capability to engineer biological systems facilitates the broader adoption of synthetic biology approaches across various industries, reducing reliance on chemical processes.
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Read at arXiv cs.LG