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arXiv AI By Yutong Hu, Tianming Huang, Yanbo Zhao, Qiongyu Zhang, Shixiang Tang, Lei Bai, Ziyi Zhou, Liang Hong, Pan Tan

Elucidating the Space of Enzymatic Reaction: A Unified Benchmark and Pretrained Model

Read the original on arXiv AI →

The paper introduces VenusRX-Bench, a unified benchmark that evaluates forward reaction prediction, single‑step retrosynthesis, and EC‑number prediction for enzymatic reactions, integrating data from multiple biochemical databases with standardized curation and leakage‑controlled splits. It also presents VenusRX, a T5‑style sequence‑to‑sequence model that jointly learns these tasks, leveraging two‑stage training on millions of template‑expanded reactions and real biochemical data, with optional EC conditioning and molecule‑library‑constrained decoding to improve complex biomolecule generation. Across benchmark tasks and challenging generalization splits, VenusRX outperforms or matches existing chemical and enzymatic baselines, demonstrating that incorporating EC information and learned reaction representations yields a bidirectional relationship between reaction structure and catalytic function.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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