Leak-CURBER: A Leakage-Controlled Multimodal Evaluation Benchmark for Enzymatic Reaction Tasks

We present Leak-CURBER, the largest multimodal, leakage-controlled benchmark for evaluating machine learning and deep learning models for enzymatic reactions. Leak-CURBER standardizes data from 10 biochemical databases and curates datasets with multimodal inputs, metrics, and leakage-controlled splits for 4 tasks. The benchmark comprises 10 subtasks: prediction of reaction outcomes (2), enzyme function (2), kinetic parameters (3), and protein–ligand binding (3). Leak-CURBER establishes new evaluation protocols that control for train/validation/test overlap across protein sequence and structure, molecule string and structure, and reaction similarity, thereby exposing generalization failures of learning methods. Evaluating the state-of-the-art (SOTA) models on Leak-CURBER shows that their strong performance degrades sharply under these splits, often approaching random classification or retrieval performance, and near-zero regression performance. Leak-CURBER includes precomputed embeddings, continual benchmark releases, reproducible curation scripts, and a public leaderboard to support reliable comparison of enzymatic-reaction models.