CLAIRE: a contrastive learning-based predictor for EC number of chemical reactions
Zishuo Zeng, Jin Guo, Jiao Jin, Xiaozhou Luo
Abstract
Predicting EC numbers for chemical reactions enables efficient enzymatic annotations for computer-aided synthesis planning. However, conventional machine learning approaches encounter challenges due to data scarcity and class imbalance. Here, we introduce CLAIRE (Contrastive Learning-based AnnotatIon for Reaction's EC), a novel framework leveraging contrastive learning, pre-trained language model-based reaction embeddings, and data augmentation to address these limitations. CLAIRE achieved notable performance improvements, demonstrating weighted average F1 scores of 0.861 and 0.911 on the testing set (n = 18,816) and an independent dataset (n = 1040) derived from yeast's metabolic model, respectively. Remarkably, CLAIRE significantly outperformed the state-of-the-art model by 3.65 folds and 1.18 folds, respectively. Its high accuracy positions CLAIRE as a promising tool for retrosynthesis planning, drug fate prediction, and synthetic biology applications. CLAIRE is freely available on GitHub ( https://github.com/zishuozeng/CLAIRE ).Scientific contributionThis work employed contrastive learning for predicting enzymatic reaction's EC numbers, overcoming the challenges in data scarcity and imbalance. The new model achieves the state-of-the-art performance and may facilitate the computer-aided synthesis planning.