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CLAIRE: a contrastive learning-based predictor for EC number of chemical reactions
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CLAIRE: a contrastive learning-based predictor for EC number of chemical reactions
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Journal Article
CLAIRE: a contrastive learning-based predictor for EC number of chemical reactions
2025
Overview
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 (
C
ontrastive
L
earning-based
A
nnotat
I
on for
R
eaction’s
E
C), 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 contribution
This 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.
Publisher
Springer International Publishing,BioMed Central Ltd,Springer Nature B.V,BMC
