Predicting enzyme-compound associations for enzyme-catalysed reactions

Abstract Enzyme-catalysed reactions are common in many areas, including pharmaceutical metabolism and agricultural chemical biodegradation. Analysing and predicting how these reactions occur is increasingly important for identifying toxic by-products and achieving regulatory approval. Incorporating enzyme information into these predictions has been shown to improve prediction capabilities. However, existing methods require knowledge of the enzyme to perform prediction, and in many situations, especially biodegradation, the complexities of the reaction environment mean the exact enzymes are not known. In this paper, we alleviate this issue by proposing a framework to train and evaluate a hierarchical multi-label classifier to predict the association between enzyme commission numbers and chemical compounds. Our method achieves a hierarchical F1-score of up to 93.2%, outperforming existing methodologies. Additionally, we examine how including true and predicted enzyme information impacts product prediction performance compared to not using enzyme information. In our case study utilising biodegradation reaction data, we find that including enzyme commission numbers improve product prediction performance by approximately two percentage points. Scientific contribution We contribute a novel method for predicting enzyme-compound associations using a hierarchical multi-label classifier framework. Our method is self tuning to find the best hyperparameters for a given dataset and achieves higher F1 scores than existing methods. We also contribute an investigation into including enzyme information into product prediction algorithms, showing that including this information can improve product prediction performance. Graphical abstract