AMTL-RFC:A multi-task learning based method for evaluating the feasibility of enzymatic reactions

In the field of metabolic engineering, evaluating the feasibility of newly generated enzymatic reactions in retrobiosynthesis is a crucial process that helps biologists to efficiently screen out infeasible reactions. However, existing methods overlook the significance of sequence features in molecular SMILES and the ability of model to comprehensively mine and extract features needs to be strengthened. To address these issues, our work propose a novel attention-based multi-task learning (MTL) reaction feasibility checker, named AMTL-RFC, for enzymatic reaction feasibility classification. The model consists of two branches: a Transformer network and a 1-D convolutional neural network (CNN) that extracts SMILES sequence features and spatial structure features of the substrate and product in the reactant pair, respectively. Moreover, a multi-task learning strategy is employed to further enhance the model's performance. Experimental results demonstrate that AMTL-RFC achieves a classification accuracy of 92.27% on the primary test set, which is highly competitive in the task of classifying the feasibility of enzyme reactions.