Contrastive Multiview Coding for Enzyme-Substrate Interaction Prediction
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Characterizing Enzyme function is an important requirement for predicting Enzyme-Substrate interactions. In this paper, we present a novel approach of applying Contrastive Multiview Coding to this problem to improve the performance of prediction. We present a method to leverage auxiliary data from an Enzymatic database like KEGG to learn the mutual information present in multiple views of enzyme-substrate reactions. We show that congruency in the multiple views of the reaction data can be used to improve prediction performance.
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