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.

翻译：给酶功能定性是预测酶-基质相互作用的重要要求。 在本文中,我们介绍了一种新颖的方法,用反向多视角编码来解决这个问题,以改进预测的性能。我们提出了一个方法,利用诸如KEGG等酶数据库的辅助数据,学习酶-基质反应多重观点中存在的相互信息。我们表明,反应数据多重观点的一致性可以用来改进预测性能。