Improved compound-protein interaction site and binding affinity prediction using self-supervised protein embeddings

Background: Compound-protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many deep learning-based methods have been proposed for predications related to compound-protein interaction. For protein inputs, how to make use of protein primary sequence and tertiary structure information has impact on prediction results.Results: In this study, we propose a deep learning model based on a multi-objective neural network, which involves a multi-objective neural network for compound-protein interaction site and binding affinity prediction. We used several kinds of self-supervised protein embeddings to enrich our protein inputs and used convolutional neural networks to extract features from them. Our results demonstrate that our model had improvements in terms of interaction site prediction and affinity prediction compared to previous models. In a case study, our model could better predict binding sites, which also showed its effectiveness.Conclusion: These results suggest that our model could be a helpful tool for compound-protein related predictions.

Automated summary

In this study, a deep learning model based on a multi-objective neural network was proposed for predicting compound-protein interaction site and binding affinity. The results demonstrated improvements in prediction and better performance in binding site prediction.