Predicting Drug-Protein Interactions Based on Similarity Reconstruction and Adaptive Combination Algorithm

Applying computational methods to predict drug-protein interactions can accelerate drug discovery. An ideal computational method should identify false negative samples within the covered space of its dataset, known as introspection, and exhibit good generalizability and scalability beyond the covered space. However, current computational methods have struggled to achieve both objectives simultaneously. In this paper, we propose CombDPI, which leverages similarity reconstruction and an adaptive combination algorithm. It comprises two parts. The first part focuses on predicting false negative samples within the dataset's covered space. CombDPI assesses similarity from various perspectives, enhances reliability by reconstructing similarity relationships, and utilizes an adaptive combination algorithm to complete the predictions. The second part of CombDPI is dedicated to predicting interactions for novel drugs or proteins. In this part, the model utilizes the similarity between novel drugs or proteins and known drugs or proteins to create their respective representations. By leveraging these representations for prediction, CombDPI becomes less dependent on the prediction of other drug-protein pairs, thereby greatly enhancing its generalizability and scalability. Experimental results on three benchmark datasets, with both in-space and out-space settings, demonstrate that CombDPI outperforms existing interaction prediction methods. Furthermore, case studies highlight CombDPI's ability to discover potential drug-protein interactions.