CoDe-DTI: Collaborative Deep Learning-based Drug-Target Interaction Prediction

Drug-target interaction (DTI) prediction is a problem that identifies novel protein-ligand interactions from previous information. DTI plays an important role in computer-aided drug discovery because it is related to many aspects of drug discovery, such as virtual screening, target prediction, side effect prediction, and drug repositioning. Previous methods can be divided into two types: content-based methods and collaborative filtering. However, both types have problems, namely, a lack of diversity and “cold-start” problems. In this study, we developed a new method named CoDe-DTI (COllaborative DEep learning-based Drug Target Interaction predictor) that combines both methods to avoid these problems. CoDe-DTI is based on collaborative deep learning, which introduces the information of chemical structures into the latent variables by combining probabilistic matrix factorization with a denoising autoencoder. Fivefold cross validation showed that CoDe-DTI significantly outperformed other machine learning-based methods regarding hit rate (top 5%). Comparing between drugwise cross validation and interactionwise cross validation, CoDe-DTI still works even when there is no interaction information of the input ligand exists. The source code for CoDe-DTI is available at: https://github.con/sekijima-lab/CoDe-DTI.