Multiple Similarity Drug-Target Interaction Prediction with Random Walks and Matrix Factorization

TL;DR

This paper introduces MDMF, a novel framework combining multi-layered network embedding and matrix factorization for improved drug-target interaction prediction, outperforming existing methods in accuracy and potential for discovering new interactions.

Contribution

The paper proposes MDMF, a unified model that integrates embedding generation and interaction prediction using multi-layered networks, addressing limitations of previous random walk and matrix factorization approaches.

Findings

Achieves significant performance improvements over state-of-the-art methods

Effectively captures higher-order proximity and local invariance in drug-target networks

Demonstrates potential to identify novel drug-target interactions

Abstract

The discovery of drug-target interactions (DTIs) is a very promising area of research with great potential. The accurate identification of reliable interactions among drugs and proteins via computational methods, which typically leverage heterogeneous information retrieved from diverse data sources, can boost the development of effective pharmaceuticals. Although random walk and matrix factorization techniques are widely used in DTI prediction, they have several limitations. Random walk-based embedding generation is usually conducted in an unsupervised manner, while the linear similarity combination in matrix factorization distorts individual insights offered by different views. To tackle these issues, we take a multi-layered network approach to handle diverse drug and target similarities, and propose a novel optimization framework, called Multiple similarity DeepWalk-based Matrix Factorization (MDMF), for DTI prediction. The framework unifies embedding generation and interaction prediction, learning vector representations of drugs and targets that not only retain higher-order proximity across all hyper-layers and layer-specific local invariance, but also approximate the interactions with their inner product. Furthermore, we develop an ensemble method (MDMF2A) that integrates two instantiations of the MDMF model, optimizing the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC) respectively. The empirical study on real-world DTI datasets shows that our method achieves statistically significant improvement over current state-of-the-art approaches in four different settings. Moreover, the validation of highly ranked non-interacting pairs also demonstrates the potential of MDMF2A to discover novel DTIs.