A multiple kernel learning algorithm for drug-target interaction prediction.

André C A Nascimento,Ricardo B C Prudêncio,Ivan G Costa

doi:10.1186/s12859-016-0890-3

Abstract

BackgroundDrug-target networks are receiving a lot of attention in late years, given its relevance for pharmaceutical innovation and drug lead discovery. Different in silico approaches have been proposed for the identification of new drug-target interactions, many of which are based on kernel methods. Despite technical advances in the latest years, these methods are not able to cope with large drug-target interaction spaces and to integrate multiple sources of biological information.ResultsWe propose KronRLS-MKL, which models the drug-target interaction problem as a link prediction task on bipartite networks. This method allows the integration of multiple heterogeneous information sources for the identification of new interactions, and can also work with networks of arbitrary size. Moreover, it automatically selects the more relevant kernels by returning weights indicating their importance in the drug-target prediction at hand. Empirical analysis on four data sets using twenty distinct kernels indicates that our method has higher or comparable predictive performance than 18 competing methods in all prediction tasks. Moreover, the predicted weights reflect the predictive quality of each kernel on exhaustive pairwise experiments, which indicates the success of the method to automatically reveal relevant biological sources.ConclusionsOur analysis show that the proposed data integration strategy is able to improve the quality of the predicted interactions, and can speed up the identification of new drug-target interactions as well as identify relevant information for the task.AvailabilityThe source code and data sets are available at www.cin.ufpe.br/~acan/kronrlsmkl/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0890-3) contains supplementary material, which is available to authorized users.

Highlights

Drug-target networks are receiving a lot of attention in late years, given its relevance for pharmaceutical innovation and drug lead discovery
In this work, we propose an extension of the Kronecker regularized least squares approach (KronRLS) algorithm under recent developments of the Multiple Kernel Learning (MKL) framework [28] to address the problem of link prediction on bipartite networks with multiple kernels
Paired kernel experiments As a base study, we evaluate the performance of KronRLS on all pairs of kernels (10 × 10 pairs)

Summary

Introduction

Drug-target networks are receiving a lot of attention in late years, given its relevance for pharmaceutical innovation and drug lead discovery. Despite technical advances in the latest years, these methods are not able to cope with large drug-target interaction spaces and to integrate multiple sources of biological information. Drug-target networks are receiving a lot of attention in late years, given their relevance for pharmaceutical innovation and drug repositioning purposes [1,2,3]. An increasing number of methods have been proposed for drug-target interaction (DTI) prediction. They can be categorized in ligand-based, docking-based, or network-based methods [4]. The docking approach, which can provide accurate estimates to DTIs, is computationally demanding and requires a 3D model of the target protein. Ligand-based methods, such as the quantitative structure activity relationship (QSAR), are based

Methods

Results

Conclusion