Faculty Publications

Improved Genome-Scale Multi-Target Virtual Screening Via A Novel Collaborative Filtering Approach To Cold-Start Problem

Document Type

Article

Journal/Book/Conference Title

Scientific Reports

Volume

6

Abstract

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.

Department

Department of Computer Science

Original Publication Date

12-13-2016

DOI of published version

10.1038/srep38860

Repository

UNI ScholarWorks, Rod Library, University of Northern Iowa

Language

en

Link to Full Text

Comments?

Share

COinS