Abstract

Background Mining high-throughput screening (HTS) assays is key for enhancing decisions in the area of drug repositioning and drug discovery. However, many challenges are encountered in the process of developing suitable and accurate methods for extracting useful information from these assays. Virtual screening and a wide variety of databases, methods and solutions proposed to-date, did not completely overcome these challenges. This study is based on a multi-label classification (MLC) technique for modeling correlations between several HTS assays, meaning that a single prediction represents a subset of assigned correlated labels instead of one label. Thus, the devised method provides an increased probability for more accurate predictions of compounds that were not tested in particular assays.ResultsHere we present DRABAL, a novel MLC solution that incorporates structure learning of a Bayesian network as a step to model dependency between the HTS assays. In this study, DRABAL was used to process more than 1.4 million interactions of over 400,000 compounds and analyze the existing relationships between five large HTS assays from the PubChem BioAssay Database. Compared to different MLC methods, DRABAL significantly improves the F1Score by about 22%, on average. We further illustrated usefulness and utility of DRABAL through screening FDA approved drugs and reported ones that have a high probability to interact with several targets, thus enabling drug-multi-target repositioning. Specifically DRABAL suggests the Thiabendazole drug as a common activator of the NCP1 and Rab-9A proteins, both of which are designed to identify treatment modalities for the Niemann–Pick type C disease.ConclusionWe developed a novel MLC solution based on a Bayesian active learning framework to overcome the challenge of lacking fully labeled training data and exploit actual dependencies between the HTS assays. The solution is motivated by the need to model dependencies between existing experimental confirmatory HTS assays and improve prediction performance. We have pursued extensive experiments over several HTS assays and have shown the advantages of DRABAL. The datasets and programs can be downloaded from https://figshare.com/articles/DRABAL/3309562.Graphical abstract. Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0177-8) contains supplementary material, which is available to authorized users.

Highlights

  • Mining high-throughput screening (HTS) assays is key for enhancing decisions in the area of drug repositioning and drug discovery

  • We enabled drug-multi-target repositioning to show the utility of our method by screening against several targets all drugs from the DrugBank database [39] approved by U.S Food and Drug Administration (FDA)

  • The ability to exploit feedback from these experiments can greatly enhance our decisions about cases, which were not tested for a particular biological target

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Summary

Introduction

Mining high-throughput screening (HTS) assays is key for enhancing decisions in the area of drug repositioning and drug discovery. Many challenges are encountered in the process of developing suitable and accurate methods for extracting useful information from these assays. The ability to analyze big amounts of this data shall enable many opportunities that will, in turn, Mining high-throughput screening (HTS) assays, for example, can provide highly valuable findings for novel uses of existing drugs or proposing new drugs with. A wide variety of databases, methods and solutions were proposed towards handling the challenges that accompany the process of drug discovery by means of virtual screening. Virtual screening is a process based on using computational methods to identify chemical compounds that have high chance to interact with a specific biological target [6]. 3D chemical similarity metrics and network algorithms were combined to achieve structure-based target prediction and reveal the binding mode of certain small molecules [11]

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