Abstract

   Drug-target interaction (DTI) is a key aspect in pharmaceutical
   research. With the ever-increasing new drug data resources,
   computational approaches have emerged as powerful and labor-saving
   tools in predicting new DTIs. However, so far, most of these
   predictions have been based on structural similarities rather than
   biological relevance. In this study, we proposed for the first time a
   “GO and KEGG enrichment score” method to represent a certain category
   of drug molecules by further classification and interpretation of the
   DTI database. A benchmark dataset consisting of 2,015 drugs that are
   assigned to nine categories ((1) G protein-coupled receptors, (2)
   cytokine receptors, (3) nuclear receptors, (4) ion channels, (5)
   transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens
   and (9) pathogens) was constructed by collecting data from KEGG. We
   analyzed each category and each drug for its contribution in GO terms
   and KEGG pathways using the popular feature selection “minimum
   redundancy maximum relevance (mRMR)” method, and key GO terms and KEGG
   pathways were extracted. Our analysis revealed the top enriched GO
   terms and KEGG pathways of each drug category, which were highly
   enriched in the literature and clinical trials. Our results provide for
   the first time the biological relevance among drugs, targets and
   biological functions, which serves as a new basis for future DTI
   predictions.

Introduction

   Drug-target interaction (DTI) studies are of great importance for drug
   research and development (R&D), as they give rise to a better
   understanding of how drug molecules interact with their targets and
   predict possible adverse drug reactions (ADRs). Over the past decade,
   statistics have revealed a significant decrease in the rate that new
   drug candidates are translated into effective therapies in the clinic
   [[33]1], and drug repositioning has grown in importance. The
   application of known drugs and compounds for new indications would
   require even more DTI information. Because the experimental examination
   of DTI is both time- and labor-consuming, it is necessary to develop
   computational approaches in this field.

   The use of in silico methods as a complement can help researchers to
   quickly obtain useful information. In recent years, a great deal of
   effort has been expended on the prediction of DTIs, and a number of
   methods have been developed.

   Text-mining approaches emerged as a simple and convenient tool to
   search published literature for the associations between drugs and
   genes [[34]2], but they tend to produce redundancy due to multiple gene
   and chemical names. Later, molecular docking approaches were widely
   applied in DTI studies. Cheng et al. used molecular docking to identify
   drugs and their targets [[35]3], and Li et al. developed reverse
   ligand-protein docking to automatically search for compound-protein
   interactions [[36]4]. Despite these advantages, docking and reverse
   docking are only suitable for proteins with known 3D structures, which
   limits their applications. Other computational methods predict DTIs by
   similarities in phenotypic side effects [[37]5] or chemical structures
   [[38]6] or by connections between chemicals with chemicals/proteins
   [[39]6]. Moreover, several network-based algorithms have been applied
   for DTI prediction. Prado-Prado et al. developed multi-target QSAR
   (Quantitative Structure–Activity Relationship) models with 3D
   structural parameters and artificial neural network algorithms for the
   prediction of acetylcholinesterase and its inhibitors [[40]7]. Cheng et
   al. employed network-based inference methods to identify new targets
   for known drugs [[41]8].

   Despite the advancement in computational methods in DTI prediction, the
   above methods are primarily based on the structural similarities of
   drugs rather than biological relevance. Recently, several studies have
   reported the feasible prediction of drug targets and drug repositioning
   using drug-involved pathway analysis. For example, Kotelnikova et al.
   found one signaling pathway that was associated with glioblastoma by
   retrieving references and databases and searching for compounds that