Abstract
Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach—based on knowledge about the chemical structures—can not fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug–target interactions to infer drug properties. To this end, we define drug similarity based on drug–target interactions and build a weighted Drug–Drug Similarity Network according to the drug–drug similarity relationships. Using an energy-model network layout, we generate drug communities associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them potential drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. Using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure based on molecular docking to analyze Azelaic acid and Meprobamate’s repurposing.
Highlights
Conventional drug design has become expensive and cumbersome, as it requires large amounts of resources and faces serious challenges [1,2]
We built our drug–drug similarity network (DDSN) as a weighted graph G = (V, E), where V is the vertex set, and E is the edge set; the vertices represent drugs and the edges represent drug–drug similarity relationships based on drug–target interactions
Using the constructed DDSN from Drug Bank 4.2 and expert analysis, we label each cluster according to its dominant property
Summary
Conventional drug design has become expensive and cumbersome, as it requires large amounts of resources and faces serious challenges [1,2]. The number of new FDA drug applications (NDAs) has significantly increased during the last decade—due to a spectacular accumulation of multi-omics data and the appearance of increasingly complex bioinformatics tools—the number of approved drugs has only marginally grown (see Figure 1) [3,4], calling for more robust alternative strategies [5]. One of the most effective alternative strategies is drug repositioning (or drug repurposing) [7,8], namely finding new pharmaceutical functions for already used drugs. The extensive medical and pharmaceutical experience reveals a surprising propensity towards multiple indications for many drugs [9], and the examples of successful drug repositioning are steadily accumulating. Out of the 90 newly approved drugs in 2016 (a 10% decrease from 2015), 25% are repositionings in formulations, combinations, and indications [4]. Drug repositioning reduces the incurred research and development (R&D) time and costs and medication risks [9,10]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
