title: DT-Web: a web-based application for drug-target interaction and drug combination prediction through domain-tuned network-based inference.
abstract: BACKGROUND
  The identification of drug-target interactions (DTI) is a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Algorithms may aim to design new therapies based on a single approved drug or a combination of them. Recently, recommendation methods relying on network-based inference in connection with knowledge coming from the specific domain have been proposed.
  
  
  METHODS
  Here we propose a web-based interface to the DT-Hybrid algorithm, which applies a recommendation technique based on bipartite network projection implementing resources transfer within the network. This technique combined with domain-specific knowledge expressing drugs and targets similarity is used to compute recommendations for each drug. Our web interface allows the users: (i) to browse all the predictions inferred by the algorithm; (ii) to upload their custom data on which they wish to obtain a prediction through a DT-Hybrid based pipeline; (iii) to help in the early stages of drug combinations, repositioning, substitution, or resistance studies by finding drugs that can act simultaneously on multiple targets in a multi-pathway environment. Our system is periodically synchronized with DrugBank and updated accordingly. The website is free, open to all users, and available at http://alpha.dmi.unict.it/dtweb/.
  
  
  CONCLUSIONS
  Our web interface allows users to search and visualize information on drugs and targets eventually providing their own data to compute a list of predictions. The user can visualize information about the characteristics of each drug, a list of predicted and validated targets, associated enzymes and transporters. A table containing key information and GO classification allows the users to perform their own analysis on our data. A special interface for data submission allows the execution of a pipeline, based on DT-Hybrid, predicting new targets with the corresponding p-values expressing the reliability of each group of predictions. Finally, It is also possible to specify a list of genes tracking down all the drugs that may have an indirect influence on them based on a multi-drug, multi-target, multi-pathway analysis, which aims to discover drugs for future follow-up studies.
authors.forename: Salvatore
authors.initials: S
authors.lastname: Alaimo
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Bonnici
authors.forename: Damiano
authors.initials: D
authors.lastname: Cancemi
authors.forename: Alfredo
authors.initials: A
authors.lastname: Ferro
authors.forename: Rosalba
authors.initials: R
authors.lastname: Giugno
authors.forename: Alfredo
authors.initials: A
authors.lastname: Pulvirenti
mesh_terms: D000465:Algorithms; D004338:Drug Combinations; D055808:Drug Discovery / Q000295:instrumentation* / Q000379:methods*; D004347:Drug Interactions; D020407:Internet
chemical_list: D004338:Drug Combinations