Literature DB >> 27115647

Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.

Salvatore Alaimo1, Rosalba Giugno1, Alfredo Pulvirenti2.   

Abstract

The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

Keywords:  Drug combination prediction; Drug repositioning; Drug–target interaction prediction; Hybrid methods network-based prediction; Recommendation systems

Mesh:

Substances:

Year:  2016        PMID: 27115647     DOI: 10.1007/978-1-4939-3572-7_23

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  6 in total

1.  A new computational drug repurposing method using established disease-drug pair knowledge.

Authors:  Nafiseh Saberian; Azam Peyvandipour; Michele Donato; Sahar Ansari; Sorin Draghici
Journal:  Bioinformatics       Date:  2019-10-01       Impact factor: 6.937

2.  On the Integration of In Silico Drug Design Methods for Drug Repurposing.

Authors:  Eric March-Vila; Luca Pinzi; Noé Sturm; Annachiara Tinivella; Ola Engkvist; Hongming Chen; Giulio Rastelli
Journal:  Front Pharmacol       Date:  2017-05-23       Impact factor: 5.810

Review 3.  A review of computational drug repurposing.

Authors:  Kyungsoo Park
Journal:  Transl Clin Pharmacol       Date:  2019-06-28

Review 4.  Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery.

Authors:  Harshita Bhargava; Amita Sharma; Prashanth Suravajhala
Journal:  Curr Genomics       Date:  2021-12-30       Impact factor: 2.689

Review 5.  Drugs and Targets in Fibrosis.

Authors:  Xiaoyi Li; Lixin Zhu; Beibei Wang; Meifei Yuan; Ruixin Zhu
Journal:  Front Pharmacol       Date:  2017-11-23       Impact factor: 5.810

6.  Drug Research Meets Network Science: Where Are We?

Authors:  Maurizio Recanatini; Chiara Cabrelle
Journal:  J Med Chem       Date:  2020-05-08       Impact factor: 7.446

  6 in total

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