Identify drug repurposing candidates by mining the Protein Data Bank
By Fabrice Moriaud, Stéphane B. Richard, Stewart A. Adcock, Laetitia Martin-Chanas, Jean-Sébastien Surgand, Marouane Ben Jelloul, François Delfaud. 2011
56 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Maryam Bagherian
- Zaneta Nikolovska-Coleska
- Maureen A. Sartor
- Hiroshi Mamitsuka
- Kayvan Najarian
- Hao Ding
- Kai Wang
- Ichigaku Takigawa
In Molecular Biology
- Maryam Bagherian
- Zaneta Nikolovska-Coleska
- Maureen A. Sartor
- Hiroshi Mamitsuka
- Kayvan Najarian
- Hao Ding
- Kai Wang
- Ichigaku Takigawa
Citations in the corpus
- SWEETLEAD: an In Silico Database of Approved Drugs, Regulated Chemicals, and Herbal Isolates for Computer-Aided Drug Discovery (2013, neutral): “promising new therapeutic strategies[15-20].”
- Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, background): “Interactions between drugs and targets (proteins) are of importance in drug research, such as facilitating the process of drug discovery [1], drug side-effect prediction [2, 3] and drug repurposing [4–6].”
- Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, background): “While various definitions have been used for these terms [3], drug repositioning usually refers to the studies that reinvestigate existing drugs that failed approval for new therapeutic indications [10], while drug repurposing suggests the application of already approved drugs and compounds to treat”