publication . Article . Other literature type . 2017

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

Eric March-Vila; Luca Pinzi; Noé Sturm; Annachiara Tinivella; Ola Engkvist; Hongming Chen; Giulio Rastelli;
Open Access
  • Published: 23 May 2017
  • Country: Italy
Abstract
Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful exam-ples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug-target, target-disease, and ulti-mately drug-disease associations.
Subjects
free text keywords: "Marie Sklodowska-Curie Actions", bidrug repurposing, drug discovery, molecular modeling, chemogenomics, structure-based drug design, ligand-based drug design, machine learning, transcriptomics, Chemogenomics; Drug discovery; Drug repurposing; Ligand-based drug design; Machine learning; Molecular modeling; Structure-based drug design; Transcriptomics; Pharmacology; Pharmacology (medical), Pharmacology, drug repurposing, drug discovery, molecular modeling, chemogenomics, structure-based drug design, ligand-based drug design, machine learning, transcriptomics, Perspective, Therapeutics. Pharmacology, RM1-950, Repurposing, Drug repositioning, Drug, media_common.quotation_subject, media_common, Medicine, business.industry, business, chemistry.chemical_compound, chemistry, In silico, Design methods, Business process discovery
Funded by
EC| BIGCHEM
Project
BIGCHEM
Big Data in Chemistry
  • Funder: European Commission (EC)
  • Project Code: 676434
  • Funding stream: H2020 | MSCA-ITN-EID
46 references, page 1 of 4

Alaimo S.Giugno R.Pulvirenti A. (2016). Recommendation techniques for drug-target interaction prediction and drug repositioning. Methods Mol. Biol. 1415 441–462. 10.1007/978-1-4939-3572-7-23 27115647 [OpenAIRE] [PubMed] [DOI]

Anighoro A.Stumpfe D.Heikamp K.Beebe K.Neckers L. M.Bajorath J. (2015). Computational polypharmacology analysis of the heat shock protein 90 interactome. J. Chem. Inf. Model. 55 676–686. 10.1021/ci5006959 25686391 [OpenAIRE] [PubMed] [DOI]

Bender A.Scheiber J.Glick M.Davies J. W.Azzaoui K.Hamon J. (2007). Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem 2 861–873. 10.1002/cmdc.200700026 17477341 [OpenAIRE] [PubMed] [DOI]

Berman H. M.Westbrook J.Feng Z.Gilliland G.Bhat T. N.Weissig H. (2000). The protein data bank. Nucleic Acids Res. 28 235–242. 10.1093/nar/28.1.235 10592235 [OpenAIRE] [PubMed] [DOI]

Bowman G. R.Bolin E. R.Hart K. M.Magui re B. C.Marqusee S. (2015). Discovery of multiple hidden allosteric sites by combining Markov state models and experiments. Proc. Natl. Acad. Sci. U. S. A. 112 2734–2739. 10.1073/pnas.1417811112 25730859 [OpenAIRE] [PubMed] [DOI]

Brown A. S.Patel C. J. (2017). A standard database for drug repositioning. Sci. Data 4 170029 10.1038/sdata.2017.29 [DOI]

Chang M.Smith S.Thorpe A.Barratt M. J.Karim F. (2010). Evaluation of phenoxybenzamine in the CFA model of pain following gene expression studies and connectivity mapping. Mol. Pain 6:56 10.1186/1744-8069-6-56 [OpenAIRE] [DOI]

Chen Y.-C.Tolbert R.Aronov A. M.McGaughey G.Walters W. P.Meireles L. (2016). Prediction of protein pairs sharing common active ligands using protein sequence, structure, and ligand similarity. J. Chem. Inf. Model. 56 1734–1745. 10.1021/acs.jcim.6b00118 27559831 [OpenAIRE] [PubMed] [DOI]

Costa F. F. (2014). Big data in biomedicine. Drug Discov. Today 19 433–440. 10.1016/j.drudis.2013.10.012 24183925 [PubMed] [DOI]

Dakshanamurthy S.Issa N. T.Assefnia S.Seshasayee A.Peters O. J.Madhavan S. (2012). Predicting new indications for approved drugs using a proteo-chemometric method. J. Med. Chem. 55 6832–6848. 10.1021/jm300576q 22780961 [OpenAIRE] [PubMed] [DOI]

Defranchi E.De Franchi E.Schalon C.Messa M.Onofri F.Benfenati F. (2010). Binding of protein kinase inhibitors to synapsin I inferred from pair-wise binding site similarity measurements. PLoS ONE 5:e12214 10.1371/journal.pone.0012214 [OpenAIRE] [DOI]

Ehrt C.Brinkjost T.Koch O. (2016). Impact of binding site comparisons on medicinal chemistry and rational molecular design. J. Med. Chem. 59 4121–4151. 10.1021/acs.jmedchem.6b00078 27046190 [OpenAIRE] [PubMed] [DOI]

Gaulton A.Hersey A.Nowotka M.Bento A. P.Chambers J.Mendez D. (2017). The ChEMBL database in 2017. Nucleic Acids Res. 45 D945–D954. 10.1093/nar/gkw1074 27899562 [OpenAIRE] [PubMed] [DOI]

Gregori-PuigjanéE.Mestres J. (2008). A ligand-based approach to mining the chemogenomic space of drugs. Comb. Chem. High Throughput Screen. 11 669–676.18795886 [PubMed]

Hall D. R.Kozakov D.Whitty A.Vajda S. (2015). Lessons from hot spot analysis for fragment-based drug discovery. Trends Pharmacol. Sci. 36 724–736. 10.1016/j.tips.2015.08.003 26538314 [OpenAIRE] [PubMed] [DOI]

46 references, page 1 of 4
Abstract
Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful exam-ples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug-target, target-disease, and ulti-mately drug-disease associations.
Subjects
free text keywords: "Marie Sklodowska-Curie Actions", bidrug repurposing, drug discovery, molecular modeling, chemogenomics, structure-based drug design, ligand-based drug design, machine learning, transcriptomics, Chemogenomics; Drug discovery; Drug repurposing; Ligand-based drug design; Machine learning; Molecular modeling; Structure-based drug design; Transcriptomics; Pharmacology; Pharmacology (medical), Pharmacology, drug repurposing, drug discovery, molecular modeling, chemogenomics, structure-based drug design, ligand-based drug design, machine learning, transcriptomics, Perspective, Therapeutics. Pharmacology, RM1-950, Repurposing, Drug repositioning, Drug, media_common.quotation_subject, media_common, Medicine, business.industry, business, chemistry.chemical_compound, chemistry, In silico, Design methods, Business process discovery
Funded by
EC| BIGCHEM
Project
BIGCHEM
Big Data in Chemistry
  • Funder: European Commission (EC)
  • Project Code: 676434
  • Funding stream: H2020 | MSCA-ITN-EID
46 references, page 1 of 4

Alaimo S.Giugno R.Pulvirenti A. (2016). Recommendation techniques for drug-target interaction prediction and drug repositioning. Methods Mol. Biol. 1415 441–462. 10.1007/978-1-4939-3572-7-23 27115647 [OpenAIRE] [PubMed] [DOI]

Anighoro A.Stumpfe D.Heikamp K.Beebe K.Neckers L. M.Bajorath J. (2015). Computational polypharmacology analysis of the heat shock protein 90 interactome. J. Chem. Inf. Model. 55 676–686. 10.1021/ci5006959 25686391 [OpenAIRE] [PubMed] [DOI]

Bender A.Scheiber J.Glick M.Davies J. W.Azzaoui K.Hamon J. (2007). Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem 2 861–873. 10.1002/cmdc.200700026 17477341 [OpenAIRE] [PubMed] [DOI]

Berman H. M.Westbrook J.Feng Z.Gilliland G.Bhat T. N.Weissig H. (2000). The protein data bank. Nucleic Acids Res. 28 235–242. 10.1093/nar/28.1.235 10592235 [OpenAIRE] [PubMed] [DOI]

Bowman G. R.Bolin E. R.Hart K. M.Magui re B. C.Marqusee S. (2015). Discovery of multiple hidden allosteric sites by combining Markov state models and experiments. Proc. Natl. Acad. Sci. U. S. A. 112 2734–2739. 10.1073/pnas.1417811112 25730859 [OpenAIRE] [PubMed] [DOI]

Brown A. S.Patel C. J. (2017). A standard database for drug repositioning. Sci. Data 4 170029 10.1038/sdata.2017.29 [DOI]

Chang M.Smith S.Thorpe A.Barratt M. J.Karim F. (2010). Evaluation of phenoxybenzamine in the CFA model of pain following gene expression studies and connectivity mapping. Mol. Pain 6:56 10.1186/1744-8069-6-56 [OpenAIRE] [DOI]

Chen Y.-C.Tolbert R.Aronov A. M.McGaughey G.Walters W. P.Meireles L. (2016). Prediction of protein pairs sharing common active ligands using protein sequence, structure, and ligand similarity. J. Chem. Inf. Model. 56 1734–1745. 10.1021/acs.jcim.6b00118 27559831 [OpenAIRE] [PubMed] [DOI]

Costa F. F. (2014). Big data in biomedicine. Drug Discov. Today 19 433–440. 10.1016/j.drudis.2013.10.012 24183925 [PubMed] [DOI]

Dakshanamurthy S.Issa N. T.Assefnia S.Seshasayee A.Peters O. J.Madhavan S. (2012). Predicting new indications for approved drugs using a proteo-chemometric method. J. Med. Chem. 55 6832–6848. 10.1021/jm300576q 22780961 [OpenAIRE] [PubMed] [DOI]

Defranchi E.De Franchi E.Schalon C.Messa M.Onofri F.Benfenati F. (2010). Binding of protein kinase inhibitors to synapsin I inferred from pair-wise binding site similarity measurements. PLoS ONE 5:e12214 10.1371/journal.pone.0012214 [OpenAIRE] [DOI]

Ehrt C.Brinkjost T.Koch O. (2016). Impact of binding site comparisons on medicinal chemistry and rational molecular design. J. Med. Chem. 59 4121–4151. 10.1021/acs.jmedchem.6b00078 27046190 [OpenAIRE] [PubMed] [DOI]

Gaulton A.Hersey A.Nowotka M.Bento A. P.Chambers J.Mendez D. (2017). The ChEMBL database in 2017. Nucleic Acids Res. 45 D945–D954. 10.1093/nar/gkw1074 27899562 [OpenAIRE] [PubMed] [DOI]

Gregori-PuigjanéE.Mestres J. (2008). A ligand-based approach to mining the chemogenomic space of drugs. Comb. Chem. High Throughput Screen. 11 669–676.18795886 [PubMed]

Hall D. R.Kozakov D.Whitty A.Vajda S. (2015). Lessons from hot spot analysis for fragment-based drug discovery. Trends Pharmacol. Sci. 36 724–736. 10.1016/j.tips.2015.08.003 26538314 [OpenAIRE] [PubMed] [DOI]

46 references, page 1 of 4
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