Lee J, Bogyo M (2013) Target deconvolution techniques in modern phenotypic profiling. Curr Opin Chem Biol 17:118–126
Article
CAS
Google Scholar
Terstappen GC, Schlüpen C, Raggiaschi R, Gaviraghi G (2007) Target deconvolution strategies in drug discovery. Nat Rev Drug Discov 6:891–903
Article
CAS
Google Scholar
Burdine L, Kodadek T (2004) Target identification in chemical genetics: the (often) missing link. Chem Biol 11:593–597
Article
CAS
Google Scholar
Schirle M, Bantscheff M, Kuster B (2012) Mass spectrometry-based proteomics in preclinical drug discovery. Chem Biol 19:72–84
Article
CAS
Google Scholar
Rix U, Superti-Furga G (2009) Target profiling of small molecules by chemical proteomics. Nat Chem Biol 5:616–624
Article
CAS
Google Scholar
Raida M (2011) Drug target deconvolution by chemical proteomics. Curr Opin Chem Biol 15:570–575
Article
CAS
Google Scholar
Feng Y, Mitchison TJ, Bender A, Young DW, Tallarico JA (2009) Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds. Nat Rev Drug Discov 8:567–578
Article
CAS
Google Scholar
Weaver S, Gleeson MP (2008) The importance of the domain of applicability in QSAR modeling. J Mol Graph Model 26:1315–1326
Article
CAS
Google Scholar
Cuatrecasas P, Wilchek M, Anfinsen CB (1968) Selective enzyme purification by affinity chromatography. Proc Natl Acad Sci USA 61:636–643
Article
CAS
Google Scholar
Schenone M, Dančík V, Wagner BK, Clemons PA (2013) Target identification and mechanism of action in chemical biology and drug discovery. Nat Chem Biol 9:232–240
Article
CAS
Google Scholar
Bender A, Young DW, Jenkins JL, Serrano M, Mikhailov D, Clemons PA, Davies JW (2007) Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprint. Comb Chem High Throughput Screen 10:719–731
Article
CAS
Google Scholar
Koutsoukas A, Simms B, Kirchmair J, Bond PJ, Whitmore AV, Zimmer S, Young MP, Jenkins JL, Glick M, Glen RC, Bender A (2011) From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteomics 74:2554–2574
Article
CAS
Google Scholar
Ji ZL, Wang Y, Yu L, Han LY, Zheng CJ, Chen YZ (2006) In silico search of putative adverse drug reaction related proteins as a potential tool for facilitating drug adverse effect prediction. Toxicol Lett 164:104–112
Article
CAS
Google Scholar
Bender A, Scheiber J, Glick M, Davies JW, Azzaoui K, Hamon J, Urban L, Whitebread S, Jenkins JL (2007) Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem 2:861–873
Article
CAS
Google Scholar
Poroikov V, Akimov D, Shabelnikova E, Filimonov D (2001) Top 200 medicines: can new actions be discovered through computer-aided prediction? SAR QSAR Environ Res 12:327–344
Article
CAS
Google Scholar
Tetko IV, Bruneau P, Mewes HW, Rohrer DC, Poda GI (2006) Can we estimate the accuracy of ADME-Tox predictions? Drug Discov Today 11:700–707
Article
CAS
Google Scholar
Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkins JL, Lavan P, Weber E, Doak AK, Côté S, Shoichet BK, Urban L (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature 486:361–367
CAS
Google Scholar
Gregori-Puigjané E, Mestres J (2008) A ligand-based approach to mining the chemogenomic space of drugs. Comb Chem High Throughput Screen 11:669–676
Article
Google Scholar
Jacob L, Hoffmann B, Stoven V, Vert JP (2008) Virtual screening of GPCRs: an in silico chemogenomics approach. BMC Bioinform 9:363
Article
Google Scholar
Jenkins JL, Bender A, Davies JW (2007) In silico target fishing: predicting biological targets from chemical structure. Drug Discov Today Technol 3:413–421
Article
Google Scholar
Lagunin A, Stepanchikova A, Filimonov D, Poroikov V (2000) PASS: prediction of activity spectra for biologically active substances. Bioinformatics 16:747–748
Article
CAS
Google Scholar
Nettles JH, Jenkins JL, Bender A, Deng Z, Davies JW, Glick M (2006) Bridging chemical and biological space: “target fishing” using 2D and 3D molecular descriptors. J Med Chem 49:6802–6810
Article
CAS
Google Scholar
Rognan D (2010) Structure-based approaches to target fishing and ligand profiling. Mol Inform 29:176–187
Article
CAS
Google Scholar
Chen X, Ung CY, Chen Y (2003) Can an in silico drug-target search method be used to probe potential mechanisms of medicinal plant ingredients? Nat Prod Rep 20:432–444
Article
CAS
Google Scholar
Gao Z, Li H, Zhang H, Liu X, Kang L, Luo X, Zhu W, Chen K, Wang X, Jiang H (2008) PDTD: a web-accessible protein database for drug target identification. BMC Bioinform 9:104
Article
Google Scholar
Bender A, Mikhailov D, Glick M, Scheiber J, Davies JW, Cleaver S, Marshall S, Tallarico JA, Harrington E, Cornella-Taracido I, Jenkins JL (2009) Use of ligand based models for protein domains to predict novel molecular targets and applications to triage affinity chromatography data. J Proteome Res 8:2575–2585
Article
CAS
Google Scholar
Cleves AE, Jain AN (2006) Robust ligand-based modeling of the biological targets of known drugs. J Med Chem 49:2921–2938
Article
CAS
Google Scholar
Nigsch F, Bender A, Jenkins JL, Mitchell JB (2008) Ligand-target prediction using Winnow and naive Bayesian algorithms and the implications of overall performance statistics. J Chem Inf Model 48:2313–2325
Article
CAS
Google Scholar
Wang L, Ma C, Wipf P, Liu H, Su W, Xie XQ (2013) TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. AAPS J 15:395–406
Article
CAS
Google Scholar
Maggiora G, Vogt M, Stumpfe D, Bajorath J (2014) Molecular similarity in medicinal chemistry. J Med Chem 57:3186–3204
Article
CAS
Google Scholar
Bender A, Glen RC (2004) Molecular similarity: a key technique in molecular informatics. Org Biomol Chem 2:3204–3218
Article
CAS
Google Scholar
Schuffenhauer A, Floersheim P, Acklin P, Jacoby E (2003) Similarity metrics for ligands reflecting the similarity of the target proteins. J Chem Inf Comput Sci 43:391–405
Article
CAS
Google Scholar
Bender A, Jenkins JL, Scheiber J, Sukuru SC, Glick M, Davies JW (2009) How similar are similarity searching methods? A principal component analysis of molecular descriptor space. J Chem Inf Model 49:108–119
Article
CAS
Google Scholar
Birchall K, Gillet VJ, Harper G, Pickett SD (2006) Training similarity measures for specific activities: application to reduced graphs. J Chem Inf Model 46:577–586
Article
CAS
Google Scholar
Willett P, Barnard JM, Downs GM (1998) Chemical similarity searching. J Chem Inf Comput Sci 38:983–996
Article
CAS
Google Scholar
Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK (2007) Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25:197–206
Article
CAS
Google Scholar
DeGraw AJ, Keiser MJ, Ochocki JD, Shoichet BK, Distefano MD (2010) Prediction and evaluation of protein farnesyltransferase inhibition by commercial drugs. J Med Chem 53:2464–2471
Article
CAS
Google Scholar
Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, Jensen NH, Kuijer MB, Matos RC, Tran TB, Whaley R, Glennon RA, Hert J, Thomas KL, Edwards DD, Shoichet BK, Roth BL (2009) Predicting new molecular targets for known drugs. Nature 462:175–181
Article
CAS
Google Scholar
Koutsoukas A, Lowe R, Kalantarmotamedi Y, Mussa HY, Klaffke W, Mitchell JB, Glen RC, Bender A (2013) In silico target predictions: defining a benchmarking data set and comparison of performance of the multiclass Naïve Bayes and Parzen-Rosenblatt window. J Chem Inf Model 53:1957–1966
Article
CAS
Google Scholar
Nidhi, Glick M, Davies JW, Jenkins JL (2006) Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J Chem Inf Model 46:1124–1133
Article
CAS
Google Scholar
Bender A, Mussa HY, Glen RC, Reiling S (2004) Molecular similarity searching using atom environments, information-based feature selection, and a naïve Bayesian classifier. J Chem Inf Comput Sci 44:170–178
Article
CAS
Google Scholar
Plewczynski D, von Grotthuss M, Spieser SA, Rychlewski L, Wyrwicz LS, Ginalski K, Koch U (2007) Target specific compound identification using a support vector machine. Comb Chem High Throughput Screen 10:189–196
Article
CAS
Google Scholar
Naive Bayes classifiers. https://www.cs.ubc.ca/~murphyk/Teaching/CS340-Fall06/reading/NB.pdf. Accessed 1 Oct 2015
Olah M, Mracec M, Ostopovici L, Rad R, Bora A, Hadaruga N, Olah I, Banda M, Simon Z, Mracec M (2004) WOMBAT: world of molecular bioactivity. Chemoinform Drug Discov 1:223–239
Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107
Article
CAS
Google Scholar
Liggi S, Drakakis G, Koutsoukas A, Cortes-Ciriano I, Martínez-Alonso P, Malliavin TE, Velazquez-Campoy A, Brewerton SC, Bodkin MJ, Evans DA, Glen RC, Carrodeguas JA, Bender A (2014) Extending in silico mechanism-of-action analysis by annotating targets with pathways: application to cellular cytotoxicity readouts. Future Med Chem 6:2029–2056
Article
CAS
Google Scholar
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29
Article
CAS
Google Scholar
Lomax J (2005) Get ready to GO! A biologist’s guide to the Gene Ontology. Brief Bioinform 6:298–304
Article
CAS
Google Scholar
Kanehisa M (2002) The KEGG database. Novartis Found Symp 247:91–101 (discussion 101–103, 119–128, 244–152)
Article
CAS
Google Scholar
Tanabe M, Kanehisa M (2012) Using the KEGG database resource. Curr Protoc Bioinform 1:1–12
Google Scholar
Drakakis G, Koutsoukas A, Brewerton SC, Evans DD, Bender A (2013) Using machine learning techniques for rationalising phenotypic readouts from a rat sleeping model. J Cheminform 5:1
Article
Google Scholar
RDKit: Cheminformatics and Machine Learning Software (2013). http://www.rdkit.org. Accessed 1 Oct 2015
ChemAxon Standardizer. https://www.chemaxon.com/products/standardizer/. Accessed 1 Oct 2015
Entrez Programming Utilities Help. http://www.ncbi.nlm.nih.gov/books/NBK25499/. Accessed 1 Oct 2015
Coordinators NR (2013) Database resources of the national center for biotechnology information. Nucleic Acids Res 41:D8–D20
Article
Google Scholar
The E-utilities in-depth: parameters, syntax and more. http://www.ncbi.nlm.nih.gov/books/NBK25499/. Accessed 1 Oct 2015
NCBI (2007) PubChem PUG Help
Bolton EE, Wang Y, Thiessen PA, Bryant SH (2008) PubChem: integrated platform of small molecules and biological activities. Annu Rep Comput Chem 4:217–241
Article
CAS
Google Scholar
Austin CP, Brady LS, Insel TR, Collins FS (2004) NIH molecular libraries initiative. Science 306:1138–1139
Article
CAS
Google Scholar
McCarthy A (2010) The NIH Molecular Libraries Program: identifying chemical probes for new medicines. Chem Biol 17:549–550
Article
CAS
Google Scholar
Hudson BD, Hyde RM, Rahr E, Wood J, Osman J (1996) Parameter based methods for compound selection from chemical databases. Quant Struct-Act Relat 15:285–289
Article
CAS
Google Scholar
Gobbi A, Lee M-L (2003) DISE: directed sphere exclusion. J Chem Inf Comput Sci 43:317–323
Article
CAS
Google Scholar
Glem RC, Bender A, Arnby CH, Carlsson L, Boyer S, Smith J (2006) Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. IDrugs 9:199–204
Google Scholar
Wale N, Karypis G (2009) Target fishing for chemical compounds using target-ligand activity data and ranking based methods. J Chem Inf Model 49:2190–2201
Article
CAS
Google Scholar
Smusz S, Kurczab R, Bojarski AJ (2013) The influence of the inactives subset generation on the performance of machine learning methods. J Cheminform 5:17
Article
CAS
Google Scholar
Zhang H (2004) The optimality of naive Bayes. In: Proceedings of the 17th International FLAIRS conference (FLAIRS2004). AAAI Press, Menlo Park, CA
Kurczab R, Smusz S, Bojarski AJ (2014) The influence of negative training set size on machine learning-based virtual screening. J Cheminform 6:32
Article
Google Scholar
Alpaydin E (2004) Introduction to machine learning, MIT press
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V (2011) Scikit-learn: Machine learning in Python. Journal Mach Learn Res 12:2825–2830
Google Scholar
Schneider K-M (2004) On word frequency information and negative evidence in Naive Bayes text classification. In: González JLV, Martínez-Barco P, Muñoz R, Saiz-Noeda M (eds) Advances in natural language processing, Alicante, Spain. Springer, Heidelberg, pp 474–485
Drakakis G, Koutsoukas A, Brewerton SC, Bodkin MJ, Evans DA, Bender A (2015) Comparing Global and Local Likelihood Score Thresholds in Multiclass Laplacian-Modified Naïve Bayes Protein Target Prediction. Comb Chem High Throughput Screen 18:323–330
Article
CAS
Google Scholar
Olah M, Rad R, Ostopovici L, Bora A, Hadaruga N, Hadaruga D, Moldovan R, Fulias A, Mracec M, Oprea TI (2007) WOMBAT and WOMBAT-PK: bioactivity databases for lead and drug discovery. In: Schreiber SL, Kapoor TM, Wess G, (eds) Chemical biology: from small molecules to systems biology and drug design. Wiley, Weinheim, Germany, pp 760–786
Dimitrov S, Dimitrova G, Pavlov T, Dimitrova N, Patlewicz G, Niemela J, Mekenyan O (2005) A stepwise approach for defining the applicability domain of SAR and QSAR models. J Chem Inf Model 45:839–849
Article
CAS
Google Scholar
Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicabilty domain estimation by projection of the training set descriptor space: a review. Altern Lab Anim 33:445–459
CAS
Google Scholar
Applicability domain of QSAR models. https://mediatum.ub.tum.de/doc/1004002/1004002.pdf. Accessed 1 Oct 2015