Generic placeholder image

Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Review Article

Five Years of the KNIME Vernalis Cheminformatics Community Contribution

Author(s): Stephen D. Roughley*

Volume 27, Issue 38, 2020

Page: [6495 - 6522] Pages: 28

DOI: 10.2174/0929867325666180904113616

open access plus

Abstract

Since the official release as a KNIME Community Contribution in June 2013, the Vernalis KNIME nodes have increased from a single node (the ‘PDB Connector’ node) to around 126 nodes (November 2017; Version 1.12.0); furthermore, a number of nodes have been adopted into the core KNIME product. In this review, we provide a brief timeline of the development of the current public release and an overview of the current nodes. We will focus in more detail on three particular areas: nodes accessing publicly available information via web services, nodes providing cheminformatics functionality without recourse to a cheminformatics toolkit, and nodes using one of the cheminformatics toolkits present in KNIME. We will conclude with a number of case studies demonstrating the use of KNIME at Vernalis.

Keywords: KNIME Community Contribution, Cheminformatics, Matched Molecular Pairs (MMP, MMPA), Protein Data Bank (PDB), Sequences, Fingerprints, SMILES, Principal Moments of Inertia (PMI).

[1]
Liu, R.; Li, X.; Lam, K.S. Combinatorial chemistry in drug discovery. Curr. Opin. Chem. Biol., 2017, 38, 117-126.
[http://dx.doi.org/10.1016/j.cbpa.2017.03.017] [PMID: 28494316]
[2]
Mullard, A. 2016 FDA drug approvals. Nat. Rev. Drug Discov., 2017, 16(2), 73-76.
[http://dx.doi.org/10.1038/nrd.2017.14] [PMID: 28148938]
[3]
Maxmen, A. Busting the billion-dollar myth: how to slash the cost of drug development. Nature, 2016, 536(7617), 388-390.
[http://dx.doi.org/10.1038/536388a] [PMID: 27558048]
[4]
Paul, S.M.; Mytelka, D.S.; Dunwiddie, C.T.; Persinger, C.C.; Munos, B.H.; Lindborg, S.R.; Schacht, A.L. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov., 2010, 9(3), 203-214.
[http://dx.doi.org/10.1038/nrd3078] [PMID: 20168317]
[5]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[6]
The RCSB protein data bank Available from: http://www.rcsb.org
[7]
Bento, A.P.; Gaulton, A.; Hersey, A.; Bellis, L.J.; Chambers, J.; Davies, M.; Krüger, F.A.; Light, Y.; Mak, L.; McGlinchey, S.; Nowotka, M.; Papadatos, G.; Santos, R.; Overington, J.P. The ChEMBL bioactivity database: an update. Nucleic Acids Res., 2014, 42(Database issue), D1083-D1090.
[http://dx.doi.org/10.1093/nar/gkt1031] [PMID: 24214965]
[8]
ChEMBL Available from: https://www.ebi.ac.uk/chembl/ (Accessed Date: 12th December 2017).
[9]
Chambers, J.; Davies, M.; Gaulton, A.; Hersey, A.; Velankar, S.; Petryszak, R.; Hastings, J.; Bellis, L.; McGlinchey, S.; Overington, J.P. UniChem: a unified chemical structure cross-referencing and identifier tracking system. J. Cheminform., 2013, 5(1), 3.
[http://dx.doi.org/10.1186/1758-2946-5-3] [PMID: 23317286]
[10]
Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S.H. pubchem substance and compound databases. Nucleic Acids Res., 2016, 44(D1), D1202-D1213.
[http://dx.doi.org/10.1093/nar/gkv951] [PMID: 26400175]
[11]
The PubChem Project Available from: https://pubchem.ncbi.nlm.nih.gov/
[12]
Wang, Y.; Bryant, S.H.; Cheng, T.; Wang, J.; Gindulyte, A.; Shoemaker, B.A.; Thiessen, P.A.; He, S.; Zhang, J. PubChem BioAssay: 2017 update. Nucleic Acids Res., 2017, 45(D1), D955-D963.
[http://dx.doi.org/10.1093/nar/gkw1118] [PMID: 27899599]
[13]
Agarwala, R.; Barrett, T.; Beck, J.; Benson, D.A.; Bollin, C.; Bolton, E.; Bourexis, D.; Brister, J.R.; Bryant, S.H.; Canese, K.; Charowhas, C.; Clark, K.; DiCuccio, M.; Dondoshansky, I.; Federhen, S.; Feolo, M.; Funk, K.; Geer, L.Y.; Gorelenkov, V.; Hoeppner, M.; Holmes, B.; Johnson, M.; Khotomlianski, V.; Kimchi, A.; Kimelman, M.; Kitts, P.; Klimke, W.; Krasnov, S.; Kuznetsov, A.; Landrum, M.J.; Landsman, D.; Lee, J.M.; Lipman, D.J.; Lu, Z.; Madden, T.L.; Madej, T.; Marchler-Bauer, A.; Karsch-Mizrachi, I.; Murphy, T.; Orris, R.; Ostell, J.; O’Sullivan, C.; Panchenko, A.; Phan, L.; Preuss, D.; Pruitt, K.D.; Rodarmer, K.; Rubinstein, W.; Sayers, E.W.; Schneider, V.; Schuler, G.D.; Sherry, S.T.; Sirotkin, K.; Siyan, K.; Slotta, D.; Soboleva, A.; Soussov, V.; Starchenko, G.; Tatusova, T.A.; Todorov, K.; Trawick, B.W.; Vakatov, D.; Wang, Y.; Ward, M.; Wilbur, W.J.; Yaschenko, E.; Zbicz, K. NCBI Resource Coordinators. NCBI resource coordinators. Database resources of the national center for biotechnology information. Nucleic Acids Res., 2016, 44(D1), D7-D19.
[http://dx.doi.org/10.1093/nar/gkv1290] [PMID: 26615191]
[14]
PubMed Central. Available from: https://www.ncbi. nlm.nih.gov/pmc/ (Accessed Date: 12th December 2017).
[15]
Benson, D.A.; Karsch-Mizrachi, I.; Lipman, D.J.; Ostell, J.; Wheeler, D.L. GenBank. Nucleic Acids Res., 2005, 33(Database issue), D34-D38.
[http://dx.doi.org/10.1093/nar/gki063] [PMID: 15608212]
[16]
GenBank. Available from: https://www.ncbi.nlm.nih.gov/genbank/ (Accessed Date: 12th December 2017).
[17]
CAS - The chemical abstracts service. Available from: https://www.cas.org/ (Accessed Date: 12th December 2017).
[18]
Elsevier Reaxys Available from: https://www.elsevier.com/solutions/reaxys (Accessed Date: 12th December 2017).
[19]
Stephens, Z.D.; Lee, S.Y.; Faghri, F.; Campbell, R.H.; Zhai, C.; Efron, M.J.; Iyer, R.; Schatz, M.C.; Sinha, S.; Robinson, G.E. Big data: astronomical or genomical? PLoS Biol., 2015, 13(7)e1002195
[http://dx.doi.org/10.1371/journal.pbio.1002195] [PMID: 26151137]
[20]
Larsen, P.O.; von Ins, M. The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index. Scientometrics, 2010, 84(3), 575-603.
[http://dx.doi.org/10.1007/s11192-010-0202-z] [PMID: 20700371]
[21]
Chessari, G.; Woodhead, A.J. From fragment to clinical candidate--a historical perspective. Drug Discov. Today, 2009, 14(13-14), 668-675.
[http://dx.doi.org/10.1016/j.drudis.2009.04.007] [PMID: 19427404]
[22]
Congreve, M.; Chessari, G.; Tisi, D.; Woodhead, A.J. Recent developments in fragment-based drug discovery. J. Med. Chem., 2008, 51(13), 3661-3680.
[http://dx.doi.org/10.1021/jm8000373] [PMID: 18457385]
[23]
Albert, J.S.; Blomberg, N.; Breeze, A.L.; Brown, A.J.; Burrows, J.N.; Edwards, P.D.; Folmer, R.H.; Geschwindner, S.; Griffen, E.J.; Kenny, P.W.; Nowak, T.; Olsson, L.L.; Sanganee, H.; Shapiro, A.B. An integrated approach to fragment-based lead generation: philosophy, strategy and case studies from AstraZeneca’s drug discovery programmes. Curr. Top. Med. Chem., 2007, 7(16), 1600-1629.
[http://dx.doi.org/10.2174/156802607782341091] [PMID: 17979771]
[24]
Andrews, S.P.; Brown, G.A.; Christopher, J.A. Structure-based and fragment-based GPCR drug discovery. ChemMedChem, 2014, 9(2), 256-275.
[http://dx.doi.org/10.1002/cmdc.201300382] [PMID: 24353016]
[25]
Barker, J.; Courtney, S.; Hesterkamp, T.; Ullmann, D.; Whittaker, M. Fragment screening by biochemical assay. Expert Opin. Drug Discov., 2006, 1(3), 225-236.
[http://dx.doi.org/10.1517/17460441.1.3.225] [PMID: 23495844]
[26]
Roughley, S.D.; Hubbard, R.E. How well can fragments explore accessed chemical space? A case study from heat shock protein 90. J. Med. Chem., 2011, 54(12), 3989-4005.
[http://dx.doi.org/10.1021/jm200350g] [PMID: 21561141]
[27]
Davis, B.J.; Roughley, S.D. Fragment-based lead discoveryin: Platform technologies in drug discovery and validation; Goodnow, R.A.Jr., Ed.; Academic Press, ; , 2017, 50, pp. 371-439.
[http://dx.doi.org/10.1016/bs.armc.2017.07.002]
[28]
Fink, T.; Bruggesser, H.; Reymond, J.L. Virtual exploration of the small-molecule chemical universe below 160 Daltons. Angew. Chem. Int. Ed. Engl., 2005, 44(10), 1504-1508.
[http://dx.doi.org/10.1002/anie.200462457] [PMID: 15674983]
[29]
Fink, T.; Reymond, J.L. Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. J. Chem. Inf. Model., 2007, 47(2), 342-353.
[http://dx.doi.org/10.1021/ci600423u] [PMID: 17260980]
[30]
Blum, L.C.; Reymond, J.L. 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. J. Am. Chem. Soc., 2009, 131(25), 8732-8733.
[http://dx.doi.org/10.1021/ja902302h] [PMID: 19505099]
[31]
Ruddigkeit, L.; van Deursen, R.; Blum, L.C.; Reymond, J.L. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J. Chem. Inf. Model., 2012, 52(11), 2864-2875.
[http://dx.doi.org/10.1021/ci300415d] [PMID: 23088335]
[32]
For ChEMBL data see Supporting Information, 2012.
[33]
RCSB PDB - Content Growth Report Available from: https://www.rcsb.org/pdb/statistics/contentGrowthChart.do?content=total&seqid=100 (Accessed Date: 12th December 2017).
[34]
GenBank and WGS Statistics Available from: https://www.ncbi.nlm.nih.gov/genbank/statistics/ (Accessed Date: 12th December 2017).
[35]
Berthold, M.R.; Cebron, N.; Dill, F.; Gabriel, T.R.; Kötter, T.; Meinl, T.; Ohl, P.; Sieb, C.; Thiel, K.; Wiswedel, B. Data analysis, machine learning and applications Proceedings of the 31st Annual Conference of the Gesell-schaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, , 2007, pp. 319-326.
[36]
KNIME Open for Innovation, Available from: https://www.knime.org/ (Accessed Date: 12th December 2017).
[37]
Berthold, M.R.; Cebron, N.; Dill, F.; Gabriel, T.R.; Kötter, T.; Meinl, T.; Ohl, P.; Thiel, K.; Wiswedel, B. KNIME - the Konstanz information miner: version 2.0 and beyond. SIGKDD Explor., 2009, 11(1), 26-31.
[http://dx.doi.org/10.1145/1656274.1656280]
[38]
Saubern, S.; Guha, R.; Baell, J.B. KNIME workflow to assess PAINS filters in SMARTS format. Comparison of RDKit and indigo cheminformatics libraries. Mol. Inform., 2011, 30(10), 847-850.
[http://dx.doi.org/10.1002/minf.201100076] [PMID: 27468104]
[39]
BIOVIA Draw | CTfile Formats Available from: http://accelrys.com/products/collaborative-science/biovia-draw/ctfile-no-fee.html (Accessed Date: 12th December 2017).
[40]
Dalby, A.; Nourse, J.G.; Hounshell, W.D.; Gushurst, A.K.I.; Grier, D.L.; Leland, B.A.; Laufer, J. Description of several chemical structure file formats used by computer programs developed at molecular design limited. J. Chem. Inf. Comput. Sci., 1992, 32(3), 244-255.
[http://dx.doi.org/10.1021/ci00007a012]
[41]
Daylight Theory, Available from: http://www.daylight.com/dayhtml/doc/theory/theory.smiles.html (Accessed Date: 12th December 2017).
[42]
James, C.A. OpenSMILES specification, Available from: http://opensmiles.org/opensmiles.html(Accessed Date: 12th December 2017)..
[43]
Daylight Theory, Available from: http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html(Accessed Date: 12th December 2017)..
[44]
InChI Trust Available from: http://www.inchi-trust.org(Accessed Date: 12th December 2017)..
[45]
Mol2 File Format - SYBYL-X - Certara Confluence 2017. Available from: https://tools.certara.com/confluence/display/SYB/Mol2+File+Format (Accessed Date: 12th December 2017).
[46]
2017. Available from: http://www.wwpdb.org/documentation/file-format (Accessed Date: 12th December 2017).
[47]
Landrum, G. RDKit 2017. Available from: http://www.rdkit.org/ (Accessed Date: 12th December 2017).
[48]
PMML 4.3 - General Structure 2017. Available from: http://dmg. org/pmml/v4-3/GeneralStructure.html (Accessed Date: 12th December 2017).
[49]
SOAP Specifications 2017. Available from: https://www.w3.org/TR/soap/ (Accessed Date: 12th December 2017).
[50]
Web Services Description Language (WSDL) Version 2.0 Part 1: Core Language 2017. Available from: https://www.w3.org/TR/wsdl20/ (Accessed Date: 12th December 2017).
[51]
Enspiral Discovery 2017. Available from: http://www.enspiral-discovery.com (Accessed Date: 12th December 2017).
[52]
The GNU General Public License v3.0 2017. Available from: https://www.gnu.org/licenses/gpl-3.0.en.html (Accessed Date: 12th December 2017).
[53]
Vernalis PDB Connector node 2017. Available from: https://www.knime.com/forum/knime-general/vernalis-pdb-connector-node (Accessed Date: 12th December 2017).
[54]
Community Contributions. 2017. Available from: https://www.knime.com/community (Accessed Date: 12th December 2017).
[55]
KNIME User Day UK. 2017. Available from: https://www. knime.com/about/events/knime-user-day-uk-2013 (Accessed Date: 12th December 2017).
[56]
Noding Guidelines v2.5 2017. Available from: https://files.knime. com/sites/default/files/inline-images/noding_guidelines.pdf (Accessed Date: 12th December 2017).
[57]
Trusted Community Contributions. 2017. Available from: https://www.knime.com/trusted-community-contributions (Accessed Date: 12th December 2017).
[58]
Vernalis Nodes for KNIME 2017. Available from: https://www.knime.com/book/vernalis-nodes-for-knime-trusted-extension (Accessed Date: 12th December 2017).
[59]
Vernalis release notes/changelog 2017. Available from: https://www. knime.com/book/vernalis-release-notes-changelog (Accessed Date: 12th December 2017).
[60]
RDKit Nodes for KNIME 2017. Available from: https://www.knime.com/rdkit (Accessed Date: 12th December 2017).
[61]
CDK Nodes for KNIME 2017. Available from: https://www.knime.com/community/cdk (Accessed Date: 12th December 2017).
[62]
Chemistry Development Kit. 2017. Available from: https://cdk.github.io/ (Accessed Date: 12th December 2017).
[63]
ChemAxon Node for KNIME JChem Extensions_English 2017. Available from: http://infocom-science.jp/product/detail/jchemextensions_english.html (Accessed Date: 12th December 2017).
[64]
Free Marvin Chemistry Extensions. 2017. Available from: https://www.knime.com/free-marvin-chemistry-extensions (Accessed Date: 12th December 2017).
[65]
Indigo Nodes for KNIME. 2017. Available from: https://www.knime. com/community/indigo (Accessed Date: 12th December 2017).
[66]
Indigo Toolkit. 2017. Available from: http://lifescience.open-source.epam.com/indigo/ (Accessed Date: 12th December 2017).
[67]
SIG ChemInf. 2017. Available from: https://www.knime.com/sig-cheminf (Accessed Date: 12th December 2017).
[68]
What's New in KNIME 2.12 - Sleep/Pause/Timer 2017. Available from: https://www.knime.com/whats-new-in-knime-212#Sleep (Accessed Date: 12th December 2017).
[69]
New Loop Ends. 2017. Available from: https://www.knime.com/forum/vernalis/new-loop-ends (Accessed Date: 12th December 2017).
[70]
Workflow timing 2017. Available from: https://www.knime.com/forum/knime-general/workflow-timing (Accessed Date: 12th December 2017).
[71]
Timing Workflows. 2017. Available from: https://www.knime.com/forum/knime-labs-general/timing-workflows (Accessed Date: 12th December 2017).
[72]
RCSB PDB - Advanced Search. 2017. Available from: https://www.rcsb.org/pdb/search/advSearch.do (Accessed Date: 12th December 2017).
[73]
RCSB PDB - REST Web Service - Search. 2017. Available from: https://www.rcsb.org/pdb/software/rest.do#search (Accessed Date: 12th December 2017).
[74]
RCSB Protein Data Bank - Web Service to retrieve custom report 2017. Available from: https://www.rcsb.org/pdb/software/ (Accessed Date: 12th December 2017).
[75]
RCSB PDB - REST Web Service - Describe components 2017. Available from: https://www.rcsb.org/pdb/software/rest.do#descComp (Accessed Date: 12th December 2017).
[76]
RCSB PDB - REST Web Service - SMILES Search. 2017. Available from: https://www.rcsb.org/pdb/software/rest.do#smiles (Accessed Date: 12th December 2017).
[77]
Gou, Y.; Graff, F.; Kilian, O.; Kafkas, S.; Katuri, J.; Kim, J-H.; Marinos, N.; McEntyre, J.; Morrison, A.; Pi, X.; Rossiter, P.; Talo, F.; Vartak, V.; Coleman, L-A.; Hawkins, C.; Kinsey, A.; Mansoor, S.; Morris, V.; Rowbotham, R.; Chaplin, D.; MacIntyre, R.; Patel, Y.; Ananiadou, S.; Black, W.J.; McNaught, J.; Rak, R.; Rowley, A.; Europe, P.M.C. Europe PMC Consortium. Europe PMC: a full-text literature database for the life sciences and platform for innovation. Nucleic Acids Res., 2015, 43(Database issue), D1042-D1048.
[http://dx.doi.org/10.1093/nar/gku1061] [PMID: 25378340]
[78]
Europe, P.M.C. 2017. Available from: https://europepmc.org/ (Accessed Date: 12th December 2017).
[79]
Advanced Search - Europe PMC. 2017. Available from: https://europepmc.org/advancesearch (Accessed Date: 12th December 2017).
[80]
Schomburg, K.; Ehrlich, H-C.; Stierand, K.; Rarey, M. From structure diagrams to visual chemical patterns. J. Chem. Inf. Model., 2010, 50(9), 1529-1535.
[http://dx.doi.org/10.1021/ci100209a] [PMID: 20795706]
[81]
SMARTSviewer. 2017. Available from: http://smartsview.zbh.uni-hamburg.de/ (Accessed Date: 12th December 2017).
[82]
Willett, P. Similarity searching using 2D structural fingerprints in: Chemoinformatics and Computational Chemical Biology; Bajorath, J., Ed.; Humana Press: Totowa, NJ, 2011, pp. 133-158.
[83]
Willett, P. Similarity-based virtual screening using 2D fingerprints. Drug Discov. Today, 2006, 11(23-24), 1046-1053.
[http://dx.doi.org/10.1016/j.drudis.2006.10.005] [PMID: 17129822]
[84]
Raymond, J.W.; Blankley, C.J.; Willett, P. Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures. J. Mol. Graph. Model., 2003, 21(5), 421-433.
[http://dx.doi.org/10.1016/S1093-3263(02)00188-2] [PMID: 12543138]
[85]
Raymond, J.W.; Willett, P. Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases. J. Comput. Aided Mol. Des., 2002, 16(1), 59-71.
[http://dx.doi.org/10.1023/A:1016387816342] [PMID: 12197666]
[86]
Wiswedel, B. Streaming data in KNIME, 2017. Available from: https://www.knime.com/blog/streaming-data-in-knime
[87]
The value returned is the first member of a Java HashSet containing the individual components See "Java 8 JavaDoc: HashSet, 2017. Available from: https://docs.oracle.com/javase/8/docs/api/java/util/HashSet.html
[88]
ChEMBL21 download 2012. Available at: (Accessed Date: 12th December 2017).
[http://dx.doi.org/10.6019/CHEMBL]
[89]
Bellamacina, C.R.; Le, V.; Shu, W.; Burger, M.T.; Bussiere, D. Pim1 complexed with a pyridylcarboxamide. PDB ID 4N70, 2014.
[90]
Burger, M.T.; Han, W.; Lan, J.; Nishiguchi, G.; Bellamacina, C.; Lindval, M.; Atallah, G.; Ding, Y.; Mathur, M.; McBride, C.; Beans, E.L.; Muller, K.; Tamez, V.; Zhang, Y.; Huh, K.; Feucht, P.; Zavorotinskaya, T.; Dai, Y.; Holash, J.; Castillo, J.; Langowski, J.; Wang, Y.; Chen, M.Y.; Garcia, P.D. structure guided optimization, in vitro activity, and in vivo activity of pan-PIM kinase inhibitors. ACS Med. Chem. Lett., 2013, 4(12), 1193-1197.
[http://dx.doi.org/10.1021/ml400307j] [PMID: 24900629]
[91]
Brough, P.A.; Barril, X.; Borgognoni, J.; Chene, P.; Davies, N.G.; Davis, B.; Drysdale, M.J.; Dymock, B.; Eccles, S.A.; Garcia-Echeverria, C.; Fromont, C.; Hayes, A.; Hubbard, R.E.; Jordan, A.M.; Jensen, M.R.; Massey, A.; Merrett, A.; Padfield, A.; Parsons, R.; Radimerski, T.; Raynaud, F.I.; Robertson, A.; Roughley, S.D.; Schoepfer, J.; Simmonite, H.; Sharp, S.Y.; Surgenor, A.; Valenti, M.; Walls, S.; Webb, P.; Wood, M.; Workman, P.; Wright, L. PDB ID. Orally active 2-amino thienopyrimidine inhibitors of the hsp90 chaperone. Med. Chem., 2009, 52(15), 4794-4809.
[http://dx.doi.org/10.1021/jm900357y]
[92]
Brough, P.A.; Barril, X.; Borgognoni, J.; Chene, P.; Davies, N.G.; Davis, B.; Drysdale, M.J.; Dymock, B.; Eccles, S.A.; Garcia-Echeverria, C.; Fromont, C.; Hayes, A.; Hubbard, R.E.; Jordan, A.M.; Jensen, M.R.; Massey, A.; Merrett, A.; Padfield, A.; Parsons, R.; Radimerski, T.; Raynaud, F.I.; Robertson, A.; Roughley, S.D.; Schoepfer, J.; Simmonite, H.; Sharp, S.Y.; Surgenor, A.; Valenti, M.; Walls, S.; Webb, P.; Wood, M.; Workman, P.; Wright, L. Combining hit identification strategies: fragment-based and in silico approaches to orally active 2-aminothieno[2,3-d]pyrimidine inhibitors of the Hsp90 molecular chaperone. J. Med. Chem., 2009, 52(15), 4794-4809.
[http://dx.doi.org/10.1021/jm900357y] [PMID: 19610616]
[93]
Lipman, D.J.; Pearson, W.R. Rapid and sensitive protein similarity searches. Science, 1985, 227(4693), 1435-1441.
[http://dx.doi.org/10.1126/science.2983426] [PMID: 2983426]
[94]
FASTA - Wikipedia. 2017. Available from: https://en.wikipedia.org/wiki/FASTA
[95]
Madden, T. The NCBI Handbook; National center for biotechnology information: (US); Be-thesda: MD, 2013.
[96]
Mathews, F.S.; Xia, Z.-X. Methanol dehydrogenase from methylophilus W3A1, 2011.
[97]
Xia, Z.; Dai, W.; Zhang, Y.; White, S.A.; Boyd, G.D.; Mathews, F.S. Determination of the gene sequence and the three-dimensional structure at 2.4 angstroms resolution of methanol dehydrogenase from Methylophilus W3A1. J. Mol. Biol., 1996, 259(3), 480-501.
[http://dx.doi.org/10.1006/jmbi.1996.0334] [PMID: 8676383]
[98]
Sauer, W.H.B.; Schwarz, M.K. Molecular shape diversity of combinatorial libraries: a prerequisite for broad bioactivity. J. Chem. Inf. Comput. Sci., 2003, 43(3), 987-1003.
[http://dx.doi.org/10.1021/ci025599w] [PMID: 12767158]
[99]
Erl Wood Cheminformatics nodes for KNIME 2017. Available from: https://www.knime.com/community/erlwood
[100]
Lumley, J.A. Deploying KNIME to the Enterprise: Reshaping Data Architecture for Healthcare, 2017 KNIME UGM 2017. Available from: https://files.knime.com/sites/default/files/jamesalu-mley_knime_ugm_berlin_march2017.pdf (Accessed 4rth March 2020).
[101]
Hussain, J.; Rea, C. Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J. Chem. Inf. Model., 2010, 50(3), 339-348.
[http://dx.doi.org/10.1021/ci900450m] [PMID: 20121045]
[102]
Papadatos, G.; Alkarouri, M.; Gillet, V.J.; Willett, P.; Kadirkamanathan, V.; Luscombe, C.N.; Bravi, G.; Richmond, N.J.; Pickett, S.D.; Hussain, J.; Pritchard, J.M.; Cooper, A.W.J.; Macdonald, S.J.F. Lead optimization using matched molecular pairs: inclusion of contextual information for enhanced prediction of HERG inhibition, solubility, and lipophilicity. J. Chem. Inf. Model., 2010, 50(10), 1872-1886.
[http://dx.doi.org/10.1021/ci100258p] [PMID: 20873842]
[103]
Wagener, M.; Lommerse, J.P.M. The quest for bioisosteric replacements. J. Chem. Inf. Model., 2006, 46(2), 677-685.
[http://dx.doi.org/10.1021/ci0503964] [PMID: 16562998]
[104]
ChEMBL. 2017. Available from: https://www.ebi.ac.uk/chembl (Accessed 12th December 2017)
[105]
KNIME Node Guide - Vernalis. 2017. Available from: https://www.knime.com/nodeguide/community/vernalis (Accessed 12th December 2017)
[106]
ChEMBL23 Download, 2012. Available at: http://CHEMBL.database.232017. (Accessed 12th December 2017)
[107]
RCSB PDB - Help - Latest Released Structures. 2017. Available from: https://www.rcsb.org/pdb/staticHelp.do?p=help/advancedsearch/latestReleasedStructures.html (Accessed 12th December 2017)
[108]
PyMOL. 2017. Available from: https://pymol.org/2/ (Accessed 12th December 2017)
[109]
The PyMOL molecular graphics system. Available at: https://ci.nii.ac.jp/naid/10020095229/ (Accessed 12th December 2017)
[110]
Murray, J.B.; Roughley, S.D.; Matassova, N.; Brough, P.A. Off-rate screening (ORS) by surface plasmon resonance. An efficient method to kinetically sample hit to lead chemical space from unpurified reaction products. J. Med. Chem., 2014, 57(7), 2845-2850.
[http://dx.doi.org/10.1021/jm401848a] [PMID: 24520903]
[111]
Brough, P.A.; Baker, L.; Bedford, S.; Brown, K.; Chavda, S.; Chell, V.; D’Alessandro, J.; Davies, N.G.; Davis, B.; Le Strat, L.; Macias, A.T.; Maddox, D.; Mahon, P.C.; Massey, A.J.; Matassova, N.; McKenna, S.; Meissner, J.W.; Moore, J.D.; Murray, J.B.; Northfield, C.J.; Parry, C.; Parsons, R.; Roughley, S.D.; Shaw, T.; Simmonite, H.; Stokes, S.; Surgenor, A.; Stefaniak, E.; Robertson, A.; Wang, Y.; Webb, P.; Whitehead, N.; Wood, M. Application of off-rate screening in the identification of novel pan-isoform inhibitors of pyruvate dehydrogenase kinase. J. Med. Chem., 2017, 60(6), 2271-2286.
[http://dx.doi.org/10.1021/acs.jmedchem.6b01478] [PMID: 28199108]
[112]
EMBL-EBI PDBe REST API. 2017. Available from: http://www.ebi.ac.uk/pdbe/pdbe-rest-api (Accessed 12th December 2017)
[113]
ChEMBL Web Services. 2017. Available from: https://www.ebi.ac.uk/chembl/ws (Accessed 12th December 2017)
[114]
Multivariate kernel density estimation – Wikipedia 2018. Available from: https://en.wikipedia.org/wiki/Multivariate_kernel_density_estimation (Accessed 12th December 2017)
[115]
Parzen, E. On Estimation of a probability density function and mode. Ann. Math. Stat., 1962, 33(3), 1065-1076.
[http://dx.doi.org/10.1214/aoms/1177704472]
[116]
Rosenblatt, M. Remarks on some nonparametric estimates of a density function. Ann. Math. Stat., 1956, 27(3), 832-837.
[http://dx.doi.org/10.1214/aoms/1177728190]
[117]
Wand, M.P.; Jones, M.C. Comparison of smoothing parameterizations in bivariate kernel density estimation. J. Am. Stat. Assoc., 1993, 88(422), 520-528.
[http://dx.doi.org/10.1080/01621459.1993.10476303]
[118]
The Jupyter Notebook-IPython. 2018. Available from: HTTPS://IPYTHON.ORG/NOTEBOOK.HTML (Accessed Date: 8 May, 2018).

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy