The impact of chemoinformatics on drug discovery in the pharmaceutical industry

References

• Medina-Franco JL, Maggiora GM. Molecular similarity analysis. In: Bajorath J, editor. Chemoinformatics for drug discovery. Wiley; 2013. p. 343–399.
   Google Scholar
• Varnek A, Baskin II. Chemoinformatics as a theoretical chemistry discipline. Mol Inform. 2011 Jan 17;30(1):20–32.
   PubMed Web of Science ®Google Scholar
• Begam BF, Kumar JS. A study on cheminformatics and its applications on modern drug discovery. Procedia Eng. 2012;38:1264–1275.
   Google Scholar
• Maggiora GM. Introduction to molecular similarity and chemical space. In: Martinez-Mayorga K, Medina-Franco JL, editors. Foodinformatics: applications of chemical information to food chemistry. Vol. 9783319102269. Switzerland: Springer International Publishing; 2014. p. 1–81.
   Google Scholar
• Brown FK. Chapter 35 - chemoinformatics: what is it and how does it impact drug discovery. In: Bristol JA, editor. Annual reports in medicinal chemistry. Vol. 33. Foster City, CA: Academic Press; 1998. p. 375–384.
   Google Scholar
• Paris G. (1999, August). Meeting of the american chemical society. Available from: http://www.warr.com/warrzone.htm.
   Google Scholar
• Gasteiger J, Funatsu K. Chemoinformatics: an important scientific discipline. J Comput Chem Japan. 2006;5(2):53–58.
   Google Scholar
• Wold S. Chemometrics; what do we mean with it, and what do we want from it? Chemometr Intell Lab Syst. 1995;30(1):109–115.
   Web of Science ®Google Scholar
• Hann M, Green R. Chemoinformatics — a new name for an old problem? Curr Opin Chem Biol. 1999;3(4):379–383.
   PubMed Web of Science ®Google Scholar
• Bajorath J. Understanding chemoinformatics: a unifying approach. Drug Discov Today. 2004;9(1):13–14.
   PubMed Web of Science ®Google Scholar
• Medina-Franco JL, Méndez-Lucio O, Martinez-Mayorga K. Chapter one - the interplay between molecular modeling and chemoinformatics to characterize protein–ligand and protein–protein interactions landscapes for drug discovery. In: Karabencheva-Christova T, editor. Advances in protein chemistry and structural biology. Vol. 96. Amsterdam, The Netherlands: Academic Press; 2014. p. 1–37.
   Google Scholar
• Rojas C, Duchowicz PR, Castro EA. Foodinformatics: quantitative structure-property relationship modeling of volatile organic compounds in peppers. J Food Sci. 2019;84(4):770–781.
   PubMed Web of Science ®Google Scholar
• Senderowitz H, Tropsha A. Materials informatics. J Chem Inf Model. 2018 Dec 24;58(12):2377–2379.
   PubMed Web of Science ®Google Scholar
• Nantasenamat C, Prachayasittikul V. Maximizing computational tools for successful drug discovery. Expert Opin Drug Discov. 2015;10(4):321–329.
   PubMed Web of Science ®Google Scholar
• Lo Y-C, Rensi SE, Torng W, et al. Machine learning in chemoinformatics and drug discovery. Drug Discov Today. 2018;23(8):1538–1546.
   PubMed Web of Science ®Google Scholar
• Maggiora GM. The reductionist paradox: are the laws of chemistry and physics sufficient for the discovery of new drugs? J Comput Aided Mol Des. 2011 Aug;25(8):699–708.
   PubMed Web of Science ®Google Scholar
• Xu J, Hagler A. Chemoinformatics and drug discovery. Molecules. 2002;7(8):566–600.
   Web of Science ®Google Scholar
• Mestres J, Gregori-Puigjane E, Valverde S, et al. Data completeness–the Achilles heel of drug-target networks. Nat Biotechnol. 2008 Sep;26(9):983–984.
   PubMed Web of Science ®Google Scholar
• Maggiora G, Gokhale V Non-specificity of drug-target interactions – consequences for drug discovery. In: Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: Jürgen Bajorath. ACS Symposium Series. Vol. 1222: Boston, MA: American Chemical Society; 2016. p. 91–142.
   Google Scholar
• Vogt M, Jasial S, Bajorath J. Extracting compound profiling matrices from screening data. ACS Omega. 2018;3(4):4706–4712.
   PubMed Web of Science ®Google Scholar
• Whitehead TM, Irwin BWJ, Hunt P, et al. Imputation of assay bioactivity data using deep learning. J Chem Inf Model. 2019;59(3):1197–1204.
   PubMed Web of Science ®Google Scholar
• Engel T. Basic overview of chemoinformatics. J Chem Inf Model. 2006 Nov-Dec;46(6):2267–2277.
   PubMed Web of Science ®Google Scholar
• Engel T, Gasteiger J. Chemoinformatics: basic concepts and methods. Hoboken, NJ: Wiley. 2018.
   Google Scholar
• Engel T, Gasteiger J. Applied chemoinformatics: achievements and future opportunities. Hoboken, NJ: Wiley2018.
   Google Scholar
• Ripphausen P, Nisius B, Bajorath J. State-of-the-art in ligand-based virtual screening. Drug Discov Today. 2011 May;16(9–10):372–376.
   PubMed Web of Science ®Google Scholar
• Lyu J, Wang S, Balius TE, et al. Ultra-large library docking for discovering new chemotypes. Nature. 2019;566(7743):224–229.
   PubMed Web of Science ®Google Scholar
• Irwin JJ, Gaskins G, Sterling T, et al. Predicted biological activity of purchasable chemical space. J Chem Inf Model. 2018;58(1):148–164.
   PubMed Web of Science ®Google Scholar
• Iyer P, Stumpfe D, Vogt M, et al. Activity landscapes, information theory, and structure - activity relationships. Mol Inform. 2013 Jun;32(5–6):421–430.
   PubMed Web of Science ®Google Scholar
• Duesbury E, Holliday J, Willett P. Maximum common substructure-based data fusion in similarity searching. J Chem Inf Model. 2015 Feb 23;55(2):222–230.
   PubMed Web of Science ®Google Scholar
• Duffy BC, Zhu L, Decornez H, et al. Early phase drug discovery: cheminformatics and computational techniques in identifying lead series. Bioorg Med Chem. 2012;20(18):5324–5342.
   PubMed Web of Science ®Google Scholar
• Bajorath J. Integration of virtual and high-throughput screening. Nat Rev Drug Discov. 2002 Nov;1(11):882–894.
   PubMed Web of Science ®Google Scholar
• Shoichet BK. Virtual screening of chemical libraries. Nature. 2004;432(7019):862–865.
   PubMed Web of Science ®Google Scholar
• Martin YC, Kofron JL, Traphagen LM. Do structurally similar molecules have similar biological activity? J Med Chem. 2002;45(19):4350–4358.
   PubMed Web of Science ®Google Scholar
• Keiser MJ, Irwin JJ, Shoichet BK. The chemical basis of pharmacology. Biochemistry. 2010 Dec 7;49(48):10267–10276.
   PubMed Web of Science ®Google Scholar
• Chen B, Mueller C, Willett P. Combination rules for group fusion in similarity-based virtual screening. Mol Inform. 2010;29(6–7):533–541.
   PubMed Web of Science ®Google Scholar
• Cereto-Massague A, Ojeda MJ, Valls C, et al. Tools for in silico target fishing. Methods. 2015;71:98–103.
   PubMed Web of Science ®Google Scholar
• Bajorath J. Extending accessible chemical space for the identification of novel leads. Expert Opin Drug Discov. 2016;11(9):825–829.
   PubMed Web of Science ®Google Scholar
• Zhao L, Wang W, Sedykh A, et al. Experimental errors in qsar modeling sets: what we can do and what we cannot do. ACS Omega. 2017;2(6):2805–2812.
   PubMed Web of Science ®Google Scholar
• Fourches D, Muratov E, Tropsha A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J Chem Inf Model. 2010;50(7):1189–1204.
   PubMed Web of Science ®Google Scholar
• Jonsdottir SO, Jorgensen FS, Brunak S. Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates. Bioinformatics. 2005 May 15;21(10):2145–2160.
   PubMed Web of Science ®Google Scholar
• Nicola G, Liu T, Gilson MK. Public domain databases for medicinal chemistry. J Med Chem. 2012 Aug 23; 55(16):6987–7002.
   PubMed Web of Science ®Google Scholar
• Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074–D1082.
   PubMed Web of Science ®Google Scholar
• Kim S, Chen J, Cheng T, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019 Jan 8;47(D1):D1102–D1109.
   PubMed Web of Science ®Google Scholar
• Bologa CG, Holmes J, Yang JJ, et al. DrugCentral: online drug compendium. Nucleic Acids Res. 2016;45(D1):D932–D939.
   PubMed Web of Science ®Google Scholar
• Minkiewicz P, Turło M, Iwaniak A, et al. Free accessible databases as a source of information about food components and other compounds with anticancer activity–brief review. Molecules. 2019;24(4):789.
   Web of Science ®Google Scholar
• Madariaga-Mazon A, Osnaya-Hernandez A, Chavez-Gomez A, et al. Distribution of toxicity values across different species and modes of action of pesticides from PESTIMEP and PPDB databases. Toxicol Res (Camb). 2019 Mar 1;8(2):146–156.
   PubMed Web of Science ®Google Scholar
• Martinez-Mayorga K, Marmolejo-Valencia AF, Cortes-Guzman F, et al. Toxicity assessment of structurally relevant natural products from mexican plants with antinociceptive activity. J Mex Chem Soc. 2017;61:186–196.
   Web of Science ®Google Scholar
• Iskar M, Zeller G, Zhao X-M, et al. Drug discovery in the age of systems biology: the rise of computational approaches for data integration. Curr Opin Biotechnol. 2012;23(4):609–616.
   PubMed Web of Science ®Google Scholar
• Trinajstic. Chemical graph theory. Boca Raton, FL: CRC Press; 1992.
   Google Scholar
• Mestres J, Rohrer DC, Maggiora GM. MIMIC: a molecular-field matching program. Exploiting applicability of molecular similarity approaches. J Comput Chem. 1997;18(7):934–954.
   Web of Science ®Google Scholar
• Maggiora M, Shanmugasundaram V. Molecular Similarity Measures.. In: Bajorath J, editor. Chemoinformatics and computational chemical biology. Totowa, NJ: Humana Press; 2011.
   Google Scholar
• Bajorath J. Molecular similarity concepts for informatics applications. Methods Mol Biol. 2017;1526:231–245.
   PubMedGoogle Scholar
• Hann MM, Oprea TI. Pursuing the leadlikeness concept in pharmaceutical research. Curr Opin Chem Biol. 2004 Jun;8(3):255–263.
   PubMed Web of Science ®Google Scholar
• Nguyen D-T, Mathias S, Bologa C, et al. Pharos: collating protein information to shed light on the druggable genome. Nucleic Acids Res. 2016;45(D1):D995–D1002.
   PubMed Web of Science ®Google Scholar
• Choudhuri S, Patton GW, Chanderbhan RF, et al. From classical toxicology to Tox21: some critical conceptual and technological advances in the molecular understanding of the toxic response beginning from the last quarter of the 20th century. Toxicol Sci. 2018;161(1):5–22.
   PubMed Web of Science ®Google Scholar
• Bajorath J. Molecular crime scene investigation - dusting for fingerprints. Drug Discov Today Technol. 2013 Dec;10(4):e491–e498.
   PubMedGoogle Scholar
• Maggiora GM. On outliers and activity cliffs–why QSAR often disappoints. J Chem Inf Model. 2006 Jul-Aug;46(4):1535.
   PubMed Web of Science ®Google Scholar
• Medina-Franco JL. Activity cliffs: facts or artifacts? Chem Biol Drug Des. 2013 May; 81(5):553–556.
   PubMed Web of Science ®Google Scholar
• Shanmugasundaram V, Bajorath J, Bienstock RJ, editors. Frontiers in molecular design and chemical information science - herman skolnik award symposium 2015. Jürgen Bajorath: American Chemical Society; 2016.
   Google Scholar
• Todeschini R, Valsecchi C. Activity cliffs and structural cliffs for categorical responses. MATCH Commun Math Comput Chem. 2018;80:283–294.
   Web of Science ®Google Scholar
• Iyer P, Stumpfe D, Vogt M, et al. Activity landscapes, information theory, and structure – activity relationships. Mol Inform. 2013;32(5–6):421–430.
   PubMed Web of Science ®Google Scholar
• Bajorath J. Representation and identification of activity cliffs. Expert Opin Drug Discov. 2017;12(9):879–883.
   PubMed Web of Science ®Google Scholar
• Brown RD, Martin YC. The information content of 2D and 3D structural descriptors relevant to ligand-receptor binding. J Chem Inf Comput Sci. 1997;37(1):1–9.
   Google Scholar
• Willett P. Combination of similarity rankings using data fusion. J Chem Inf Model. 2013;53(1):1–10.
   PubMed Web of Science ®Google Scholar
• Houghten RA, Pinilla C, Appel JR, et al. Mixture-based synthetic combinatorial libraries. J Med Chem. 1999 Sep 23;42(19):3743–3778.
   PubMed Web of Science ®Google Scholar
• Blum LC, 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.
   PubMed Web of Science ®Google Scholar
• Maggiora GM, Bajorath J. Chemical space networks: a powerful new paradigm for the description of chemical space. J Comput Aided Mol Des. 2014;28: 795–802. (1573-4951 (Electronic)).
   PubMed Web of Science ®Google Scholar
• Avram S, Funar-Timofei S, Borota A, et al. Quantitative estimation of pesticide-likeness for agrochemical discovery. J Cheminform. 2014 Dec;6(1):42.
   PubMedGoogle Scholar
• Xue L, Bajorath J. Molecular descriptors for effective classification of biologically active compounds based on principal component analysis identified by a genetic algorithm. J Chem Inf Comput Sci. 2000;40(3):801–809.
   PubMedGoogle Scholar
• Xue L, Stahura FL, Godden JW, et al. Fingerprint scaling increases the probability of identifying molecules with similar activity in virtual screening calculations. J Chem Inf Comput Sci. 2001;41(3):746–753.
   PubMedGoogle Scholar
• Peters JU. Polypharmacology in drug discovery. New York: John Wiley & Sons, Inc.; 2012.
   Google Scholar
• Anighoro A, Bajorath J, Rastelli G. Polypharmacology: challenges and opportunities in drug discovery. J Med Chem. 2014;57: 7874–7887. (1520-4804 (Electronic)).
   PubMed Web of Science ®Google Scholar
• Barratt MJ, Frail DE. Drug repositioning: bringing new life to shelved assets and existing drugs. New York: John Wiley & Sons; 2012.
   Google Scholar
• Ramsay RR, Popovic-Nikolic MR, Nikolic K, et al. A perspective on multi-target drug discovery and design for complex diseases. Clin Transl Med. 2018 Jan 17;7(1):3.
   PubMed Web of Science ®Google Scholar
• Raevsky OA, Mukhametov A, Grigorev VY, et al. Applications of multi-target computer-aided methodologies in molecular design of CNS drugs. Curr Med Chem. 2018;25(39):5293–5314.
   PubMed Web of Science ®Google Scholar
• Maggiora G, Gokhale V. A simple mathematical approach to the analysis of polypharmacology and polyspecificity data. F1000Res. 2017;6:788.
   PubMedGoogle Scholar
• Jacoby E. Computational Chemogenomics. 1st ed. New York: Jenny Stanford Publishing; 2013.
   Google Scholar
• Schneider G. Automating drug discovery [Perspective]. Nat Rev Drug Discov. 2017;17:97.
   PubMed Web of Science ®Google Scholar
• Marmolejo AF, Medina-Franco JL, Giulianotti M, et al. Interaction fingerprints and their applications to identify hot spots. Methods Mol Biol. 2015;1335:313–324.
   PubMedGoogle Scholar
• Kitchen DB, Decornez H, Furr JR, et al. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 2004;3(11):935–949.
   PubMed Web of Science ®Google Scholar
• Lavecchia A. Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today. 2015;20(3):318–331.
   PubMed Web of Science ®Google Scholar
• Coello Coello C, Satchidananda SD, Ghosh S. Swarm intelligence for multi-objective problems in data mining. Berlin: Springer; 2009.
   Google Scholar
• Ramsundar B, Eastman P, Walters P, et al. Deep learning for the life sciences. USA: O’Reilly Media; 2019.
   Google Scholar
• Hansch C, Fujita T. p-σ-π analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc. 1964;86(8):1616–1626.
   Web of Science ®Google Scholar
• Zhang H, Ando HY, Chen L, et al. On-the-fly selection of a training set for aqueous solubility prediction. Mol Pharm. 2007;4(4):489–497.
   PubMedGoogle Scholar
• Martin TM, Harten P, Young DM, et al. Does rational selection of training and test sets improve the outcome of QSAR modeling? J Chem Inf Model. 2012;52(10):2570–2578.
   PubMed Web of Science ®Google Scholar
• Mahe P, Vert JP. Virtual screening with support vector machines and structure kernels. Comb Chem High Throughput Screen. 2009 May;12(4):409–423.
   PubMed Web of Science ®Google Scholar
• Segall MD. Multi-parameter optimization: identifying high quality compounds with a balance of properties. Curr Pharm Des. 2012;18(9):1292–1310.
   PubMed Web of Science ®Google Scholar
• Ferreira LL, Andricopulo AD. From chemoinformatics to deep learning: an open road to drug discovery. Future Med Chem. 2019 Mar;11(5):371–374.
   PubMed Web of Science ®Google Scholar
• Ferreira LLG, Andricopulo AD. ADMET modeling approaches in drug discovery. Drug Discov Today. 2019 Mar 16;24:1157–1165.
   Web of Science ®Google Scholar
• Villoutreix BO, Lagorce D, Labbé CM, et al. One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade. Drug Discov Today. 2013;18(21):1081–1089.
   PubMed Web of Science ®Google Scholar
• González-Medina M, Naveja JJ, Sánchez-Cruz N, et al. Open chemoinformatic resources to explore the structure, properties and chemical space of molecules. RSC Adv. 2017;7(85):54153–54163.
   Web of Science ®Google Scholar
• Potemkin V, Potemkin A, Grishina M. Internet resources for drug discovery and design. Curr Top Med Chem. 2018;18(22):1955–1975.
   PubMed Web of Science ®Google Scholar
• Banegas-Luna AJ, Ceron-Carrasco JP, Perez-Sanchez H. A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data. Future Med Chem. 2018 Nov;10(22):2641–2658.
   PubMed Web of Science ®Google Scholar
• Capuzzi SJ, Kim IS-J, Lam WI, et al. Chembench: a publicly accessible, integrated cheminformatics portal. J Chem Inf Model. 2017;57(2):105–108.
   PubMed Web of Science ®Google Scholar
• Tetko IV, Maran U, Tropsha A. Public (Q)SAR services, integrated modeling environments, and model repositories on the web: state of the art and perspectives for future development. Mol Inform. 2017;36(3):1600082.
   Web of Science ®Google Scholar
• González-Medina M, Medina-Franco JL. Platform for unified molecular analysis: PUMA. J Chem Inf Model. 2017;57(8):1735–1740.
   PubMed Web of Science ®Google Scholar
• González-Medina M, Méndez-Lucio O, Medina-Franco JL. Activity landscape plotter: a web-based application for the analysis of structure–activity relationships. J Chem Inf Model. 2017;57(3):397–402.
   PubMed Web of Science ®Google Scholar
• González-Medina M, Prieto-Martínez FD, Owen JR, et al. Consensus diversity plots: a global diversity analysis of chemical libraries. J Cheminform. 2016 Nov 10;8(1):63.
   PubMedGoogle Scholar
• Schrödinger L. Discovery informatics suite 2019-1. New York, NY; 2019.
   Google Scholar
• Scott WL, Denton RE, Marrs KA, et al. Distributed drug discovery: advancing chemical education through contextualized combinatorial solid-phase organic laboratories. J Chem Educ. 2015;92(5):819–826.
   Web of Science ®Google Scholar
• Segall MD. Multi-parameter optimization in drug discovery. Innovat Pharm Technol. 2011;39:42–46.
   Google Scholar
• Speck-Planche A, Cordeiro MN. Multitasking models for quantitative structure-biological effect relationships: current status and future perspectives to speed up drug discovery. Expert Opin Drug Discov. 2015 Mar;10(3):245–256.
   PubMed Web of Science ®Google Scholar
• Speck-Planche A. Recent advances in fragment-based computational drug design: tackling simultaneous targets/biological effects. Future Med Chem. 2018 Sep 1;10(17):2021–2024.
   PubMed Web of Science ®Google Scholar
• Wild DJ, Wiggins GD. Challenges for chemoinformatics education in drug discovery. Drug Discov Today. 2006 May;11(9–10):436–439.
   PubMed Web of Science ®Google Scholar
• Petit J, Meurice N, Kaiser C, et al. Softening the Rule of Five–where to draw the line? Bioorg Med Chem. 2012;20(18):5343–5351.
   PubMed Web of Science ®Google Scholar
• Vogt I, Mestres J. Drug-target networks. Mol Inform. 2010 Jan 12;29(1–2):10–14.
   PubMed Web of Science ®Google Scholar
• Leist M, Ghallab A, Graepel R, et al. Adverse outcome pathways: opportunities, limitations and open questions. Arch Toxicol. 2017 Nov;91(11):3477–3505.
   PubMed Web of Science ®Google Scholar
• Taylor DL, Giuliano KA. High content screening. In:editors, Jorde LB, Little PF, Dunn MJ. Encyclopedia of genetics, genomics, proteomics and bioinformatics. NJ, USA: Wiley. 2005
   Google Scholar
• Stockwell BR. Chemical genetics: ligand-based discovery of gene function. Nat Rev Genet. 2000;1(2):116–125.
   PubMed Web of Science ®Google Scholar