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Volumn 8, Issue 1, 2016, Pages 1-11

Computational methods for prediction of in vitro effects of new chemical structures

Author keywords

Machine learning; Molecular fingerprints; Similarity searching; Tox21 challenge; Toxicity prediction

Indexed keywords


EID: 84990817477     PISSN: None     EISSN: 17582946     Source Type: Journal    
DOI: 10.1186/s13321-016-0162-2     Document Type: Article
Times cited : (36)

References (60)
  • 1
    • 23044491985 scopus 로고    scopus 로고
    • Keynote review: is declining innovation in the pharmaceutical industry a myth?
    • Schmid EF, Smith DA (2005) Keynote review: is declining innovation in the pharmaceutical industry a myth? Drug Discov Today 10:1031-1039
    • (2005) Drug Discov Today , vol.10 , pp. 1031-1039
    • Schmid, E.F.1    Smith, D.A.2
  • 2
    • 79959929769 scopus 로고    scopus 로고
    • How were new medicines discovered?
    • 1:CAS:528:DC%2BC3MXotlCmsbs%3D
    • Swinney DC, Anthony J (2011) How were new medicines discovered? Nat Rev Drug Discov 10:507-519
    • (2011) Nat Rev Drug Discov , vol.10 , pp. 507-519
    • Swinney, D.C.1    Anthony, J.2
  • 3
    • 77952304596 scopus 로고    scopus 로고
    • Predictive toxicology approaches for small molecule oncology drugs
    • 1:STN:280:DC%2BC3c7is1ahtw%3D%3D
    • Maziasz T, Kadambi VJ, Silverman L, Fedyk E, Alden CL (2010) Predictive toxicology approaches for small molecule oncology drugs. Toxicol Pathol 38:148-164
    • (2010) Toxicol Pathol , vol.38 , pp. 148-164
    • Maziasz, T.1    Kadambi, V.J.2    Silverman, L.3    Fedyk, E.4    Alden, C.L.5
  • 5
    • 77949904078 scopus 로고    scopus 로고
    • In silico toxicology in drug discovery - concepts based on three-dimensional models
    • 1:CAS:528:DC%2BC3cXls1GnsA%3D%3D
    • Vedani A, Smiesko M (2009) In silico toxicology in drug discovery - concepts based on three-dimensional models. Altern Lab Anim ATLA 37:477-496
    • (2009) Altern Lab Anim ATLA , vol.37 , pp. 477-496
    • Vedani, A.1    Smiesko, M.2
  • 6
    • 0003670171 scopus 로고    scopus 로고
    • V Pliska B Testa H Waterbeemd van de 134 VCH Publishers Weinheim
    • Pliska V, Testa B, van de Waterbeemd H (eds) (1996) Lipophilicity in drug action and toxicology, vol 134. VCH Publishers, Weinheim, pp 49-71
    • (1996) Lipophilicity in drug action and toxicology , pp. 49-71
  • 7
    • 84990854606 scopus 로고
    • Aqueous two-phase partitioning. Physical chemistry and bioanalytical applications
    • Giuliano KA (1995) Aqueous two-phase partitioning. Physical chemistry and bioanalytical applications. FEBS Lett 98:98-102
    • (1995) FEBS Lett , vol.98 , pp. 98-102
    • Giuliano, K.A.1
  • 8
    • 0017175636 scopus 로고
    • Quantitative structure-activity relationships. 2. A mixed approach, based on Hansch and free-Wilson analysis
    • 1:CAS:528:DyaE28XhsVCmtL4%3D
    • Kubinyi H (1976) Quantitative structure-activity relationships. 2. A mixed approach, based on Hansch and free-Wilson analysis. J Med Chem 19:587-600
    • (1976) J Med Chem , vol.19 , pp. 587-600
    • Kubinyi, H.1
  • 9
    • 0029080388 scopus 로고
    • The expanding role of quantitative structure-activity relationships (QSAR) in toxicology
    • 1:CAS:528:DyaK2MXotVegt78%3D
    • Hansch C, Hoekman D, Leo A, Zhang L, Li P (1995) The expanding role of quantitative structure-activity relationships (QSAR) in toxicology. Toxicol Lett 79:45-53
    • (1995) Toxicol Lett , vol.79 , pp. 45-53
    • Hansch, C.1    Hoekman, D.2    Leo, A.3    Zhang, L.4    Li, P.5
  • 10
    • 0001074001 scopus 로고    scopus 로고
    • Handbook of molecular descriptors
    • Todeschini R, Consonni V (2000) Handbook of molecular descriptors. New York 11:688
    • (2000) New York , vol.11 , pp. 688
    • Todeschini, R.1    Consonni, V.2
  • 12
    • 0028148495 scopus 로고
    • Computational techniques for the prediction of toxicity
    • 1:CAS:528:DyaK2cXmt1eks7k%3D
    • Livingstone DJ (1994) Computational techniques for the prediction of toxicity. Toxicol Vitro 8:873-877
    • (1994) Toxicol Vitro , vol.8 , pp. 873-877
    • Livingstone, D.J.1
  • 13
    • 84990820825 scopus 로고    scopus 로고
    • TOPKAT (TOxicity Prediction by Komputer Assisted Technology)
    • TOPKAT (TOxicity Prediction by Komputer Assisted Technology). http://accelrys.com/
  • 14
    • 84990820828 scopus 로고    scopus 로고
    • ADMET Predictor™ (Simulations Plus, Inc. USA)
    • ADMET Predictor™ (Simulations Plus, Inc., USA). http://www.simulations-plus.com/
  • 15
    • 84990828874 scopus 로고    scopus 로고
    • ADME-Tox Prediction (Advanced Chemistry Development, Inc. Canada)
    • ADME-Tox Prediction (Advanced Chemistry Development, Inc., Canada). http://www.acdlabs.com/
  • 16
    • 84990828873 scopus 로고    scopus 로고
    • DEREK (Lhasa Limited)
    • DEREK (Lhasa Limited). http://www.lhasalimited.org/
  • 17
    • 84879516711 scopus 로고    scopus 로고
    • U.S. Environmental Protection Agency
    • Toxicity Estimation Software Tools (U.S. Environmental Protection Agency). http://www2.epa.gov/chemical-research/toxicity-estimation-software-tool-test
    • Toxicity Estimation Software Tools
  • 18
    • 84904993806 scopus 로고    scopus 로고
    • Machine learning methods in chemoinformatics
    • 1:CAS:528:DC%2BC2cXht1ans77J
    • Mitchell JBO (2014) Machine learning methods in chemoinformatics. Wiley Interdiscip Rev Comput Mol Sci 4:468-481
    • (2014) Wiley Interdiscip Rev Comput Mol Sci , vol.4 , pp. 468-481
    • Mitchell, J.B.O.1
  • 20
    • 84925400066 scopus 로고    scopus 로고
    • Machine-learning approaches in drug discovery: methods and applications
    • Lavecchia A (2015) Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today 20:318-331
    • (2015) Drug Discov Today , vol.20 , pp. 318-331
    • Lavecchia, A.1
  • 21
    • 44849090379 scopus 로고    scopus 로고
    • A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model
    • Judson R, Elloumi F, Setzer RW, Li Z, Shah I (2008) A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model. BMC Bioinform 9:241
    • (2008) BMC Bioinform , vol.9 , pp. 241
    • Judson, R.1    Elloumi, F.2    Setzer, R.W.3    Li, Z.4    Shah, I.5
  • 22
    • 79954987569 scopus 로고    scopus 로고
    • Evaluation of different machine learning methods for ligand-based virtual screening
    • Kurczab R, Smusz S, Bojarski A (2011) Evaluation of different machine learning methods for ligand-based virtual screening. J Cheminform 3(Suppl 1):P41
    • (2011) J Cheminform , vol.3 , pp. P41
    • Kurczab, R.1    Smusz, S.2    Bojarski, A.3
  • 23
    • 84899072790 scopus 로고    scopus 로고
    • Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity
    • Webb SJ, Hanser T, Howlin B, Krause P, Vessey JD (2014) Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity. J Cheminform 6:8
    • (2014) J Cheminform , vol.6 , pp. 8
    • Webb, S.J.1    Hanser, T.2    Howlin, B.3    Krause, P.4    Vessey, J.D.5
  • 25
    • 84862848391 scopus 로고    scopus 로고
    • Machine learning methods for property prediction in chemoinformatics: Quo Vadis?
    • 1:CAS:528:DC%2BC38XmvV2ntL4%3D
    • Varnek A, Baskin I (2012) Machine learning methods for property prediction in chemoinformatics: Quo Vadis? J Chem Inf Model 52:1413-1437
    • (2012) J Chem Inf Model , vol.52 , pp. 1413-1437
    • Varnek, A.1    Baskin, I.2
  • 27
    • 84990828880 scopus 로고    scopus 로고
    • Tox21 challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs
    • Huang R, Xia M, Nguyen D, Zhao T, Sakamuru S (2016) Tox21 challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs. Front Environ Sci 3:1-9
    • (2016) Front Environ Sci , vol.3 , pp. 1-9
    • Huang, R.1    Xia, M.2    Nguyen, D.3    Zhao, T.4    Sakamuru, S.5
  • 29
    • 84990828878 scopus 로고    scopus 로고
    • Tox21 Data Challenge 2014. https://tripod.nih.gov/tox21/challenge/leaderboard.jsp
    • (2014) Tox21 Data Challenge
  • 30
    • 80051969454 scopus 로고    scopus 로고
    • Accelrys: San Diego, CA
    • MACCS Structural keys; Accelrys: San Diego, CA, 2011. http://accelrys.com/
    • (2011) MACCS Structural keys
  • 31
    • 77952772341 scopus 로고    scopus 로고
    • Extended-connectivity fingerprints
    • 1:CAS:528:DC%2BC3cXlt1Onsbg%3D
    • Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742-754
    • (2010) J Chem Inf Model , vol.50 , pp. 742-754
    • Rogers, D.1    Hahn, M.2
  • 32
    • 84990824713 scopus 로고    scopus 로고
    • ToxPrint
    • ToxPrint. https://toxprint.org/
  • 33
    • 0023856614 scopus 로고
    • Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents by the U.S. NCI/NTP
    • 1:CAS:528:DyaL1cXhs1ejsLw%3D
    • Ashby J, Tennant RW (1988) Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents by the U.S. NCI/NTP. Mutat Res 204:17-115
    • (1988) Mutat Res , vol.204 , pp. 17-115
    • Ashby, J.1    Tennant, R.W.2
  • 34
    • 10744227159 scopus 로고    scopus 로고
    • European branch of the International Life Sciences Institute: structure-based thresholds of toxicological concern (TTC): guidance for application to substances present at low levels in the diet
    • 1:CAS:528:DC%2BD3sXptVentLw%3D
    • Kroes R, Renwick AG, Cheeseman M, Kleiner J, Mangelsdorf I, Piersma A, Schilter B, Schlatter J, van Schothorst F, Vos JG, Würtzen G (2004) European branch of the International Life Sciences Institute: structure-based thresholds of toxicological concern (TTC): guidance for application to substances present at low levels in the diet. Food Chem Toxicol 42:65-83
    • (2004) Food Chem Toxicol , vol.42 , pp. 65-83
    • Kroes, R.1    Renwick, A.G.2    Cheeseman, M.3    Kleiner, J.4    Mangelsdorf, I.5    Piersma, A.6    Schilter, B.7    Schlatter, J.8    Van Schothorst, F.9    Vos, J.G.10    Würtzen, G.11
  • 35
    • 0029404240 scopus 로고
    • Electrotopological state indices for atom types: a novel combination of electronic, topological, and valence state information
    • 1:CAS:528:DyaK2MXovF2hsbc%3D
    • Hall LH, Kier LB (1995) Electrotopological state indices for atom types: a novel combination of electronic, topological, and valence state information. J Chem Inf Model 35:1039-1045
    • (1995) J Chem Inf Model , vol.35 , pp. 1039-1045
    • Hall, L.H.1    Kier, L.B.2
  • 36
    • 0000247432 scopus 로고
    • A characterization of molecular similarity methods for property prediction
    • Johnson M, Basak S, Maggiora G (1988) A characterization of molecular similarity methods for property prediction. Math Comput Model 11:630-634
    • (1988) Math Comput Model , vol.11 , pp. 630-634
    • Johnson, M.1    Basak, S.2    Maggiora, G.3
  • 37
    • 84894057083 scopus 로고    scopus 로고
    • Molecular similarity in medicinal chemistry
    • 1:CAS:528:DC%2BC3sXhs1Kktb3E
    • Maggiora G, Vogt M, Stumpfe D, Bajorath J (2014) Molecular similarity in medicinal chemistry. J Med Chem 57:3186-3204
    • (2014) J Med Chem , vol.57 , pp. 3186-3204
    • Maggiora, G.1    Vogt, M.2    Stumpfe, D.3    Bajorath, J.4
  • 38
    • 85060344810 scopus 로고    scopus 로고
    • Prediction of compounds activity in nuclear receptor signaling and stress pathway assays using machine learning algorithms and low-dimensional molecular descriptors
    • December
    • Stefaniak F (2015) Prediction of compounds activity in nuclear receptor signaling and stress pathway assays using machine learning algorithms and low-dimensional molecular descriptors. Front Environ Sci 3(December):1-7
    • (2015) Front Environ Sci , vol.3 , pp. 1-7
    • Stefaniak, F.1
  • 39
    • 79960136516 scopus 로고    scopus 로고
    • BRAINSTORMING: consensus learning in practice
    • Plewczynski D (2009) BRAINSTORMING: consensus learning in practice. Front Neuroinform 679:14
    • (2009) Front Neuroinform , vol.679 , pp. 14
    • Plewczynski, D.1
  • 44
    • 84990858292 scopus 로고    scopus 로고
    • KNIME AG
    • KNIME AG. https://www.knime.org/
  • 45
    • 84990862787 scopus 로고    scopus 로고
    • Molecular Networks GmbH
    • Molecular Networks GmbH. https://www.molecular-networks.com/
  • 46
    • 2942700377 scopus 로고    scopus 로고
    • Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures
    • 1:CAS:528:DC%2BD2cXhsVOis7g%3D
    • Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, Schuffenhauer A (2004) Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. J Chem Inf Comput Sci 44:1177-1185
    • (2004) J Chem Inf Comput Sci , vol.44 , pp. 1177-1185
    • Hert, J.1    Willett, P.2    Wilton, D.J.3    Acklin, P.4    Azzaoui, K.5    Jacoby, E.6    Schuffenhauer, A.7
  • 47
    • 0037526838 scopus 로고    scopus 로고
    • Similarity-based approaches to virtual screening
    • 1:CAS:528:DC%2BD3sXktF2itb4%3D
    • Willett P (2003) Similarity-based approaches to virtual screening. Biochem Soc Trans 31(Pt 3):603-606
    • (2003) Biochem Soc Trans , vol.31 , pp. 603-606
    • Willett, P.1
  • 49
    • 0035478854 scopus 로고    scopus 로고
    • Random Forests
    • Breiman L (2001) Random Forests. Mach Learn 45:5-32
    • (2001) Mach Learn , vol.45 , pp. 5-32
    • Breiman, L.1
  • 50
    • 0025206332 scopus 로고
    • Probabilistic neural networks
    • Specht DF (1990) Probabilistic neural networks. Neural Netw 3:109-118
    • (1990) Neural Netw , vol.3 , pp. 109-118
    • Specht, D.F.1
  • 52
    • 4043167653 scopus 로고    scopus 로고
    • Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds
    • 1:CAS:528:DC%2BD2cXks1Gis7Y%3D
    • Helma C, Cramer T, Kramer S, De Raedt L (2004) Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds. J Chem Inf Comput Sci 44:1402-1411
    • (2004) J Chem Inf Comput Sci , vol.44 , pp. 1402-1411
    • Helma, C.1    Cramer, T.2    Kramer, S.3    De Raedt, L.4
  • 53
    • 84920547881 scopus 로고    scopus 로고
    • In silico prediction of chemical toxicity on avian species using chemical category approaches
    • 1:CAS:528:DC%2BC2cXitV2msr%2FM
    • Zhang C, Cheng F, Sun L, Zhuang S, Li W, Liu G, Lee PW, Tang Y (2015) In silico prediction of chemical toxicity on avian species using chemical category approaches. Chemosphere 122:280-287
    • (2015) Chemosphere , vol.122 , pp. 280-287
    • Zhang, C.1    Cheng, F.2    Sun, L.3    Zhuang, S.4    Li, W.5    Liu, G.6    Lee, P.W.7    Tang, Y.8
  • 54
    • 0344835882 scopus 로고    scopus 로고
    • Constructive training of probabilistic neural networks
    • Berthold MR, Diamond J (1998) Constructive training of probabilistic neural networks. Neurocomputing 19:167-183
    • (1998) Neurocomputing , vol.19 , pp. 167-183
    • Berthold, M.R.1    Diamond, J.2
  • 57
    • 0000131403 scopus 로고    scopus 로고
    • Cross-validation methods
    • Browne M (2000) Cross-validation methods. J Math Psychol 44:108-132
    • (2000) J Math Psychol , vol.44 , pp. 108-132
    • Browne, M.1
  • 58
    • 0032077905 scopus 로고    scopus 로고
    • Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology
    • van Erkel AR, Pattynama PM (1998) Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. Eur J Radiol 27:88-94
    • (1998) Eur J Radiol , vol.27 , pp. 88-94
    • Van Erkel, A.R.1    Pattynama, P.M.2
  • 59
    • 0442293872 scopus 로고    scopus 로고
    • Receiver operating characteristic methodology
    • Pepe MS (2000) Receiver operating characteristic methodology. J Am Stat Assoc 95:308-311
    • (2000) J Am Stat Assoc , vol.95 , pp. 308-311
    • Pepe, M.S.1
  • 60
    • 11344274987 scopus 로고    scopus 로고
    • Statistics review 13: receiver operating characteristic curves
    • Bewick V, Cheek L, Ball J (2004) Statistics review 13: receiver operating characteristic curves. Crit Care 8:508-512
    • (2004) Crit Care , vol.8 , pp. 508-512
    • Bewick, V.1    Cheek, L.2    Ball, J.3


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