425
12 Consensus Drug Design Using IT Microcosm
17. Butov GM, Vassiliev PM, Parshin GYu et al (2008) Synthesis and virtual screening for biological activity of adamantyl-containing trimethylbicyclo[2.2.1]heptan-2-one derivatives.
Bull Volgogr Res Cent RAMS 3:67
18. Cammarata A (1972) Interrelationship of the regression models used for structure activity
analisis. J Med Chem 15(6):573–577
19. Chernikov MV, Vassiliev PM (2006) Computer screening for novel benzimidazole derivatives with a high 5-HT 3 -antiserotonin activity using information technology Microcosm. Bull
Volgogr Res Cent RAMS 2:7–8
20. Daintith J (ed) (2004) Dictionary of computing: Oxford paperback reference. Oxford University Press, New-York
21. DeSimone RW, Currie KS, Mitchell SA et al (2004) Privileged structures: applications in
drug discovery. Comb Chem High Throughput Screen 7(5):473–494
22. Draper NR, Smith H (1998) Applied regression analysis. Wiley series in probability and
statistics. Wiley, New York
23. Duch W, Swaminathan K, Meller J (2007) Artificial intelligence approaches for rational drug
design and discovery. Curr Pharm Des 13(14):1497–1508
24. Esbensen KH (2010) Multivariate data analysis—in practice. An introduction to multivariate
data analysis and experimental design. CAMO AS, Oslo
25. Freson K, Thys C, Wittevrongel C et al (2006) Mechanisms of action and targets for actual
and future antiplatelet drugs. Mini Rev Med Chem 6(6):719–726
26. Gabbasov ZA, Popov EG, Gavrilov IYu et al (1989) New highly sensitive method for analyzing platelet aggregation. Labor delo 10:15–18
27. Gasteiger J, Engel T (eds) (2003) Chemoinformatics: a textbook. Wiley-VCH Verlag GmbH
& Co KGaA, Weinheim
28. Gendenshtein EI, Khadzsay YaI (1961) On pharmacologic properties of ajmalin, a new antiarrhythmic agent. Farmacol i toxicol, 24(1):49–57
29. Germashev IV, Derbisher VE, Vasil’ev PM (1998) Prediction of the activity of low-molecular
organics in polymer compounds using probabilistic methods. Theor Found Chem Eng 32
(5):514–517
30. Germashev IV, Derbisher VE, Zotov YuL et al (2001) Computer-assisted design of active
additives for polyvinyl chloride. Int Polym Sci Technol 28(7):36–38
31. Gertrudes JC, Maltarollo VG, Silva RA et al (2012) Machine learning techniques and drug
design. Curr Med Chem 19(25):4289–4297
32. Golender VE, Rosenblit AB (1978) Computer methods for drug design. Zinatne, Riga
33. González MP, Terán C, Saíz-Urra L et al (2008) Variable selection methods in QSAR: an
overview. Curr Top Med Chem 8(18):1606–1627
34. Gorelik AL, Skripkin VA (1984) Recognition methods. Vysshaya shkola, Moscow
35. Gorlov IF, Yurina OS, Vasiliev PM (2002) An experimental testing of results of the computeraided prediction of a pharmacological activity spectrum of walnut’s extract. Russ Agric Sci
5:45–47
36. Gupta SP (ed) (2006) QSAR and molecular modeling studies in heterocyclic drugs I. Topics
in Heterocyclic Chemistry, vol 3. Springer-Verlag, Heidelberg
37. Gurova NA, Vassiliev PM (2009) Testing the prediction of antiarrhythmic activity of imidazonezimidazole derivatives. In: Proceedings of the XVI Russian National Congress “Man and
drug”, Moscow, 6–10 Apr 2009, p 79
38. Gurova NA, Vassiliev PM, Anisimova VA (2007) Computer prediction and experimental
testing of antiarrhythmic activity of derivatives of nitrogen-containing heterocycles. In: Proceeding of the III Congress of Pharmacologists of Russia “Pharmacology—practical health”,
Saint Petersburg, 23–27 Sept 2007. Psycopharmacol biol narcol 7:1671
39. Hawkins DM (2004) The problem of overfitting. J Chem Inf Comput Sci 44(1):1–12
40. Haykin S (2009) Neural networks and learning machines. Pearson Education, New Jersey
41. Hollander M, Wolfe DA (1999) Nonparametric statistical methods. Wiley Series in Probability and Statistics. Wiley, New York
12 Consensus Drug Design Using IT Microcosm
17. Butov GM, Vassiliev PM, Parshin GYu et al (2008) Synthesis and virtual screening for biological activity of adamantyl-containing trimethylbicyclo[2.2.1]heptan-2-one derivatives.
Bull Volgogr Res Cent RAMS 3:67
18. Cammarata A (1972) Interrelationship of the regression models used for structure activity
analisis. J Med Chem 15(6):573–577
19. Chernikov MV, Vassiliev PM (2006) Computer screening for novel benzimidazole derivatives with a high 5-HT 3 -antiserotonin activity using information technology Microcosm. Bull
Volgogr Res Cent RAMS 2:7–8
20. Daintith J (ed) (2004) Dictionary of computing: Oxford paperback reference. Oxford University Press, New-York
21. DeSimone RW, Currie KS, Mitchell SA et al (2004) Privileged structures: applications in
drug discovery. Comb Chem High Throughput Screen 7(5):473–494
22. Draper NR, Smith H (1998) Applied regression analysis. Wiley series in probability and
statistics. Wiley, New York
23. Duch W, Swaminathan K, Meller J (2007) Artificial intelligence approaches for rational drug
design and discovery. Curr Pharm Des 13(14):1497–1508
24. Esbensen KH (2010) Multivariate data analysis—in practice. An introduction to multivariate
data analysis and experimental design. CAMO AS, Oslo
25. Freson K, Thys C, Wittevrongel C et al (2006) Mechanisms of action and targets for actual
and future antiplatelet drugs. Mini Rev Med Chem 6(6):719–726
26. Gabbasov ZA, Popov EG, Gavrilov IYu et al (1989) New highly sensitive method for analyzing platelet aggregation. Labor delo 10:15–18
27. Gasteiger J, Engel T (eds) (2003) Chemoinformatics: a textbook. Wiley-VCH Verlag GmbH
& Co KGaA, Weinheim
28. Gendenshtein EI, Khadzsay YaI (1961) On pharmacologic properties of ajmalin, a new antiarrhythmic agent. Farmacol i toxicol, 24(1):49–57
29. Germashev IV, Derbisher VE, Vasil’ev PM (1998) Prediction of the activity of low-molecular
organics in polymer compounds using probabilistic methods. Theor Found Chem Eng 32
(5):514–517
30. Germashev IV, Derbisher VE, Zotov YuL et al (2001) Computer-assisted design of active
additives for polyvinyl chloride. Int Polym Sci Technol 28(7):36–38
31. Gertrudes JC, Maltarollo VG, Silva RA et al (2012) Machine learning techniques and drug
design. Curr Med Chem 19(25):4289–4297
32. Golender VE, Rosenblit AB (1978) Computer methods for drug design. Zinatne, Riga
33. González MP, Terán C, Saíz-Urra L et al (2008) Variable selection methods in QSAR: an
overview. Curr Top Med Chem 8(18):1606–1627
34. Gorelik AL, Skripkin VA (1984) Recognition methods. Vysshaya shkola, Moscow
35. Gorlov IF, Yurina OS, Vasiliev PM (2002) An experimental testing of results of the computeraided prediction of a pharmacological activity spectrum of walnut’s extract. Russ Agric Sci
5:45–47
36. Gupta SP (ed) (2006) QSAR and molecular modeling studies in heterocyclic drugs I. Topics
in Heterocyclic Chemistry, vol 3. Springer-Verlag, Heidelberg
37. Gurova NA, Vassiliev PM (2009) Testing the prediction of antiarrhythmic activity of imidazonezimidazole derivatives. In: Proceedings of the XVI Russian National Congress “Man and
drug”, Moscow, 6–10 Apr 2009, p 79
38. Gurova NA, Vassiliev PM, Anisimova VA (2007) Computer prediction and experimental
testing of antiarrhythmic activity of derivatives of nitrogen-containing heterocycles. In: Proceeding of the III Congress of Pharmacologists of Russia “Pharmacology—practical health”,
Saint Petersburg, 23–27 Sept 2007. Psycopharmacol biol narcol 7:1671
39. Hawkins DM (2004) The problem of overfitting. J Chem Inf Comput Sci 44(1):1–12
40. Haykin S (2009) Neural networks and learning machines. Pearson Education, New Jersey
41. Hollander M, Wolfe DA (1999) Nonparametric statistical methods. Wiley Series in Probability and Statistics. Wiley, New York