498
N. Ya. Golovenko et al.
41. Clark DE (1999) Rapid calculation of polar molecular surface area and its application to
the prediction of transport phenomena. 1. Prediction of intestinal absorption. J Pharm Sci
88:807–814
42. Kuz’min VE, Artemenko AG, Polischuk PG et al (2005) Hierarchic system of QSAR models
(1D–4D) on the base of simplex representation of molecular structure. J Mol Model 11:457–467
43. Kuz’min VE, Artemenko AG, Lozitsky VP et al (2002) The analysis of structure–anticancer
and antiviral activity relationships for macrocyclic pyridinophanes and their analogues on the
basis of 4D QSAR models (simplex representation of molecular structure). Acta Biochim Pol
49:157–168
44. Lindgren F, Geladi P, Rannar S et al (1994) Interactive variable selection (IVS) for PLS. Part
1: theory and algorithms. J Chemom 8:349–363
45. Rannar S, Lindgren F, Geladi P et al (1994) A PLS kernel algorithm for data sets with many
variables and fewer objects. Part 1: theory and algorithm. J Chemom 8:111–125
46. Rogers D, Hopfinger AJ (1994) Application of genetic function approximation to quantitative
structure–activity relationships and quantitative structure-property relationships. J Chem Inf
Comput Sci 34(4):854–866
47. Carhart RE, Smith DH, Venkataraghavan R (1985) Atom pairs as molecular features in structure–activity studies. Definition and application. J Chem Inf Comput Sci 25:64–73
48. Kuz’min VE, Artemenko AG, Muratov EN (2008) Hierarchical QSAR technology on the
base of Simplex representation of molecular structure. J Comput Aided Mol Des 22:403–421
49. Lindenberg M, Kopp S, Dressman JB (2004) Classification of orally administered drugs on
the World Health Organization Model list of essential medicines according to the biopharmaceutics classification system. Eur J Pharm Biopharm 58:265–278
50. Kuz’min VE, Artemenko AG, Muratov EN et al (2010) Virtual screening and molecular design based on hierarchical QSAR technology. In: Puzyn T, Cronin M, Leszczynski J (eds)
Recent advances in QSAR studies. Springer, London, pp. 127–176
51. Golbraikh A, Tropsha A (2002) Beware of Q2. J Mol Graph Model 20:269–276
52. Seel M, Turner DB, Willett P (1999) Effect of parameter variations on the effectiveness of
HQSAR analyses. Quant Struct-Activ Relat 18:245–252
53. Breiman L (2001) Random forests. Mach Learn 45:5–32
54. Breiman L, Friedman JH, Olshen RA et al (1984) Classification and regression trees. Wadsworth, Belmont, p 368
55. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random Forest:
a classification and regression tool for compound classification and qsar modeling. J Chem
Inf Comput Sci 43(6):1947–1958
56. program CF, Polishchuk PG (2010–2013) http://qsar4u.com. Accessed 10 March 20112
57. Kuz’min VE, Artemenko AG, Lozitska RN et al (2005) Investigation of anticancer activity of
macrocyclic Schiff bases by means of 4D-QSAR based on simplex representation of molecular structure. SAR QSAR Environ Res 16(3):219–230
58. Kuz’min VE, Artemenko AG, Muratov EN et al (2007) Quantitative structure-activity relationship studies of [(biphenyloxy)propyl]isoxazole derivatives-human rhinovirus 2 replication inhibitors. J Med Chem 50:4205–4213
59. Muratov EN, Varlamova EV, Artemenko AG et al (2011) QSAR analysis of[(biphenyloxy)
propyl] isoxazoles: agents against coxsackievirus B3. Future Med Chem 3(1):15–27
60. Muratov EN, Artemenko AG, Varlamova EV et al (2010) Per aspera ad astra: application of
Simplex QSAR approach in antiviral research. Future Med Chem 2:1205–1226
61. Kovdienko NA, Polischuk PG, Muratov EN et al (2010) Application of random forest and
multiple linear regression technologues to QSPR prediction of an aqueous solubility for military compounds. Mol informatics 29:394–406
62. Golovenko NYa, Kuz’min, Artemenko AG et al (2011) Prediction of bioavailability of drugs
by the method of classification models. Clin Inform Telemed 8(7):88–92
63. Oh DM, Curl RL, Amidon GL (1993) Estimating the fraction dose absorbed from suspensions
of poorly soluble compounds in humans: a mathematical model. Pharm Res 10(2):264–270
N. Ya. Golovenko et al.
41. Clark DE (1999) Rapid calculation of polar molecular surface area and its application to
the prediction of transport phenomena. 1. Prediction of intestinal absorption. J Pharm Sci
88:807–814
42. Kuz’min VE, Artemenko AG, Polischuk PG et al (2005) Hierarchic system of QSAR models
(1D–4D) on the base of simplex representation of molecular structure. J Mol Model 11:457–467
43. Kuz’min VE, Artemenko AG, Lozitsky VP et al (2002) The analysis of structure–anticancer
and antiviral activity relationships for macrocyclic pyridinophanes and their analogues on the
basis of 4D QSAR models (simplex representation of molecular structure). Acta Biochim Pol
49:157–168
44. Lindgren F, Geladi P, Rannar S et al (1994) Interactive variable selection (IVS) for PLS. Part
1: theory and algorithms. J Chemom 8:349–363
45. Rannar S, Lindgren F, Geladi P et al (1994) A PLS kernel algorithm for data sets with many
variables and fewer objects. Part 1: theory and algorithm. J Chemom 8:111–125
46. Rogers D, Hopfinger AJ (1994) Application of genetic function approximation to quantitative
structure–activity relationships and quantitative structure-property relationships. J Chem Inf
Comput Sci 34(4):854–866
47. Carhart RE, Smith DH, Venkataraghavan R (1985) Atom pairs as molecular features in structure–activity studies. Definition and application. J Chem Inf Comput Sci 25:64–73
48. Kuz’min VE, Artemenko AG, Muratov EN (2008) Hierarchical QSAR technology on the
base of Simplex representation of molecular structure. J Comput Aided Mol Des 22:403–421
49. Lindenberg M, Kopp S, Dressman JB (2004) Classification of orally administered drugs on
the World Health Organization Model list of essential medicines according to the biopharmaceutics classification system. Eur J Pharm Biopharm 58:265–278
50. Kuz’min VE, Artemenko AG, Muratov EN et al (2010) Virtual screening and molecular design based on hierarchical QSAR technology. In: Puzyn T, Cronin M, Leszczynski J (eds)
Recent advances in QSAR studies. Springer, London, pp. 127–176
51. Golbraikh A, Tropsha A (2002) Beware of Q2. J Mol Graph Model 20:269–276
52. Seel M, Turner DB, Willett P (1999) Effect of parameter variations on the effectiveness of
HQSAR analyses. Quant Struct-Activ Relat 18:245–252
53. Breiman L (2001) Random forests. Mach Learn 45:5–32
54. Breiman L, Friedman JH, Olshen RA et al (1984) Classification and regression trees. Wadsworth, Belmont, p 368
55. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random Forest:
a classification and regression tool for compound classification and qsar modeling. J Chem
Inf Comput Sci 43(6):1947–1958
56. program CF, Polishchuk PG (2010–2013) http://qsar4u.com. Accessed 10 March 20112
57. Kuz’min VE, Artemenko AG, Lozitska RN et al (2005) Investigation of anticancer activity of
macrocyclic Schiff bases by means of 4D-QSAR based on simplex representation of molecular structure. SAR QSAR Environ Res 16(3):219–230
58. Kuz’min VE, Artemenko AG, Muratov EN et al (2007) Quantitative structure-activity relationship studies of [(biphenyloxy)propyl]isoxazole derivatives-human rhinovirus 2 replication inhibitors. J Med Chem 50:4205–4213
59. Muratov EN, Varlamova EV, Artemenko AG et al (2011) QSAR analysis of[(biphenyloxy)
propyl] isoxazoles: agents against coxsackievirus B3. Future Med Chem 3(1):15–27
60. Muratov EN, Artemenko AG, Varlamova EV et al (2010) Per aspera ad astra: application of
Simplex QSAR approach in antiviral research. Future Med Chem 2:1205–1226
61. Kovdienko NA, Polischuk PG, Muratov EN et al (2010) Application of random forest and
multiple linear regression technologues to QSPR prediction of an aqueous solubility for military compounds. Mol informatics 29:394–406
62. Golovenko NYa, Kuz’min, Artemenko AG et al (2011) Prediction of bioavailability of drugs
by the method of classification models. Clin Inform Telemed 8(7):88–92
63. Oh DM, Curl RL, Amidon GL (1993) Estimating the fraction dose absorbed from suspensions
of poorly soluble compounds in humans: a mathematical model. Pharm Res 10(2):264–270