456
I. I. Baskin and N. I. Zhokhova
References
1. Varnek A, Baskin II (2011) Chemoinformatics as a theoretical chemistry discipline. Mol Inf
30(1):20–32. doi:10.1002/minf.201000100
2. Gasteiger J, Engel T (2003) Chemoinformatics: a textbook. Wiley-VCH, Weinheim
3. Gasteiger J (2003) Handbook of chemoinformatics: from data to knowledge. Wiley-VCH,
Weinheim
4. Varnek A, Baskin I (2012) Machine learning methods for property prediction in chemoinformatics: quo vadis? J Chem Inf Mod 52(6):1413–1437. doi:10.1021/ci200409x
5. Vapnik V (1998) Statistical learning theory. Wiley-Interscience, New York
6. Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT, Cambridge
7. Zhokhova NI, Baskin II, Bakhronov DK, Palyulin VA, Zefirov NS (2009) Method of continuous molecular fields in the search for quantitative structure-activity relationships. Dokl Chem
429(1):273–276
8. Baskin II, Zhokhova NI (2013) The continuous molecular fields approach to building 3DQSAR models. J Comput-Aided Mol Des 27(5):427–442. doi:10.1007/s10822-013-9656-4
9. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. doi:10.1007/
bf00994018
10. Saunders C, Gammerman A, Vovk V (1998) Ridge regression learning algorithm in dual variables. In: proceedings of the Fifteenth International Conference on Machine Learning (ICML98). Morgan Kaufmann, Burlington, pp 515–521
11. Rasmussen CE, Williams CKI (2006) Gaussian processes in machine learning. Adaptive computation and machine learning. MIT, Cambridge
12. Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471
13. Karpov PV, Baskin II, Zhokhova NI, Zefirov NS (2011) Method of continuous molecular
fields in the one-class classification task. Dokl Chem 440(2):263–265
14. Karpov PV, Baskin II, Zhokhova NI, Nawrozkij MB, Zefirov AN, Yablokov AS, Novakov IA,
Zefirov NS (2011) One-class approach: models for virtual screening of non-nucleoside HIV1 reverse transcriptase inhibitors based on the concept of continuous molecular fields. Russ
Chem Bull 60(11):2418–2424. doi:10.1007/s11172-011-0372-8
15. Kubinyi H (ed) (2000) 3D QSAR in drug design. Volume 1: theory methods and applications
(Three-dimensional quantitative structure activity relationships). Kluwer/Escom, Dordrecht
16. Kubinyi H, Folkers G, Martin YC (eds) (2002a) 3D QSAR in drug design. Volume 2: ligandprotein Interactions and Molecular Similarity. Kluwer Academic Publishers, Dordrecht
17. Kubinyi H, Folkers G, Martin YC (eds) (2002b) 3D QSAR in drug design. Volume 3: Recent
advances. Kluwer Academic Publishers, Dordrecht
18. Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA).
1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110(18):5959–
5967. doi:10.1021/ja00226a005
19. Testa B, Carrupt PA, Gaillard P, Billois F, Weber P (1996) Lipophilicity in molecular modeling. Pharm Res 13(3):335–343. doi:10.1023/a:1016024005429
20. Kim KH, Greco G, Novellino E, Silipo C, Vittoria A (1993) Use of the hydrogen bond potential function in a comparative molecular field analysis (CoMFA) on a set of benzodiazepines.
J Comput-Aided Mol Des 7(3):263–280
21. Waller CL, Marshall GR (1993) Three-dimensional quantitative structure-activity relationship of angiotesin-converting enzyme and thermolysin inhibitors. II. A comparison of
CoMFA models incorporating molecular orbital fields and desolvation free energies based on
active-analog and complementary-receptor-field alignment rules. J Med Chem 36(16):2390–
2403
22. Kellogg GE (1996) E-state fields: applications to 3D QSAR. J Comput-Aided Mol Des
10(6):513–520
I. I. Baskin and N. I. Zhokhova
References
1. Varnek A, Baskin II (2011) Chemoinformatics as a theoretical chemistry discipline. Mol Inf
30(1):20–32. doi:10.1002/minf.201000100
2. Gasteiger J, Engel T (2003) Chemoinformatics: a textbook. Wiley-VCH, Weinheim
3. Gasteiger J (2003) Handbook of chemoinformatics: from data to knowledge. Wiley-VCH,
Weinheim
4. Varnek A, Baskin I (2012) Machine learning methods for property prediction in chemoinformatics: quo vadis? J Chem Inf Mod 52(6):1413–1437. doi:10.1021/ci200409x
5. Vapnik V (1998) Statistical learning theory. Wiley-Interscience, New York
6. Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT, Cambridge
7. Zhokhova NI, Baskin II, Bakhronov DK, Palyulin VA, Zefirov NS (2009) Method of continuous molecular fields in the search for quantitative structure-activity relationships. Dokl Chem
429(1):273–276
8. Baskin II, Zhokhova NI (2013) The continuous molecular fields approach to building 3DQSAR models. J Comput-Aided Mol Des 27(5):427–442. doi:10.1007/s10822-013-9656-4
9. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. doi:10.1007/
bf00994018
10. Saunders C, Gammerman A, Vovk V (1998) Ridge regression learning algorithm in dual variables. In: proceedings of the Fifteenth International Conference on Machine Learning (ICML98). Morgan Kaufmann, Burlington, pp 515–521
11. Rasmussen CE, Williams CKI (2006) Gaussian processes in machine learning. Adaptive computation and machine learning. MIT, Cambridge
12. Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471
13. Karpov PV, Baskin II, Zhokhova NI, Zefirov NS (2011) Method of continuous molecular
fields in the one-class classification task. Dokl Chem 440(2):263–265
14. Karpov PV, Baskin II, Zhokhova NI, Nawrozkij MB, Zefirov AN, Yablokov AS, Novakov IA,
Zefirov NS (2011) One-class approach: models for virtual screening of non-nucleoside HIV1 reverse transcriptase inhibitors based on the concept of continuous molecular fields. Russ
Chem Bull 60(11):2418–2424. doi:10.1007/s11172-011-0372-8
15. Kubinyi H (ed) (2000) 3D QSAR in drug design. Volume 1: theory methods and applications
(Three-dimensional quantitative structure activity relationships). Kluwer/Escom, Dordrecht
16. Kubinyi H, Folkers G, Martin YC (eds) (2002a) 3D QSAR in drug design. Volume 2: ligandprotein Interactions and Molecular Similarity. Kluwer Academic Publishers, Dordrecht
17. Kubinyi H, Folkers G, Martin YC (eds) (2002b) 3D QSAR in drug design. Volume 3: Recent
advances. Kluwer Academic Publishers, Dordrecht
18. Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA).
1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110(18):5959–
5967. doi:10.1021/ja00226a005
19. Testa B, Carrupt PA, Gaillard P, Billois F, Weber P (1996) Lipophilicity in molecular modeling. Pharm Res 13(3):335–343. doi:10.1023/a:1016024005429
20. Kim KH, Greco G, Novellino E, Silipo C, Vittoria A (1993) Use of the hydrogen bond potential function in a comparative molecular field analysis (CoMFA) on a set of benzodiazepines.
J Comput-Aided Mol Des 7(3):263–280
21. Waller CL, Marshall GR (1993) Three-dimensional quantitative structure-activity relationship of angiotesin-converting enzyme and thermolysin inhibitors. II. A comparison of
CoMFA models incorporating molecular orbital fields and desolvation free energies based on
active-analog and complementary-receptor-field alignment rules. J Med Chem 36(16):2390–
2403
22. Kellogg GE (1996) E-state fields: applications to 3D QSAR. J Comput-Aided Mol Des
10(6):513–520