23. Kapetanovic IM (2008) Computer-aided drug discovery and development (CADDD): in
silico-chemico-biological approach. Chem Biol Inter 171:165–176
24. Karelson M, Lobanov VS, Katritzky AR (1996) Quantum-chemical descriptors in QSAR/
QSPR studies. Chem Rev 96:1027–1044
25. Gozalbes R, Doucet JP, Derouin F (2002) Application of topological descriptors in QSAR
and drug design: history and new trends. Curr Drug Targets Infect Disord 2:93–102
26. Perkins R, Fang H, Tong W et al (2003) Quantitative structure-activity relationship methods:
perspectives on drug discovery and toxicology. Environ Toxicol Chem 22:1666–1679
27. Kitchen DB, Decornez H, Furr JR (2004) Docking and scoring in virtual screening for drug
discovery: methods and applications. Nat Rev Drug Discov 3:935–949
28. Paul N, Kellenberger E, Bret G et al (2004) Recovering the true targets of specific ligands by
virtual screening of the protein data bank. Proteins 54:671–680
29. Kharkar PS, Warrier S, Gaud RS (2014) Reverse docking: a powerful tool for drug
repositioning and drug rescue. Future Med Chem 6:333–342
30. Ravindra GK, Srivani P, Achaiah G et al (2007) Strategies to design pyrazolyl urea
derivatives for p38 kinase inhibition: a molecular modeling study. J Comput Aided Mol Des
25:155–166
31. Srivastava HK, Bohari M, Sastry GN (2012) Modeling anti-HIV compounds: the role of
analogue based approaches. Curr Comput Aided Drug Des 8:224–248
32. Gaur AS, Bhardwaj A, Sharma A et al (2017) Assessing therapeutic potential of molecules:
molecular property diagnostic suite for tuberculosis (MPDS
TB
). J Chem Sci 129:515
33. Yang SY (2010) Pharmacophore modeling and applications in drug discovery: challenges
and recent advances. Drug Discov Today 15:444–450
34. Braga RC, Andrade CH (2013) Assessing the performance of 3D pharmacophore models in
virtual screening: how good are they? Curr Topics Med Chem 13:1127–1138
35. Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound
ligands and their use as virtual screening filters. J Chem Inf Model 45:160–169
36. Koutsoukas A, Simms B, Kirchmair J et al (2011) From in silico target prediction to
multi-target drug design: current databases, methods and applications. J Proteomics
74:2554–2574
37. Ehrlich P (1909) Ueber den jetzigen Stand der Chemotherapie. Ber Dtsch Chem Ges 42:17–47
38. Ehrlich P, Morgenroth J. Über Haemolysine (1900) Dritte Mitteilung. Berl Klin Wochnschr
37:453–457
39. Wermuth CG, Ganellin CR, Lindberg P et al (1998) Glossary of terms used in medicinal
chemistry (IUPAC recommendations 1997). Annu Rep Med Chem 33:385–395
40. Güner OF (ed) (2000) Pharmacophore perception, development, and use in drug design, vol
2. Internat’l University Line
41. Bush B, Sheridan RJ (1993) PATTY: a programmable atom type and language for automatic
classification of atoms in molecular databases. Chem Inf Comput Sci 33:756–762
42. Greene J, Kahn S, Savoj H et al (1994) Chemical function queries for 3D database search.
J Chem Inf Comput Sci 34:1297–1308
43. Molecular Simulations Inc. (MSI), “Catalyst Software”. http://www.accelrys.com/about/msi.
html
44. Wang T, Zhou JJ (1998) 3DFS: a new 3D flexible searching system for use in drug design.
Chem Inf Comput Sci 38:71–77
45. Pickett S, Mason J, McLay IJ (1996) Diversity profiling and design using 3D
pharmacophores: pharmacophore-derived queries (PDQ). Chem Inf Comput Sci 36:1214–
1223
46. Dixon SL, Smondyrev AM, Knoll EH et al (2006) PHASE: a new engine for pharmacophore
perception, 3D QSAR model development, and 3D database screening: 1. Methodology and
preliminary results. J Comput Aided Mol Des 20:647–671
47. Schueler FW (1946) Sex hormonal action and chemical constitution. Science 103:221–223
48. Beckett AH (1959) Stereochemical factors in biological activity. In: Fortschritte der
Arzneimittel Forschung. Birkhäuser Verlag, Basel, pp 455–530
50
C. Choudhury and G. Narahari Sastry
silico-chemico-biological approach. Chem Biol Inter 171:165–176
24. Karelson M, Lobanov VS, Katritzky AR (1996) Quantum-chemical descriptors in QSAR/
QSPR studies. Chem Rev 96:1027–1044
25. Gozalbes R, Doucet JP, Derouin F (2002) Application of topological descriptors in QSAR
and drug design: history and new trends. Curr Drug Targets Infect Disord 2:93–102
26. Perkins R, Fang H, Tong W et al (2003) Quantitative structure-activity relationship methods:
perspectives on drug discovery and toxicology. Environ Toxicol Chem 22:1666–1679
27. Kitchen DB, Decornez H, Furr JR (2004) Docking and scoring in virtual screening for drug
discovery: methods and applications. Nat Rev Drug Discov 3:935–949
28. Paul N, Kellenberger E, Bret G et al (2004) Recovering the true targets of specific ligands by
virtual screening of the protein data bank. Proteins 54:671–680
29. Kharkar PS, Warrier S, Gaud RS (2014) Reverse docking: a powerful tool for drug
repositioning and drug rescue. Future Med Chem 6:333–342
30. Ravindra GK, Srivani P, Achaiah G et al (2007) Strategies to design pyrazolyl urea
derivatives for p38 kinase inhibition: a molecular modeling study. J Comput Aided Mol Des
25:155–166
31. Srivastava HK, Bohari M, Sastry GN (2012) Modeling anti-HIV compounds: the role of
analogue based approaches. Curr Comput Aided Drug Des 8:224–248
32. Gaur AS, Bhardwaj A, Sharma A et al (2017) Assessing therapeutic potential of molecules:
molecular property diagnostic suite for tuberculosis (MPDS
TB
). J Chem Sci 129:515
33. Yang SY (2010) Pharmacophore modeling and applications in drug discovery: challenges
and recent advances. Drug Discov Today 15:444–450
34. Braga RC, Andrade CH (2013) Assessing the performance of 3D pharmacophore models in
virtual screening: how good are they? Curr Topics Med Chem 13:1127–1138
35. Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound
ligands and their use as virtual screening filters. J Chem Inf Model 45:160–169
36. Koutsoukas A, Simms B, Kirchmair J et al (2011) From in silico target prediction to
multi-target drug design: current databases, methods and applications. J Proteomics
74:2554–2574
37. Ehrlich P (1909) Ueber den jetzigen Stand der Chemotherapie. Ber Dtsch Chem Ges 42:17–47
38. Ehrlich P, Morgenroth J. Über Haemolysine (1900) Dritte Mitteilung. Berl Klin Wochnschr
37:453–457
39. Wermuth CG, Ganellin CR, Lindberg P et al (1998) Glossary of terms used in medicinal
chemistry (IUPAC recommendations 1997). Annu Rep Med Chem 33:385–395
40. Güner OF (ed) (2000) Pharmacophore perception, development, and use in drug design, vol
2. Internat’l University Line
41. Bush B, Sheridan RJ (1993) PATTY: a programmable atom type and language for automatic
classification of atoms in molecular databases. Chem Inf Comput Sci 33:756–762
42. Greene J, Kahn S, Savoj H et al (1994) Chemical function queries for 3D database search.
J Chem Inf Comput Sci 34:1297–1308
43. Molecular Simulations Inc. (MSI), “Catalyst Software”. http://www.accelrys.com/about/msi.
html
44. Wang T, Zhou JJ (1998) 3DFS: a new 3D flexible searching system for use in drug design.
Chem Inf Comput Sci 38:71–77
45. Pickett S, Mason J, McLay IJ (1996) Diversity profiling and design using 3D
pharmacophores: pharmacophore-derived queries (PDQ). Chem Inf Comput Sci 36:1214–
1223
46. Dixon SL, Smondyrev AM, Knoll EH et al (2006) PHASE: a new engine for pharmacophore
perception, 3D QSAR model development, and 3D database screening: 1. Methodology and
preliminary results. J Comput Aided Mol Des 20:647–671
47. Schueler FW (1946) Sex hormonal action and chemical constitution. Science 103:221–223
48. Beckett AH (1959) Stereochemical factors in biological activity. In: Fortschritte der
Arzneimittel Forschung. Birkhäuser Verlag, Basel, pp 455–530
50
C. Choudhury and G. Narahari Sastry