9. Gálvez J, García-Domenech R (2010) On the contribution of molecular topology to drug
design and discovery. Curr Comput Aided Drug Des 6:252–268
10. Gugisch R, Kerber A, Kohnert A, Laue R, Meringer M, Rücker C, Wassermann A (2014)
MOLGEN 5.0, a molecular structure generator. In: Advances in mathematical chemistry and
applications, vol 1. Bentham Publishers, pp 113–138
11. Harary F (1972) Graph theory. Addison-Wesley
12. Faulon JL, Bender A (2010) Handbook of chemoinformatics algorithms. CRC press
13. Wong WW, Burkowski FJ (2009) A constructive approach for discovering new drug leads:
using a kernel methodology for the inverse-QSAR problem. J Cheminform 1:4
14. Klopman G (1994) Artificial intelligence approach to structure-activity studies: computer
automated structure evaluation of biological activity of organic molecules. J Am Chem Soc
106:7315–7321
15. Raychaudhury C, Rizvi MIH, Pal D (2018) Combinatorial design of molecule using
activity-linked substructural topological information as applied to antitubercular compounds.
Curr Comput Aided Drug Des https://doi.org/10.2174/1573409914666180509152711
16. Beyer T, Hedetniemi SM (1980) Constant time generation of rooted trees. SIAM J Comput
9:706–712
17. Gibbs NE (1969) A cycle generation algorithm for finite undirected linear graphs. J ACM
16:564–568
18. Klopman G, Raychaudhury C (1990) Vertex indexes of molecular graphs in structure-activity
relationships: a study of the convulsant-anticonvulsant activity of barbiturates and the
carcinogenicity of unsubstituted polycyclic aromatic hydrocarbons. J Chem Inf Comput Sci
30:12–19
19. Raychaudhury C, Pal D (2012) Use of vertex index in structure-activity analysis and design of
molecules. Curr Comput Aided Drug Des 8:128–134
20. Raychaudhury C, Klopman G (1990) New vertex indices and their applications in evaluating
antileukemic activity of 9-anilinoacridines and the activity of 2′, 3′-dideoxy-nuclosides
against HIV. Bull Soc Chim Belg 99:255–264
21. Raychaudhury C, Dey I, Bag P, Biswas G, Das B, Roy P, Banerjee A(1993) Use of a rule
based graph-theoretical system in evaluating the activity of a class of nucleoside analogues
against human immunodeficiency virus. Arzneim Forsch Drug Res 43:1122–1125
22. Prathipati P, Ma NL, Keller TH (2008) Global bayesian models for the prioritization of
antitubercular agents. J Chem Inf Model 48:2362–2370
23. GTB data set. http://pallab.cds.iisc.ac.in/gtb_data.mol
24. Kandel DD, Raychaudhury C, Pal D (2014) Two new atom centered fragment descriptors and
scoring function enhance classification of antibacterial activity. J Mol Model 20:2164
25. Raychaudhury C, Kandel DD, Pal D (2014) Role of vertex index in substructure identification
and activity prediction: a study on antitubercular activity of a series of acid alkyl ester
derivatives. Croat Chem Acta 87:39–47
26. Moss G (1999) Extension and revision of the von Baeyer system for naming polycyclic
compounds (including bicyclic compounds). Pure Appl Chem 71:513–529
27. Weininger D, Weininger A, Weininger JL (1989) SMILES. 2. Algorithm for generation of
unique SMILES notation. J Chem Inf Comput Sci 29:97–101
108
Md.I. H. Rizvi et al.
design and discovery. Curr Comput Aided Drug Des 6:252–268
10. Gugisch R, Kerber A, Kohnert A, Laue R, Meringer M, Rücker C, Wassermann A (2014)
MOLGEN 5.0, a molecular structure generator. In: Advances in mathematical chemistry and
applications, vol 1. Bentham Publishers, pp 113–138
11. Harary F (1972) Graph theory. Addison-Wesley
12. Faulon JL, Bender A (2010) Handbook of chemoinformatics algorithms. CRC press
13. Wong WW, Burkowski FJ (2009) A constructive approach for discovering new drug leads:
using a kernel methodology for the inverse-QSAR problem. J Cheminform 1:4
14. Klopman G (1994) Artificial intelligence approach to structure-activity studies: computer
automated structure evaluation of biological activity of organic molecules. J Am Chem Soc
106:7315–7321
15. Raychaudhury C, Rizvi MIH, Pal D (2018) Combinatorial design of molecule using
activity-linked substructural topological information as applied to antitubercular compounds.
Curr Comput Aided Drug Des https://doi.org/10.2174/1573409914666180509152711
16. Beyer T, Hedetniemi SM (1980) Constant time generation of rooted trees. SIAM J Comput
9:706–712
17. Gibbs NE (1969) A cycle generation algorithm for finite undirected linear graphs. J ACM
16:564–568
18. Klopman G, Raychaudhury C (1990) Vertex indexes of molecular graphs in structure-activity
relationships: a study of the convulsant-anticonvulsant activity of barbiturates and the
carcinogenicity of unsubstituted polycyclic aromatic hydrocarbons. J Chem Inf Comput Sci
30:12–19
19. Raychaudhury C, Pal D (2012) Use of vertex index in structure-activity analysis and design of
molecules. Curr Comput Aided Drug Des 8:128–134
20. Raychaudhury C, Klopman G (1990) New vertex indices and their applications in evaluating
antileukemic activity of 9-anilinoacridines and the activity of 2′, 3′-dideoxy-nuclosides
against HIV. Bull Soc Chim Belg 99:255–264
21. Raychaudhury C, Dey I, Bag P, Biswas G, Das B, Roy P, Banerjee A(1993) Use of a rule
based graph-theoretical system in evaluating the activity of a class of nucleoside analogues
against human immunodeficiency virus. Arzneim Forsch Drug Res 43:1122–1125
22. Prathipati P, Ma NL, Keller TH (2008) Global bayesian models for the prioritization of
antitubercular agents. J Chem Inf Model 48:2362–2370
23. GTB data set. http://pallab.cds.iisc.ac.in/gtb_data.mol
24. Kandel DD, Raychaudhury C, Pal D (2014) Two new atom centered fragment descriptors and
scoring function enhance classification of antibacterial activity. J Mol Model 20:2164
25. Raychaudhury C, Kandel DD, Pal D (2014) Role of vertex index in substructure identification
and activity prediction: a study on antitubercular activity of a series of acid alkyl ester
derivatives. Croat Chem Acta 87:39–47
26. Moss G (1999) Extension and revision of the von Baeyer system for naming polycyclic
compounds (including bicyclic compounds). Pure Appl Chem 71:513–529
27. Weininger D, Weininger A, Weininger JL (1989) SMILES. 2. Algorithm for generation of
unique SMILES notation. J Chem Inf Comput Sci 29:97–101
108
Md.I. H. Rizvi et al.