Combinatorial Drug Discovery
from Activity-Related Substructure
Identification
Md. Imbesat Hassan Rizvi, Chandan Raychaudhury and Debnath Pal
Abstract A newly developed drug discovery method composed of graph theoretical approaches for generating structures combinatorially from an activity-related
root vertex, prediction of activity using topological distance-based vertex index and
a rule-based algorithm and prioritization of putative active compounds using a
newly defined Molecular Priority Score (MPS) has been described in this chapter.
The rule-based method is also used for identifying suitable activity-related vertices
(atoms) present in the active compounds of a data set, and identified vertex is used
for combinatorial generation of structures. An algorithm has also been described for
identifying suitable training set–test set splits (combinations) for a given data set
since getting a suitable training set is of utmost importance for getting acceptable
activity prediction. The method has also been used, to our knowledge for the first
time, for matching and searching rooted trees and sub-trees in the compounds of a
data set to discover novel drug candidates. The performance of different modules of
the proposed method has been investigated by considering two different series
of bioactive compounds: (1) convulsant and anticonvulsant barbiturates and
(2) nucleoside analogues with their activities against HIV and a data set of 3779
potential antitubercular compounds. While activity prediction, compound prioritization and structure generation studies have been carried out for barbiturates and
nucleoside analogues, activity-related tree–sub-tree searching in the said data set
has been carried for screening potential antitubercular compounds. All the results
show a high level of success rate. The possible relation of this work with scaffold
hopping and inverse quantitative structure–activity relationship (iQSAR) problem
has also been discussed. This newly developed method seems to hold promise for
discovering novel therapeutic candidates.
Keywords Graph theory Á Vertex index of molecular graph Á Root vertex
Combinatorial molecular structure generation Á Activity prediction
Compound prioritization and screening Á Drug discovery
Md.I. H. Rizvi Á C. Raychaudhury Á D. Pal (&)
Department of Computational and Data Sciences, Indian Institute of Science,
Bangalore 560012, India
e-mail: dpal@iisc.ac.in
© Springer Nature Switzerland AG 2019
C. G. Mohan (ed.), Structural Bioinformatics: Applications in Preclinical Drug
Discovery Process, Challenges and Advances in Computational Chemistry
and Physics 27, https://doi.org/10.1007/978-3-030-05282-9_4
71
from Activity-Related Substructure
Identification
Md. Imbesat Hassan Rizvi, Chandan Raychaudhury and Debnath Pal
Abstract A newly developed drug discovery method composed of graph theoretical approaches for generating structures combinatorially from an activity-related
root vertex, prediction of activity using topological distance-based vertex index and
a rule-based algorithm and prioritization of putative active compounds using a
newly defined Molecular Priority Score (MPS) has been described in this chapter.
The rule-based method is also used for identifying suitable activity-related vertices
(atoms) present in the active compounds of a data set, and identified vertex is used
for combinatorial generation of structures. An algorithm has also been described for
identifying suitable training set–test set splits (combinations) for a given data set
since getting a suitable training set is of utmost importance for getting acceptable
activity prediction. The method has also been used, to our knowledge for the first
time, for matching and searching rooted trees and sub-trees in the compounds of a
data set to discover novel drug candidates. The performance of different modules of
the proposed method has been investigated by considering two different series
of bioactive compounds: (1) convulsant and anticonvulsant barbiturates and
(2) nucleoside analogues with their activities against HIV and a data set of 3779
potential antitubercular compounds. While activity prediction, compound prioritization and structure generation studies have been carried out for barbiturates and
nucleoside analogues, activity-related tree–sub-tree searching in the said data set
has been carried for screening potential antitubercular compounds. All the results
show a high level of success rate. The possible relation of this work with scaffold
hopping and inverse quantitative structure–activity relationship (iQSAR) problem
has also been discussed. This newly developed method seems to hold promise for
discovering novel therapeutic candidates.
Keywords Graph theory Á Vertex index of molecular graph Á Root vertex
Combinatorial molecular structure generation Á Activity prediction
Compound prioritization and screening Á Drug discovery
Md.I. H. Rizvi Á C. Raychaudhury Á D. Pal (&)
Department of Computational and Data Sciences, Indian Institute of Science,
Bangalore 560012, India
e-mail: dpal@iisc.ac.in
© Springer Nature Switzerland AG 2019
C. G. Mohan (ed.), Structural Bioinformatics: Applications in Preclinical Drug
Discovery Process, Challenges and Advances in Computational Chemistry
and Physics 27, https://doi.org/10.1007/978-3-030-05282-9_4
71