HHCPF Hexadecahydro-1H-Cyclopenta[a]Phenanthrene Framework
HTS
High-throughput screening
MD
Molecular dynamics
Mtb
Mycobacterium tuberculosis
QSAR
Quantitative structure-activity relationship
TB
Tuberculosis
1 Introduction
Rational drug discovery is highly interdisciplinary and is one of the outstanding
challenges, besides being highly arduous and expensive. The process of designing
new medications requires investment of roughly 14 years [1] of time and cost as
high as 1 billion USD [2]. Along with rapidly evolving HTS [3] and combinatorial
chemistry technologies, computer-aided drug design (CADD) strategies are also
effectively contributing to accelerate and economize the process of drug development [4–6]. A broad range of CADD applications are employed at almost all early
stages of the drug discovery pipelines, starting from target identification, target
structure prediction, screening of initial hits to prioritization and optimization of
leads and understanding their structure-property relationships [7, 8]. We have been
working in state-of-the-art CADD techniques such as homology modelling [9],
molecular dynamics simulations [10–12], QSAR [13–15], molecular docking [16],
pharmacophore modelling [17], virtual screening [18, 19] and cheminformatics [20]
since more than a decade. One of the fundamental applications of cheminformatics
is to develop programmes that store, manage and retrieve molecular structures in
various formats, their calculated/experimental properties and bioactivities.
Cheminformatics also involves computing molecular fingerprints and descriptors
based on the molecular structures that label a physicochemical property and can be
used as screening filters [21, 22]. These molecular descriptors of known active
molecules can also be used to develop quantitative structure-activity/property
relationship (QSAR/QSPR) models to predict the inhibitory activity or toxicity of
novel compounds and preliminarily profile them in silico without performing
expensive in vitro and in vivo assays [23–26]. Docking and simulations predict the
three-dimensional binding mode of a given molecule in the binding site of a
macromolecular receptor (protein/DNA), and their affinity is quantitatively assessed
by a docking score. This technique has not only been proved enormously useful to
study receptor–ligand interactions but also is used as a popular tool to virtually
screen compound libraries to obtain a hit or to identify the target for a molecule by
reverse engineering [27–29]. A large number of studies from our group have
focused on application of these techniques to a plethora of drug targets such as
phosphodiesterases [14], kinases [12, 30], HIV proteases [10, 13] and reverse
transcriptase [31] and Mtb cyclopropane synthases [11, 17, 18]. We have also
26
C. Choudhury and G. Narahari Sastry
HTS
High-throughput screening
MD
Molecular dynamics
Mtb
Mycobacterium tuberculosis
QSAR
Quantitative structure-activity relationship
TB
Tuberculosis
1 Introduction
Rational drug discovery is highly interdisciplinary and is one of the outstanding
challenges, besides being highly arduous and expensive. The process of designing
new medications requires investment of roughly 14 years [1] of time and cost as
high as 1 billion USD [2]. Along with rapidly evolving HTS [3] and combinatorial
chemistry technologies, computer-aided drug design (CADD) strategies are also
effectively contributing to accelerate and economize the process of drug development [4–6]. A broad range of CADD applications are employed at almost all early
stages of the drug discovery pipelines, starting from target identification, target
structure prediction, screening of initial hits to prioritization and optimization of
leads and understanding their structure-property relationships [7, 8]. We have been
working in state-of-the-art CADD techniques such as homology modelling [9],
molecular dynamics simulations [10–12], QSAR [13–15], molecular docking [16],
pharmacophore modelling [17], virtual screening [18, 19] and cheminformatics [20]
since more than a decade. One of the fundamental applications of cheminformatics
is to develop programmes that store, manage and retrieve molecular structures in
various formats, their calculated/experimental properties and bioactivities.
Cheminformatics also involves computing molecular fingerprints and descriptors
based on the molecular structures that label a physicochemical property and can be
used as screening filters [21, 22]. These molecular descriptors of known active
molecules can also be used to develop quantitative structure-activity/property
relationship (QSAR/QSPR) models to predict the inhibitory activity or toxicity of
novel compounds and preliminarily profile them in silico without performing
expensive in vitro and in vivo assays [23–26]. Docking and simulations predict the
three-dimensional binding mode of a given molecule in the binding site of a
macromolecular receptor (protein/DNA), and their affinity is quantitatively assessed
by a docking score. This technique has not only been proved enormously useful to
study receptor–ligand interactions but also is used as a popular tool to virtually
screen compound libraries to obtain a hit or to identify the target for a molecule by
reverse engineering [27–29]. A large number of studies from our group have
focused on application of these techniques to a plethora of drug targets such as
phosphodiesterases [14], kinases [12, 30], HIV proteases [10, 13] and reverse
transcriptase [31] and Mtb cyclopropane synthases [11, 17, 18]. We have also
26
C. Choudhury and G. Narahari Sastry