mapping of a compound with a pharmacophore model which is often given by the
RMSD between the feature of a model and atoms of the target molecule does not
stand accurate as it does not take an account of similarity with the known active
molecules [114]. Especially, the ligand-based pharmacophore models do not consider the overall compatibility with the receptor, thus sometimes end up with
screening molecules those are very different from the other active compounds, with
a completely different set of functional groups not complementary with the receptor.
The pharmacophore-based searches against the compound databases lack fast
conformation sampling as most of the programmes rely on conformer databases
having only a limited number of energetically favourable conformations of molecules [115, 116]. There is a possibility of missing an active molecule if a suitable
conformation is not available. So, it is desirable to generate as many low-energy
conformers as possible for the database compounds, but again it would consume a
lot of computational time. Especially for the rotatable bonds of small hydroxyl
groups, it is difficult to sample all the different rotations.
9 Summary
Evolving from a simple concept to a well-validated and widely exploited method,
the pharmacophore modelling approaches have been an essential part of many drug
discovery strategies. The pharmacophore-based approaches are well known for their
strength to propose a diverse set of molecules having diverse molecular frameworks
but owing to a desired biological activity for one target. It has been extensively
applied for virtual screening, lead optimization, target identification, toxicity prediction and de novo lead design, and it has ways to go [117]. Considering the
strengths and limitations of the pharmacophore approaches, it can either be used
alone to identify potential functional group substituents in molecules, design new
molecules specific for a target by scaffold hopping keeping the substituents with
certain pharmacophoric feature and orientation constant virtually screen for inhibitors, perform ADMET profiling of compounds, investigate possible off-targets or
can be applied as a complementing approach along with other methods like docking
and QSAR. The concept can be sensibly applied for fragment-based drug design,
characterization of protein–protein interaction interfaces and target-based classification of chemical space. In this chapter, we touched upon the basic concepts and
methods of generation of pharmacophore models. The diverse applications of the
pharmacophore approaches exemplified though a number of case studies are
believed to be useful for the readers. However, we believe that the choice and way of
application of the method depends on the research problem and the type of initial
data available.
Acknowledgements CC and GNS thank the Department of Science and Technology (DST),
Government of India, for financial support in the forms of DST-INSPIRE Faculty Award [DST/
INSPIRE/04/2016/000732] and JC Bose Fellowship, respectively.
48
C. Choudhury and G. Narahari Sastry
RMSD between the feature of a model and atoms of the target molecule does not
stand accurate as it does not take an account of similarity with the known active
molecules [114]. Especially, the ligand-based pharmacophore models do not consider the overall compatibility with the receptor, thus sometimes end up with
screening molecules those are very different from the other active compounds, with
a completely different set of functional groups not complementary with the receptor.
The pharmacophore-based searches against the compound databases lack fast
conformation sampling as most of the programmes rely on conformer databases
having only a limited number of energetically favourable conformations of molecules [115, 116]. There is a possibility of missing an active molecule if a suitable
conformation is not available. So, it is desirable to generate as many low-energy
conformers as possible for the database compounds, but again it would consume a
lot of computational time. Especially for the rotatable bonds of small hydroxyl
groups, it is difficult to sample all the different rotations.
9 Summary
Evolving from a simple concept to a well-validated and widely exploited method,
the pharmacophore modelling approaches have been an essential part of many drug
discovery strategies. The pharmacophore-based approaches are well known for their
strength to propose a diverse set of molecules having diverse molecular frameworks
but owing to a desired biological activity for one target. It has been extensively
applied for virtual screening, lead optimization, target identification, toxicity prediction and de novo lead design, and it has ways to go [117]. Considering the
strengths and limitations of the pharmacophore approaches, it can either be used
alone to identify potential functional group substituents in molecules, design new
molecules specific for a target by scaffold hopping keeping the substituents with
certain pharmacophoric feature and orientation constant virtually screen for inhibitors, perform ADMET profiling of compounds, investigate possible off-targets or
can be applied as a complementing approach along with other methods like docking
and QSAR. The concept can be sensibly applied for fragment-based drug design,
characterization of protein–protein interaction interfaces and target-based classification of chemical space. In this chapter, we touched upon the basic concepts and
methods of generation of pharmacophore models. The diverse applications of the
pharmacophore approaches exemplified though a number of case studies are
believed to be useful for the readers. However, we believe that the choice and way of
application of the method depends on the research problem and the type of initial
data available.
Acknowledgements CC and GNS thank the Department of Science and Technology (DST),
Government of India, for financial support in the forms of DST-INSPIRE Faculty Award [DST/
INSPIRE/04/2016/000732] and JC Bose Fellowship, respectively.
48
C. Choudhury and G. Narahari Sastry