accuracy and efficiency balance in selecting poses rank-by-number and
percent-by-number are more useful, while for accuracy number-by-number and
vote-by-number approaches are more pertinent to pose selection [207]. GOLD
score and Dock score were poor individually but were useful in consensus scoring
[207]. Consensus score involving all nine scores or five CScore functions were
useful without any optimization and suitable for practical usage [207]. However,
Free energy and empirical scoring has been used together in the recent paper [174].
5.7 Inclusion of Flexibility of Ligand and Receptor
In computer-assisted drug discovery process such as structure-based drug design
and ligand-based drug design, ligand flexibility plays key role for pharmacophore
features extraction and model generation [208], 3D-QSAR analysis [209], molecular docking-based studies [210], shape similarity [211], and so on. In these cases,
the outcome results largely depend upon the ability to achieve those conformers that
represent the bound state. Hence, it is important to achieve bioactive conformational
space of each compounds under study [212]. The term “bioactive conformation
generation” specifies the generation of pool of all possible molecular structures that
are found in the bound state of the complex macromolecules. Various studies
suggest that during the interaction with the receptor, small molecules generally
adopt low-energy conformation [213].
The literature suggests two major classes of methods that are utilized to explore
the conformational landscape of the small molecules [214]. These approaches
include stochastic sampling, systematic or deterministic sampling. Deterministic
approaches attempt to generate full range of minimum energy conformations by
adopting systematic exhaustively space search approach. This type of space search
methods largely dependent upon the number of rotational bonds a small molecule
has. Due to combinatorial explosion in torsion angle combinations, this approach is
feasible only for very small molecules [214]. Stochastic sampling tries to explore
various energy landscapes by incorporating randomness during the search process.
Monte Carlo-type (MC) simulations and genetic algorithms (GAs) are the major
techniques of this type of sampling methods [214]. A detailed review of these
approaches can be found in the following papers [215].
Using above-mentioned approaches, various conformation generation programs
have been developed and utilized in drug discovery process cited in Table 11.
These programs generally adopt heuristics to overcome combinatorial explosion in
case of systematic search and random perturbations and selection in stochastic
search.
Ligand being usually smaller in size with lesser number of rotatable bonds
exhaustive sampling of available conformational space is achievable with current
computational capabilities; but proteins being large macromolecules, available
conformational space is vast due to large number of degrees-of-freedom (DOFs)
and its exhaustive sampling is almost infeasible. Therefore, techniques seeking to
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S. K. Panday and I. Ghosh
percent-by-number are more useful, while for accuracy number-by-number and
vote-by-number approaches are more pertinent to pose selection [207]. GOLD
score and Dock score were poor individually but were useful in consensus scoring
[207]. Consensus score involving all nine scores or five CScore functions were
useful without any optimization and suitable for practical usage [207]. However,
Free energy and empirical scoring has been used together in the recent paper [174].
5.7 Inclusion of Flexibility of Ligand and Receptor
In computer-assisted drug discovery process such as structure-based drug design
and ligand-based drug design, ligand flexibility plays key role for pharmacophore
features extraction and model generation [208], 3D-QSAR analysis [209], molecular docking-based studies [210], shape similarity [211], and so on. In these cases,
the outcome results largely depend upon the ability to achieve those conformers that
represent the bound state. Hence, it is important to achieve bioactive conformational
space of each compounds under study [212]. The term “bioactive conformation
generation” specifies the generation of pool of all possible molecular structures that
are found in the bound state of the complex macromolecules. Various studies
suggest that during the interaction with the receptor, small molecules generally
adopt low-energy conformation [213].
The literature suggests two major classes of methods that are utilized to explore
the conformational landscape of the small molecules [214]. These approaches
include stochastic sampling, systematic or deterministic sampling. Deterministic
approaches attempt to generate full range of minimum energy conformations by
adopting systematic exhaustively space search approach. This type of space search
methods largely dependent upon the number of rotational bonds a small molecule
has. Due to combinatorial explosion in torsion angle combinations, this approach is
feasible only for very small molecules [214]. Stochastic sampling tries to explore
various energy landscapes by incorporating randomness during the search process.
Monte Carlo-type (MC) simulations and genetic algorithms (GAs) are the major
techniques of this type of sampling methods [214]. A detailed review of these
approaches can be found in the following papers [215].
Using above-mentioned approaches, various conformation generation programs
have been developed and utilized in drug discovery process cited in Table 11.
These programs generally adopt heuristics to overcome combinatorial explosion in
case of systematic search and random perturbations and selection in stochastic
search.
Ligand being usually smaller in size with lesser number of rotatable bonds
exhaustive sampling of available conformational space is achievable with current
computational capabilities; but proteins being large macromolecules, available
conformational space is vast due to large number of degrees-of-freedom (DOFs)
and its exhaustive sampling is almost infeasible. Therefore, techniques seeking to
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S. K. Panday and I. Ghosh