inhibitors. A limited study like this brings out the uncertainty in pose and rank
prediction by popular tools.
5.3 Selection of Appropriate Database
Chemical databases are selected from the ensemble of the small organic and synthetic molecules, used for ligand docking, constituents of such chemical libraries
influence the final outcome in the drug designing process. In general, chemical
library databases are created to aid the drug discovery process by providing innovation in new lead structures selection. After the establishment of the in silico drug
designing protocol, chemical databases are screened to identify the probable inhibitors which can be tested by experimental methods. Success rate in finding true
inhibitor by in silico means depends upon both screening protocol and chemical
databases used. So, before the selection of the chemical libraries, basic biological
target specific chemical features should be marked. For the virtual screening purposes, the compound database may be selected in such a manner so that maximum
structurally diverse chemicals can be utilized against the studied biological target(s).
Chemo-informatics tools are mainly used not only for diversity analysis [186, 187]
but also for converting them into focused chemical libraries [188].
Various chemical compounds databases are available which include databases of
general organic compounds intended for screening, drugs, commercial databases,
and databases with known biological activity, crystal structure information, and
various physicochemical properties information [189, 190]. Table 9 shows some of
the commonly used chemical databases which are categorized based on the different
features like associated bioactivity information, known drug information, and
having target specific information. Most of these databases provide chemical
information using 1D representatives such as SMILE and InChI Key, or 2D
structural coordinate information stored in SD file format. These databases are also
provided online interface to access the whole chunk of chemical compounds for
similarity-based screening. These functionalities intended to search close analogues
of known bioactive compounds and thereby advances the lead optimization process.
Though different chemical databases are available for virtual high-throughput
screening (vHTS), it is recommended to convert any chemical library to “target or
focus” chemical library to avoid the false hits selection as novel inhibitor [191]. In the
literature, several characteristic properties of small molecules have been discussed
that are followed by the “lead-or drug-like” molecules and are considered to be
important for a drug to be successful [192]. Currently, list of open-source
Chemoinformatics tools is available which can be utilized for drug-like properties
calculation and chemical databases filtration [193]. Well-known physicochemical
properties which are used as empirical rules are Lipinski’s “Rule of Five” [194], “Rule
of Three” [195], and Pfizer’s “Rule of 3/75” [196] (Table 10). Apart from filtering for
lead-like properties, it is also important to exclude known toxicophores or metabolically liable moieties which can interfere with the assay and detection protocol.
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prediction by popular tools.
5.3 Selection of Appropriate Database
Chemical databases are selected from the ensemble of the small organic and synthetic molecules, used for ligand docking, constituents of such chemical libraries
influence the final outcome in the drug designing process. In general, chemical
library databases are created to aid the drug discovery process by providing innovation in new lead structures selection. After the establishment of the in silico drug
designing protocol, chemical databases are screened to identify the probable inhibitors which can be tested by experimental methods. Success rate in finding true
inhibitor by in silico means depends upon both screening protocol and chemical
databases used. So, before the selection of the chemical libraries, basic biological
target specific chemical features should be marked. For the virtual screening purposes, the compound database may be selected in such a manner so that maximum
structurally diverse chemicals can be utilized against the studied biological target(s).
Chemo-informatics tools are mainly used not only for diversity analysis [186, 187]
but also for converting them into focused chemical libraries [188].
Various chemical compounds databases are available which include databases of
general organic compounds intended for screening, drugs, commercial databases,
and databases with known biological activity, crystal structure information, and
various physicochemical properties information [189, 190]. Table 9 shows some of
the commonly used chemical databases which are categorized based on the different
features like associated bioactivity information, known drug information, and
having target specific information. Most of these databases provide chemical
information using 1D representatives such as SMILE and InChI Key, or 2D
structural coordinate information stored in SD file format. These databases are also
provided online interface to access the whole chunk of chemical compounds for
similarity-based screening. These functionalities intended to search close analogues
of known bioactive compounds and thereby advances the lead optimization process.
Though different chemical databases are available for virtual high-throughput
screening (vHTS), it is recommended to convert any chemical library to “target or
focus” chemical library to avoid the false hits selection as novel inhibitor [191]. In the
literature, several characteristic properties of small molecules have been discussed
that are followed by the “lead-or drug-like” molecules and are considered to be
important for a drug to be successful [192]. Currently, list of open-source
Chemoinformatics tools is available which can be utilized for drug-like properties
calculation and chemical databases filtration [193]. Well-known physicochemical
properties which are used as empirical rules are Lipinski’s “Rule of Five” [194], “Rule
of Three” [195], and Pfizer’s “Rule of 3/75” [196] (Table 10). Apart from filtering for
lead-like properties, it is also important to exclude known toxicophores or metabolically liable moieties which can interfere with the assay and detection protocol.
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
145