Chemistry Reference
In-Depth Information
various targets. It can span pure ligand-based approaches (comparison of known
ligands to predict their most probable targets), pure target-based approaches
(comparison of targets or ligand-binding sites to predict their most likely ligands)
or ultimately target-ligand based approaches to predict binding affinities.
Effectively, chemogenomic techniques can be used to profile small molecules
against large collections of macromolecular structures in order to identify
proteins. These can in turn bind to the referred molecules (target fishing).
Chemogenomics approaches can also determine a global pharmacological profile
(ligand profiling) as complementary ways to increase the productivity of drug
discovery. Typical approaches are poised towards identification of new ligands
for a target. For inverse virtual screening, each single ligand is compared against a
collection of targets. This can yield fruitful drug discovery rewards for ligand
profiling and target fishing [321, 322].
Chemogenomics approaches can also help to identify new molecular targets for
compounds (in clinical trials or for existing drugs called drug repositioning
aggregating more volumes of polypharmacological data). It is of interest to
develop semantic web technologies in order to link different diseases.
Chemogenomic approaches can also be used for modeling and predicting
pharmacokinetics features of compounds, which can be profiled for focused
testing and screening of the promising compounds. This would reduce risks of
late-stage attrition and reduce overall costs.
The chemogenomics approach can provide tools that permit the prediction of
toxicity and crucial ADME-Tox properties including volume of distribution,
clearance, metabolism, plasma protein binding and transporters [321-323].
KNOWLEDGE-BASED DRUG DISCOVERY
Biopharmaceutical companies need more ways to screen for potential problems
with promising molecules at the earliest possible stage. It is important to
streamline the entire process such that compounds move quickly along the
development pipeline. There is great need for more solutions that can transform
data into knowledge.
