(4) screening of small molecules against target protein, i.e., hit to lead identification,
(5) lead optimization, (6) preclinical testing, and (7) clinical testing.
Almost all these steps generate huge data from experimental laboratory and
computational laboratory experimentations and need better way of handling data
with fast and better analytics approaches. Target identification and validation
involve selection of protein molecules whose activity when blocked or enhanced
can affect the particular disease-related cellular pathway. This involves a systems
biology approach wherein an understanding of all the proteins involved in the
pathway or finding possibility of any alternate pathway available, role of particular
protein in particular pathway and identifying side effects of the target protein.
Second most important thing is to have database of lakhs of small molecules which
can be screened against the target protein. The source of these small molecules can
be microbial metabolites, plant origin, and chemically synthesized. There are various drug molecule databases, i.e., Chemspider [18], DrugBank [19], ZINC [20] to
name a few which are already available.
The technique to screen these lakhs of molecules to a target protein is performed
using molecular docking. The screening process should be fast enough, which
demands the use of and better computational or programming techniques. Each of
these molecules tends to have conformational flexibility which in turn makes the
docking process more time-consuming. Choice of efficient force field and scoring
methodologies also plays an important role in screening of these molecules. In order
to achieve this, high-throughput docking methods have been developed. Although,
the analysis of these docked conformations to choose the best ligand becomes a
big data analytics problem as it involves finding of various parameters and several
interactions between the target protein and the docked ligand.
Docking or screening projects a static picture of the binding of ligand with the
receptor [21]. However, the dynamic picture would be obtained from the molecular
dynamics simulations which provide an understanding of the flexibility of protein
and ligand. Molecular dynamics simulation gives an insight about various intermolecular interactions and binding affinities between protein-ligand complex,
thereby ensuing binding efficiency [16]. Molecular docking followed by simulations generates huge molecular trajectories data. Thus, the management and fast
analytics of this data have become the need of the hour.
The upcoming area of drug repurposing is again proving to be a bigger computational task, and it has the potential to deliver a drug molecule for a chosen
disease [22, 23]. Various pharmaceuticals and R&D laboratories are working on
drug repurposing which involves docking of already approved FDA drugs on new
target protein. The involvement of FDA-approved drugs suggests that they have
been already tested on humans for their toxicity and pharmacology. Hence, rejection of such drugs due to toxicity is ruled out, and entire duration required for the
drug discovery process can be shortened by few years. HPC-based molecular
docking and molecular dynamics simulations pose a challenging role in this area of
drug repurposing.
In order to manage this rapidly increasing data and efficient analysis, there is
need to develop tools with parallelization and thereby enhance the overall
350
R. R. Joshi et al.
(5) lead optimization, (6) preclinical testing, and (7) clinical testing.
Almost all these steps generate huge data from experimental laboratory and
computational laboratory experimentations and need better way of handling data
with fast and better analytics approaches. Target identification and validation
involve selection of protein molecules whose activity when blocked or enhanced
can affect the particular disease-related cellular pathway. This involves a systems
biology approach wherein an understanding of all the proteins involved in the
pathway or finding possibility of any alternate pathway available, role of particular
protein in particular pathway and identifying side effects of the target protein.
Second most important thing is to have database of lakhs of small molecules which
can be screened against the target protein. The source of these small molecules can
be microbial metabolites, plant origin, and chemically synthesized. There are various drug molecule databases, i.e., Chemspider [18], DrugBank [19], ZINC [20] to
name a few which are already available.
The technique to screen these lakhs of molecules to a target protein is performed
using molecular docking. The screening process should be fast enough, which
demands the use of and better computational or programming techniques. Each of
these molecules tends to have conformational flexibility which in turn makes the
docking process more time-consuming. Choice of efficient force field and scoring
methodologies also plays an important role in screening of these molecules. In order
to achieve this, high-throughput docking methods have been developed. Although,
the analysis of these docked conformations to choose the best ligand becomes a
big data analytics problem as it involves finding of various parameters and several
interactions between the target protein and the docked ligand.
Docking or screening projects a static picture of the binding of ligand with the
receptor [21]. However, the dynamic picture would be obtained from the molecular
dynamics simulations which provide an understanding of the flexibility of protein
and ligand. Molecular dynamics simulation gives an insight about various intermolecular interactions and binding affinities between protein-ligand complex,
thereby ensuing binding efficiency [16]. Molecular docking followed by simulations generates huge molecular trajectories data. Thus, the management and fast
analytics of this data have become the need of the hour.
The upcoming area of drug repurposing is again proving to be a bigger computational task, and it has the potential to deliver a drug molecule for a chosen
disease [22, 23]. Various pharmaceuticals and R&D laboratories are working on
drug repurposing which involves docking of already approved FDA drugs on new
target protein. The involvement of FDA-approved drugs suggests that they have
been already tested on humans for their toxicity and pharmacology. Hence, rejection of such drugs due to toxicity is ruled out, and entire duration required for the
drug discovery process can be shortened by few years. HPC-based molecular
docking and molecular dynamics simulations pose a challenging role in this area of
drug repurposing.
In order to manage this rapidly increasing data and efficient analysis, there is
need to develop tools with parallelization and thereby enhance the overall
350
R. R. Joshi et al.