  Drug discovery is the most expensive, time demanding and challenging project
in biopharmaceutical companies which aims at the identification and
optimization of lead compounds from large-sized chemical libraries. The lead
compounds should have high affinity binding and specificity for a target
associated with a disease and in addition they should have favorable
pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties).
Overall, drug discovery is a multivariable optimization and can be carried out
in supercomputers using a reliable scoring function which is a measure of
binding affinity or inhibition potential of the drug-like compound. The major
problem is that the number of compounds in the chemical spaces is huge making
the computational drug discovery very demanding. However, it is cheaper and
less time consuming when compared to experimental high throughput screening. As
the problem is to find the most stable (global) minima for numerous
protein-ligand complexes (at the order of 10$^6$ to 10$^{12}$), the parallel
implementation of in-silico virtual screening can be exploited to make the drug
discovery in affordable time. In this review, we discuss such implementations
of parallelization algorithms in virtual screening programs. The nature of
different scoring functions and search algorithms are discussed, together with
a performance analysis of several docking softwares ported on high-performance
computing architectures.
