A Review on Parallel Virtual Screening Softwares for High Performance Computers

TL;DR

This review discusses the implementation of parallel algorithms in virtual screening software to accelerate drug discovery processes on high-performance computing systems, focusing on scoring functions, search algorithms, and software performance.

Contribution

It provides a comprehensive overview of parallel virtual screening software, highlighting recent advancements and performance analyses on high-performance computing architectures.

Findings

Parallelization significantly speeds up virtual screening.

Performance varies across different docking software.

High-performance computing enables large-scale drug discovery.

Abstract

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.