Computational Approaches in Drug Designing and Their Applications

Abstract

Computational approaches have tremendous potential to speed up the process of drug discovery. There are several tools based on the methods and algorithms of computer science which can be used for structure modeling, cavity/binding site prediction, molecular docking and virtual screening, visualization and interaction analysis of docked structure, pharmacophore mapping, de novo ligand designing, lead optimization, molecular dynamics simulation, predicting absorption, distribution, metabolism, excretion, and toxicity (ADMET), and building quantitative structure–activity (QSAR) model. The selection of a potential drug target, its structural features, and precise information of the binding site is a very important step to proceed for drug discovery and designing. Target–ligand complex analysis and pharmacophore mapping can guide the process of de novo drug designing. Molecular docking and simulation analysis can be used to evaluate the stability of a ligand in the binding site of the target and also provides information about the residues involved in the interaction. Pharmacokinetics and pharmacodynamics optimization of lead compounds are required to improve the specificity, affinity for binding to the target, and their absorption, distribution to a target site, metabolism of drug, excretion, and toxicity. Prior ADMET prediction using different models can reduce the risk of drug failure in a clinical trial. QSAR model can be developed for the biological activity of known drugs against a target, which can be used for predicting the biological activity of a new molecule. These computer-aided drug designing (CADD) techniques are more efficient, less costly, and less time-consuming as compared to the traditional methods of drug discovery. These CADD approaches/tools have been designed by incorporating the parameter of basics scientific principles related to the problem, and prediction models are derived and validated by vast dataset. Still, there are some limitations related to different CADD tools, which may be overcome by including some other relevant parameters and dataset in a program, and also by increasing the computational capability.