Research Techniques Made Simple: Molecular Docking in Dermatology - A Foray into In Silico Drug Discovery

Abstract

Drug discovery is a complex process with many potential pitfalls. To go to market, a drug must undergo extensive preclinical optimization followed by clinical trials to establish its efficacy and minimize toxicity and adverse events. The process can take 10e15 years and command vast research and development resources costing over $1 billion. The success rates for new drug approvals in the United States are < 15%, and investment costs often cannot be recouped. With the increasing availability of large public datasets (big data) and computational capabilities, data science is quickly becoming a key component of the drug discovery pipeline. One such computational method, large-scale molecular modeling, is critical in the preclinical hit and lead identification process. Molecular modeling involves the study of the chemical structure of a drug and how it interacts with a potential disease-relevant target, as well as predicting its ADMET properties. The scope of molecular modeling is wide and complex. Here we specifically discuss docking, a tool commonly employed for studying drug-target interactions. Docking allows for the systematic exploration of how a drug interacts at a protein binding site and allows for the rank-ordering of drug libraries for prioritization in subsequent studies. This process can be efficiently used to virtually screen libraries containing over millions of compounds.

Natural Phytocompounds from Common Indian Spices for Identification of Three Potential Inhibitors of Breast Cancer: A Molecular Modelling Approach

“…From hit identification through lead optimization and beyond, molecular modelling has been an essential part of many drug-development programs [ 14 ]. The study of the chemical make-up of a drug, its interactions with putative disease-relevant targets, and projections of its ADMET characteristics are all included in molecular modelling [ 15 ]. The application of molecular modelling is very broad and multifaceted [ 15 ].…”

“…The study of the chemical make-up of a drug, its interactions with putative disease-relevant targets, and projections of its ADMET characteristics are all included in molecular modelling [ 15 ]. The application of molecular modelling is very broad and multifaceted [ 15 ]. One crucial technique of molecular modelling is the molecular docking study which involves docking of small molecules to protein binding sites [ 16 ].…”

Identification of Interleukin-1-Beta Inhibitors in Gouty Arthritis Using an Integrated Approach Based on Network Pharmacology, Molecular Docking, and Cell Experiments

“…Network pharmacology, originally proposed by Andrew L. Hopkins in 2007, mainly studies the complex and diverse relationships among drugs, targets, diseases, and pathways, providing new methods for the development of new drugs and new ideas for the treatment of some diseases [21][22][23]. Molecular docking, developed with the continuous update of computer technology and the rapid increase of structural, chemical, and biological data related to available therapeutic targets, can be used to identify new compounds with therapeutic significance and contribute to drug discovery and development [24,25]. is study firstly screened the active compounds of TFDC and then analyzed and summarized the possible targets of active compounds in treating GA. Next, the targets of active compounds and the pathways of targets that might play a therapeutic role were investigated by using network pharmacology, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.…”

In silico Analysis of the Polyphenolic Metabolites of Zea mays L. "Purple Corn" on HMG-CoA Reductase

“…33,35 Molecular docking is a bioinformatic method that facilitates predicting the binding of drug-like molecules (ligands) to an active site of target proteins (target); the more stable, specific and favorable the ligand-target binding, the greater biological activity. 35,36 In Cheminformatics, there are a variety of in silico methods that predict the pharmacokinetic and physicochemical properties of molecules based on the chemical structure. 37,38 Therefore, the in silico analysis of the polyphenols of Zea mays L. "purple corn", on the HMG-CoA reductase enzyme, will allow knowing the molecules with the best interaction prognosis, better pharmacokinetic and physicochemical properties, proposing new structures as potential alternatives for the treatment of hypercholesterolemia.…”