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 10-15 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.  Read more . . .  https://www.sciencedirect.com/science/article/pii/S0022202X19317968?via%3Dihub

Issa NT, Badiavas EV, Schürer S. Research Techniques Made Simple: Molecular Docking in Dermatology – A Foray into In Silico Drug Discovery. J Invest Dermatol. 2019 Dec;139(12):2400-2408.e1. doi: 10.1016/j.jid.2019.06.129. PMID: 31753122.  https://www.sciencedirect.com/science/article/pii/S0022202X19317968?via%3Dihub