![]() One such computational method, large-scale molecular modeling, is critical in the preclinical hit and lead identification process. ![]() 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. The success rates for new drug approvals in the United States are < 15%, and investment costs often cannot be recouped. The process can take 10–15 years and command vast research and development resources costing over $1 billion. 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. Drug discovery is a complex process with many potential pitfalls.
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