Unlocking the Power of Deep Learning in Drug Discovery
Drug discovery is a challenging endeavor. The first step – finding compounds with the desired medicinal effect on the target pathogens – has traditionally involved the automated high-throughput screening of large compound libraries to identify “hits” with biological activity. Once identified, promising compounds are put through a process called lead generation to evaluate criteria such as their dose-response curve, cellular efficacy, affinity towards the target, reactivity with other compounds, cytotoxicity, etc.
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