Webinar: Accelerating drug discovery by building and turning high-quality data into actionable insights

Webinar: Accelerating drug discovery by building and turning high-quality data into actionable insights

Drug discovery is a time-consuming and
costly process (and all the more costly because it’s time-consuming).
One of the hopes of the era of Big Data has been that having access to a wealth
of scientific information could help speed the discovery process. However, the
data itself has proven to come with many challenges. Medicinal chemists like
myself are often frustrated in our attempts to integrate and work with
disparate and unstructured sets of data.

I will join Min Lu, Ph.D. from Merck & Co. in presenting a webinar on April 28 covering our recent research in relation to accelerating drug discovery with high-quality data – and we think our findings will be of interest to many chemists.

More about our topic: Structuring data
in discovery chemistry

The prioritization of hits from large
compound lists for further follow-up is a challenging task for medicinal
chemists. During this step of drug discovery, multiple parameters such as
synthetic accessibility, target specificity, physicochemical properties and potential
toxicities, in addition to desired biological activity, must be considered
simultaneously. Increasing amounts of biological data are accumulating in the
pharmaceutical industry and published literature (e.g. journals and patents).

However, data does not equal actionable
information, and guidelines for appropriate data capture, harmonization,
integration, mining and visualization need to be established to fully harness
its potential. In our work, we describe ongoing efforts to structure data in
the area of discovery chemistry. We are integrating complementary data from
both internal and external data sources (Reaxys) into one, and will demonstrate
how this well-curated database facilitates compound set design, tool compound
selection, target deconvolution in phenotypic screening, and predictive model
building (e.g. target prediction).

Early in the discovery process, chemists
select a subset of compounds for further research, often from many viable
candidates. These decisions determine the success of a discovery campaign, and
ultimately what kind of drugs are developed and marketed to the public.

Join us for the webinar to learn more

In this webinar coming up on April 28 at 10am EST, we will present our findings in the context of complex problem solving and decision theory, and discuss the implications on drug discovery. To join us, please register here.

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