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Depending on who you ask, drug development costs can range from 1.6 billion to 5 billion dollars by the time the drug hits the market.

Anything that can be done to shorten the timetable — and produce high quality promising candidates — is key. This is particularly true during the drug discovery phase, when the hunt for molecules is on.

What can medicinal chemistry teams do to accelerate this process?

Some think a hypothesis-driven approach can make a significant difference, but using state of the art tools we can take that one step further. With this approach, teams of synthetic, medicinal, and computational chemists work in tandem to design new compounds with appropriate calculated properties, based on emerging SAR and associated structural biology and/or computational modelling data.

These priority compounds are synthesized and purified for in vitro and in vivo evaluation. The data generated are captured and analyzed using platforms such as Dotmatics data repository, QSAR, Vortex visualization, predictive models using artificial intelligence (AI) methods, and computer aided drug design (CADD) are developed to further enhance the design process. By harnessing this combination of scientific expertise, computational models, and emerging AI technologies, chemists can rapidly provide enhanced compounds and ultimately quality development candidates in a more cost and time efficient way.

Learn more about medicinal chemistry in drug discovery here.

Reprinted with edits and permission by Montana, J. Keeping The Pipeline Moving. Eureka blog. Jun 30, 2015. Available: