Data Analysis and Informatics

Preparing Early Drug Discovery for the Machine Learning Revolution

Tuesday, February 6
10:30 AM - 11:00 AM
Location: 8

The mass-collection of data and its mining with machine learning algorithms is driving a revolution within the industrialized world, enabling data-forward organizations to improve operational efficiency and facilitate decision-making. Operationalizing data science and machine learning pipelines requires well-behaved, curated datasets on which to train models, and ideally, a streamlined workflow that handles data collection, standardization, model building and publication. In early drug discovery, the process of iterative drug design known as the “design, make, test” cycle, whereby chemists design and evaluate drug candidates, synthesize selected designs, and test them in biological assays is becoming increasingly complex as the technologies employed in each step continue to advance. Successful management of these complex workflows is often difficult to accomplish with available commercial software packages, which often necessitates the construction of novel applications that are capable of integrating with legacy systems. While building highly-integrated software tools is a distinct challenge, the reward is an opportunity to gather information which could be used to further improve the efficiency of drug discovery. This talk will highlight Merck’s approach to building tools that enable collaborative drug design and high-throughput chemical synthesis while gathering information that we anticipate will transform the way scientists discover new medicines.

Scott T. Harrison

Principal Scientist
Merck

I grew up in Michigan and attended the University of Michigan for my undergraduate training where I received a B.S. in Chemistry and Biochemistry. I then moved on to obtain a PhD from the Scripps research institute with an emphasis on natural product total synthesis. I joined Merck in 2006, where I applied parallel chemistry technologies to several neuroscience programs including COMT and our beta-amyloid PET tracer effort which culminated in the discovery of MK-3328. Since 2011, I've been supporting early discovery from our Cheminformatics group where I promote the development of scientist-facing self-service tools that facilitate drug design, chemical synthesis, and biological testing.

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