Pills and capsules placed on a target symbolizing the concept of target-based drug discovery

Harnessing Molecular Drivers of Comorbidity for Precision Drug Discovery

Webinar

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A critical factor in the successful development of drugs is identifying suitable targets that drive disease progression and developing therapies that effectively treat the disease while minimizing adverse effects.

In this webinar, Professor Jeffrey Skolnick will introduce POLYPHARM-AI, a suite of AI algorithms designed to identify common driver proteins shared across multiple comorbid diseases. He will also showcase its performance through benchmarking studies, applications in ovarian cancer cell lines and its capability to identify novel pain treatments.

We’re also excited to welcome Kevin Grady from Lonza, who will discuss how primary cells and optimized media systems can build advanced 2D and 3D predictive models, supporting preclinical and clinical workflows in drug discovery.

By attending this webinar, you will learn:

How AI-driven tools can identify drug targets across comorbid diseases
The role of predictive cell models in understanding pharmacokinetics and metabolism
New approaches for tackling complex diseases and advancing drug discovery

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Speakers

Jeffrey Skolnick, PhD

Regents’ Professor and Director of the Center for the Study of Systems Biology

Georgia Institute of Technology

Jeffrey Skolnick is a Regents’ Professor and the Director of the Center for the Study of Systems Biology in the School of Biological Sciences at the Georgia Institute of Technology. He is also the Thrust Lead for Precision Medicine and Drug Discovery un the Institute for Data Engineering and Science (IDEaS). He is the Mary and Maisie Gibson Chair in Computational Systems Biology and a Georgia Research Alliance Eminent Scholar in Computational Systems Biology. He is also the Chief Scientific Officer of the Ovarian Cancer Institute. He attended graduate school in Chemistry at Yale, receiving a Ph.D. in Chemistry. He then held a postdoctoral fellowship at Bell Laboratories. Next, he joined the faculty of the Chemistry Department at Louisiana State University, Baton Rouge. Then, he moved to Washington University. Following his emerging interest in biology, he joined the Department of Molecular Biology of the Scripps Research Institute, where he held the rank of Professor. Among his awards are the Southeastern Universities Research Association (SURA) Distinguished Scientist Award, the Sigma Xi Sustained Research Award, and an Alfred P. Sloan Research Fellowship. He is a Fellow of the AAAS, the Biophysical Society, and the St. Louis Academy of Science. He is the author of over 415 publications, has an h-index of 96 and has served on 21 editorial boards. Dr. Skolnick’s current research interests are in computational biology and bioinformatics. He has developed AI based approaches to predict disease mode of action proteins, drug efficacy and side effects, diagnostic tools to identify early-stage cancers and a non- Mendelian approach to precision medicine. He has applied these tools to aging, cancer, chronic fatigue syndrome and cancer metabolomics. He has also done substantial research on the possible origins of the biochemistry of life.

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Regents’ Professor and Director of the Center for the Study of Systems Biology

Georgia Institute of Technology