Jeffrey Skolnick
Regents’ Professor 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 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 Thrust Lead for Precision Medicine and Drug Discovery in the Institute for Data Engineering and Science (IDEaS). As well as 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 97 and has served on over 20 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.
Seminars
- Transforming molecular glue discovery with structure-guided design, novel platforms and emerging tools
- Employing AI for degrader design to enable modelling and informed development
- Strategizing the future of induced proximity through rational, predictive design
- GlueFinder enables rational molecular glue discovery: GlueFinder systematically mines protein structures to identify ligandable pockets near protein– protein interfaces that can stabilize ternary complexes between therapeutic targets and E3 ubiquitin ligases
- Broad applicability to major oncogenic targets: Applied to EGFR, HER2, and KRAS, GlueFinder predicted candidate molecular glues capable of recruiting 24, 111, and 148 distinct E3 ligases, respectively, substantially expanding the range of potential degradation strategies
- Transforms targeted protein degradation design: By decoupling glue discovery from traditional degrader scaffolds and dependence on specific ligases such as VHL and Cereblon, GlueFinder provides a general computational framework for expanding the druggable proteome
