Machine Learning in Drug Development: Part 2

Prof. Katherine Seley-Radtke group’s NIH-funded research employs a chemical biology approach tonucleoside, nucleotide and heterocyclic drug discovery and development with therapeuticemphasis on antiviral, anticancer and antiparasitic targets and overcoming resistance to currentlyused drugs. Current focus is targeting Ebola, Zika, Dengue and MERS viruses. She has served asthe Program Director for UMBC’s Chemistry-Biology Interface graduate training programfunded by NIH since 2007. This program promotes hands on cross disciplinary research foralmost 50 PhD students from four departments at UMBC and UMB. She is currently theImmediate Past President and Secretary-Elect for the International Society of Nucleosides,Nucleotides and Nucleic Acids and a Board member of the International Society for AntiviralResearch. Prof. Seley-Radtke also serves as a standing member for several NIH study sectionsand is an Associate Editor for three scientific journals – Antiviral Chemistry & Chemotherapy,Molecules – Chemical Biology, and Current Protocols in Chemical Biology. Joy is an Associate Professor of Pediatrics at Emory University with a 25-year experience in the pharmaceutical industry. She received her B.S. from Peking University School of Pharmaceutical Sciences, her Ph.D. in Medicinal Chemistry from Dr. Raymond Bergeron’s lab at the University of Florida School of Pharmacy, and postdoctoral training in enzymology in Dr. Karen Anderson’s lab at Yale University School of Medicine. Joy’s research focuses on drug mechanisms of action, drug combinations, drug resistance, drug metabolism, off-target effects, and toxicity. Joy contributed to the approval of three marketed drugs: Emtricitabine (FTC) for HIV, Sofosbuvir for HCV, and is one of the inventors of Remdesivir, the first FDA-approved direct antiviral for treating COVID-19, and Obeldesivir (GS-5245), currently in clinical trials for the treatment of RSV infection.

• 1. Transforming Modern Drug Discovery with Machine Learning – Applications in Ligand-based Drug Design2. Optimizing Multi-Modal Drug Design Through Computational Pocket Mapping and Data Integration3. Harnessing AI for Nucleic Acid Drug Discovery: Small Molecule Targeting DNA and RNA4. AI-aided Drug Development for Protein Degraders: Design, Lead Identification, and Optimization5. Artificial Intelligence in the Development of Antiviral Drugs – Progress and Applications6. Artificial Intelligence for Drug Target Identification7. Machine Learning in Proteomic Biomarker Discovery