Machine learning algorithms can be trained to distinguish wolves from dogs in photographs, but sometimes these algorithms learn incorrectly by assuming that any animal pictured with snow in the background is a wolf (bottom right). Two School scientists argue that scientific applications of machine learning also need to be monitored to ensure they are working properly.
Michael Keiser, PhD, and Kangway Chuang, PhD, want to use machine learning to speed the pace of drug discovery. By digging into the work of another lab, the pair realized how machine learning could lead scientists astray—and came up with methods to avoid its worst pitfalls.
A team led by Pamela England, PhD, identified a new target (yellow) for future Parkinson’s disease therapies within the Nurr1 protein (blue).
Scientists in the UCSF School of Pharmacy recently identified the first drug-binding target site on a molecule known to play a role in Parkinson's disease, opening the door to a new generation of therapies for the condition.
Drug discovery today begins with computation rather than test tube experimentation. Three School of Pharmacy faculty emeriti, Robert Langridge, Irwin “Tack” Kuntz, and the late Peter Kollman, were awarded the UCSF Medal for creating computational tools for drug discovery that are now used worldwide.
B. Joseph Guglielmo, PharmD, addressed Half Century Club Luncheon attendees.
For UCSF School of Pharmacy alumni who attended the event, Alumni Weekend 2018 offered a chance to explore how science connects the School’s research, education, and patient care agendas; learn about the lives and professional accomplishments of pharmacy school graduates; and get a glimpse of what’...