A 3D printed model of a SARS-CoV-2 viral particle (blue) dotted with the spike protein (red) that enables the virus to infect human cells. The spike protein is the target of research efforts to prevent and treat COVID-19.
Team efforts at the School and beyond produce two potential treatments for COVID-19.
Beth Apsel Winger, MD, PhD (left), and Matthew Jacobson, PhD (right) explore a cancer-causing mutation in a protein in virtual reality, using UCSF ChimeraX.
ChimeraX, virtual reality software developed at the UCSF Resource for Biocomputing, Visualization and Informatics, is now the tool of choice for computational structural biologists in the School of Pharmacy. The Jacobson Lab recently used ChimeraX to find a promising new cure for a drug-resistant...
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.
