Experience and Outcomes of a Pharmaceutical Care Leadership Residency Program

The University of Minnesota College of Pharmacy’s Ambulatory Care Residency Program has graduated 22 residents from its Leadership Emphasis program from 1999 to 2014. The Leadership Emphasis program is unique in its design, providing a set of experiences over two years focused on developing leadership skills in practice development, establishing personal influence, advocacy in the profession, and teaching. The program’s design has focused on bringing value to three distinct audiences: pharmacists enrolled in the program, the local pharmacy practice community, and the College of Pharmacy. This paper explores the program’s contributions in each of these areas. Program graduates from 1999-2009 were interviewed and cited the independent, yet mentored, activities of the program as instrumental to their professional and personal development. The program has provided significant value to the College of Pharmacy, primarily in the form of instructional support, service to faculty practice sites and development of new practice sites for APPEs. Teaching and precepting hours offset the salary of the residents, resulting in financial benefits for the College. In the second year of the program, residents pursue development of new practice sites, 15 of which have been sustained to provide at least a half-time pharmacist position, having a direct impact on pharmacy practice development in the region. The program provides a win-win-win situation for all the stakeholders involved. Schools and colleges of pharmacy are encouraged to consider whether a similar program may assist in achieving its own goals in practitioner development, teaching and learning, and community engagement

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery