Social work education: Reflections during Covid-19 lockdown

Teaching social work students in Aotearoa New Zealand during the Covid-19 crisis produced an acute awareness of the impact of lockdown levels 3 and 4 on student wellbeing. Students were required to rapidly adapt to study in a fully online environment without the face-to-face support of university campus life. Normal social and academic pressures were immediately intensified, with no immediate relief in sight. Student resilience was tested further due to multiple factors such as: suddenly reduced incomes, parenting during lockdown, caring for whānau both within and external to their “bubble”, and being unable to come together with loved ones to celebrate life events or mourn those who had passed

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