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

Multiple action options in the context of time: When exams approach, students study more and experience fewer motivational conflicts

Capelle JD, Grunschel C, Bachmann O, Knappe M, Fries S. Multiple action options in the context of time: When exams approach, students study more and experience fewer motivational conflicts. Motivation and Emotion. 2021.University students' study motivation in a particular moment is shaped by contextual factors such as upcoming exams and conflicts between different action tendencies. We investigated how these two contextual factors are related. Based on the theoretical assumption that students' in-the-moment study motivation increases relative to their motivation for other activities as exams approach, we investigated how students' study activities and their experience of motivational action conflicts develop when exams come closer in time. Using the experience sampling method, we tracked the in-situ activities and conflict experience of 134 first-semester university students over one week and a total of 4995 measurement points just before exams. Multilevel logistic regression revealed that the probability to study increased by 13.9% and the probability to experience a motivational conflict decreased by 17.5% each day the exam came closer in time. Multilevel regression showed that motivational conflicts were more intense the closer the exam was in time. Students were generally less likely to experience conflicts while studying and experienced more intense conflicts when the conflicting activity was study related. We discuss that both multiple goals and the temporal distance of relevant events should be considered as relevant contexts shaping the situated motivation of university students