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Characterizing the aggregation of the peptide amyloid β is essential to better understand Alzheimer’s disease and to find potential targets for drug development. Deep neural networks make it possible to describe the kinetics of this peptide, opening the way for achieving this goal.
Computational approaches for drug repurposing can accelerate the identification of treatments during a pandemic. In this Review, the authors discuss this topic in the context of COVID-19 and propose a strategy to make computational drug repurposing more effective in future pandemics.
While estimating causality from observational data is challenging, quasi-experiments provide causal inference methods with plausible assumptions that can be practical to a range of real-world problems.
