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Early-stage drug discovery has been limited by initial hit identification and lead optimization and their associated costs (1). Ultra-large virtual screens (ULVSs), which involve the virtual evaluation of massive numbers of molecules to engage a macromolec-ular target, have the ability to significantly alleviate these problems, as was recently demonstrated in multiple studies (2–7). Despite their potential, ULVSs have so far only explored a tiny fraction of the chemical space and of available docking programs. Here, we present VirtualFlow 2.0, the next generation of the first open-source drug discovery platform dedicated to ultra-large virtual screen ings. VirtualFlow 2.0 provides the REAL Space from Enamine containing 69 billion drug-like molecules in a "ready-to-dock" format, the largest library of its kind available to date. We provide an 18-dimensional matrix for intuitive exploration of the library through a web interface, where each dimension corresponds to a molecular property of the ligands. Additionally, VirtualFlow 2.0 supports multiple techniques that dramatically reduce computational costs, including a new method called Adaptive Target-Guided Virtual Screening (ATG-VS). By sampling a representative sparse version of the library, ATG-VS identifies the sections of the ultra-large chemical space that harbors the highest potential to engage the target site, leading to substantially reduced computational costs by up to a factor of 1000. In addition, VirtualFlow 2.0 supports the latest deep learning and GPU-based docking methods, allowing further speed-ups by up to two orders of magnitude. VirtualFlow 2.0 supports 1500 unique docking methods providing target-specific and consensus docking options to increase accuracy and has the ability to screen new types of ligands (such as peptides) and target receptors (including RNA and DNA). Moreover, VirtualFlow 2.0 has many advanced new features, such as enhanced AI and cloud support. We demonstrate a perfectly linear scaling behavior up to 5.6 million CPUs in the AWS Cloud, a new global record for parallel cloud computing. Due to its open-source nature and versatility, we expect that VirtualFlow 2.0 will play a key role in the future of early-stage drug discovery.

Studying the ICNARC Case Mix Programme Database has yielded results showing intensive care admissions by those infected with COVID-19 have twice the corrected mortality of patients presenting with non-COVID-19 viral pneumonia. A basis of an APACHE-II-like score denoted as “BASCA” is also outlined in this study.

The COVID-19 pandemic is placing unprecedented demands on healthcare systems worldwide and exacting a massive humanitarian toll. This makes the development of accurate predictive models imperative, not just for understanding the course of the pandemic but more importantly for gaining insight into the efficacy of public health measures and planning accordingly. Epidemiological models are forced to make assumptions about many unknowns and therefore can be unreliable. Here, taking an empirical approach, we report a 20-30 day lag between the peak of confirmed to recovered cases and the peak of daily deaths in each country, independent of the epoch of that country in its pandemic cycle. This analysis is expected to be largely independent of the proportion of the population being tested and therefore should aid in planning around the timing and easing of public health measures. Our data also suggests broad predictions for the number of fatalities, generally somewhat lower than most other models. Finally, our model suggests that the world as a whole is shortly to enter a recovery phase, at least as far as the first pandemic wave is concerned.