Herein we provide a living summary of the data generated during the COVID Moonshot project focused on the development of SARS-CoV-2 main protease (Mpro) inhibitors. Our approach uniquely combines crowdsourced medicinal chemistry insights with high throughput crystallography, exascale computational chemistry infrastructure for simulations, and machine learning in triaging designs and predicting synthetic routes. This manuscript describes our methodologies leading to both covalent and non-covalent inhibitors displaying protease IC50 values under 150 nM and viral inhibition under 5 uM in multiple different viral replication assays. Furthermore, we provide over 200 crystal structures of fragment-like and lead-like molecules in complex with the main protease. Over 1000 synthesized and ordered compounds are also reported with the corresponding activity in Mpro enzymatic assays using two different experimental setups. The data referenced in this document will be continually updated to reflect the current experimental progress of the COVID Moonshot project, and serves as a citable reference for ensuing publications. All of the generated data is open to other researchers who may find it of use.
We perform molecular dynamics simulations of poly-(benzodithiophene-thienopyrrolodione) (BDT-TPD) oligomers in order to evaluate the accuracy with which unoptimized molecular models can predict experimentally characterized morphologies. The predicted morphologies are characterized using simulated grazing-incidence X-ray scattering (GIXS) and compared to the experimental scattering patterns. We find that approximating the aromatic rings in BDT-TPD with rigid bodies, rather than combinations of bond, angle, and dihedral constraints, results in 14% lower computational cost and provides nearly equivalent structural predictions compared to the flexible model case. The predicted glass transition temperature of BDT-TPD (410 ± 32 K) is found to be in agreement with experiments. Predicted morphologies demonstrate short-range structural order due to stacking of the chain backbones (π−π stacking around 3.9 Å), and long-range spatial correlations due to the self-organization of backbone stacks into "ribbons" (lamellar ordering around 20.9 Å), representing the best-to-date computational predictions of structure of complex conjugated oligomers. We find that expensive simulated annealing schedules are not needed to predict experimental structures here, with instantaneous quenches providing nearly equivalent predictions at a fraction of the computational cost of annealing. We therefore suggest utilizing rigid bodies and fast cooling schedules for high-throughput screening studies of semiflexible polymers and oligomers to utilize their significant computational benefits where appropriate.
Mesoscale simulation techniques have helped to bridge the length scales and time scales needed to predict the microstructures of cured epoxies, but gaps in computational cost and experimental relevance have limited their impact. In this work, we develop an open-source plugin epoxpy for HOOMD-Blue that enables epoxy crosslinking simulations of millions of particles to be routinely performed on a single modern graphics card. We demonstrate the first implementation of custom temperature-time curing profiles with dissipative particle dynamics and show that reaction kinetics depend sensitively on the stochastic bonding rates. We provide guidelines for modeling first-order reaction dynamics in a classic epoxy/hardener/toughener system and show structural sensitivity to the temperature-time profile during cure. We conclude with a discussion of how these efficient large-scale simulations can be used to evaluate ensembles of epoxy processing protocols to quantify the sensitivity of microstructure on processing.
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