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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.

A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the Free-Solv database, and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration, and requires a fraction of the training set size to outperform standalone FEP calculations. The ML-derived correction terms are further shown to be transferable to a range of related FEP simulation protocols. The approach may be used to inexpensively improve the accuracy of FEP calculations, and to flag molecules which will benefit the most from bespoke forcefield parameterization efforts.

DLM: Contributed to the outline, drafted some of the sections, gave ideas on figures, and helped edit the paper. LNN: Helped write the simulation length, stopping conditions, and information saving section. Edited and reviewed alchemical path section. SP Wrote Sec. 10. AR: Created figure 12, contributed to sections 3 and 7, and helped edit the paper. JS: Created Figs. 1,2,3,4,6,14, and an initial draft of 5. Wrote Sec. 8.7, the checklist Sec. 12, and contributed to general formatting discussions and editing. MRS: Helped create figure 7, wrote Sec. 7.2.3 describing choices for alchemical pathways and parts of 8 on the analysis for free energy calculations. Reviewed and edited text throughout. GT: Contributed to Sec. 1 and 5, and helped edit the paper. HX: Contributed Sec. 4.4, to Sec. 7.1.1, and to Sec. 8.5. For a more detailed description of author contributions, see the GitHub issue tracking and changelog at https://github.com/alchemistry/alchemical-best-practices. Other Contributions Julia E. Rice participated in the original discussion of the document at the Best Practices in Molecular Simulation Workshop Hosted by at NIST, Gaithersburg, MD, August 24th-25th, 2017. Marieke Schor proofread the manuscript. For a more detailed description of contributions from the community and others, see the GitHub issue tracking and changelog at https://github.com/alchemistry/alchemical-best-practices. This LiveCoMS document is maintained online on GitHub at https://github.com/alchemistry/alchemical-best-practices; to provide feedback, suggestions, or help improve it, please visit the GitHub repository and participate via the issue tracker.Potentially Conflicting Interests JM is a current member of the Scientific Advisory Board of Cresset. MK is employed by Cresset who commercially distribute a software for performing alchemical free energy calculations. MRS is a Open Science Fellow and consultant for Silicon Therapeutics. JDC is a current member of the Scientific Advisory Board of OpenEye Scientific Software and a consultant to Foresite Laboratories.

Atomic partial charges are crucial parameters in molecular dynamics (MD) simulation, dictating the electrostatic contributions to intermolecular energies, and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of ab initio semiempirical quantum chemical methods such as AM1-BCC, and is expensive for large systems or large numbers of molecules. We propose a hybrid physical / graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserves total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling, for the first time, the use of fully consistent charge models for small molecules and biopolymers for the construction of nextgeneration self-consistent biomolecular force fields. Implemented in the free and open source package espaloma_charge, this approach provides drop-in replacements for both AmberTools antechamber and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at https://github.com/choderalab/espaloma_charge. Molecular mechanics (MM) force fields abstract atoms as point charge-carrying particles, with their electrostatic energy ( ) calculated by some Coulomb's law [10](or some modified form), where is Coulomb constant (energy * distance 2 / charge 2 ) and the interatomic distance. In fixed-charge molecular mechanics force fields, the partial charges are treated as constant, static parameters, agnostic of instantaneous geometry. As such, partial charge assignment-the manner in which partial charges are assigned to each atom in a given system based on their chemical environmentsplays a crucial role in molecular dynamics (MD) simulation, determining the electrostatic energy ( ) at every step and shaping the energy landscape.