Submission Details

Design Rationale:

These structures were generated automatically using a Graph-Based Genetic-Algorithm (GA), which attempted to build mimic molecules of a reference structure. The reference structure was compound 35 from 2011Akaji, which is a tetrapeptide transition state mimic for SARS-CoV(-1), with an IC50 of 98 nM [1]. The intent of this work-package was to try and find some non-peptide mimics. The generative part of the method is based on Jan Jensen's python GB-GA (https://github.com/jensengroup/GB-GA), but with a custom similarly metric that provides a smooth continuous scoring including chemical specificity between 3D structures. Each step of the GA evaluated 42 conformers (generated with open-babel, scored with RMSD) of the trial compound, then maximised the 3D chemical overlap of these conformers against the reference molecule in a single 3D pose. The highest conformer score was used in the GA. The algorithm takes approximately 5 cpu seconds per molecule, which is mostly linear in the number of conformers that are checked. 18145 separate GA streams with a population size of 50 and 100 generations were seeded with the MPro-XChem hits (i.e. all fragments) and the first 1000 molecules of ZINC, and scored with a purely electrostatic chemical similarity. The elite (highest scoring) structures from these runs were then used to seed a second round of 9525 GA streams, where the metric additionally included a score of vdW dispersion chemical specificity (scored at the best electrostatic match), where these multiple objectives were scalarised by taking a weighted geometric mean (electrostatic^0.8 * dispersion^0.2). So the molecules here are the best of ~138 million evaluated molecules, after 200 generations of evolution. The final outputs were simply ranked by the scoring function, and approx. 30% discarded by a simple RDKIT 'problematic group' filter. Clearly the algorithm has been rather keen to put lots of heteroatoms (O,N,S) in place, in order to reproduce a chemical similarity to the peptide backbone. No analysis of stability was made. No chemist has adjusted or post-processed these structures. Clearly much is missing from the metric - and any suggestions on how to 'fix' a structure with some obviously problematic hetero atoms / groups would be greatly appreciated! These could then be easily re-scored, to see whether the metric still thought they were similar. This work was done in collaboration with Kuano Ltd, and used computer time on the Imperial College Research Computing Service,  DOI: [10.14469/hpc/2232](http://doi.org/10.14469/hpc/2232). The overall aim of this work is to characterise the transition states of the actual substrates of 3CL-pro, and then use this GB-GA generative method, and a suitably developed metric to direct suggest transition state analogues. Future work will be to develop the metrics to make closer analogues, and with Kuano to build a robust platform with more sophisticated synthesis likelihood & stability filters. [1] Akaji, K., Konno, H., Mitsui, H., Teruya, K., Shimamoto, Y., Hattori, Y., … Sanjoh, A. (2011). Structure-Based Design, Synthesis, and Evaluation of Peptide-Mimetic SARS 3CL Protease Inhibitors. __Journal of Medicinal Chemistry__, __54__(23), 7962–7973. https://doi.org/10.1021/jm200870n

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AAR-POS-d2a4d1df-1