Inspecting the mechanism of fragment hit binding on SARS-CoV-2 Mpro by using supervised molecular dynamics (SuMD) simulations.

Computational approaches supporting the early characterization of fragments molecular recognition mechanism represent a valuable complement to more expansive and low-throughput experimental techniques. In this retrospective study, we have investigated the geometric accuracy with which High-Throughput supervised molecular dynamics simulations (HT-SuMD) can anticipate the experimental bound state for a set of 23 fragments targeting the SARS-CoV-2 main protease. Despite the encouraging results herein reported, in line with those previously described for other MD-based posing approaches, a high number of incorrect binding modes still complicate HT-SuMD routine application. To overcome this limitation, fragment pose stability has been investigated and integrated as part of our in-silico pipeline, allowing us to prioritize only the more reliable predictions.