Inspecting the Mechanism of Fragment Hits Binding on SARS‐CoV‐2 M(pro) by Using Supervised Molecular Dynamics (SuMD) Simulations

Bissaro, Maicol; Bolcato, Giovanni; Pavan, Matteo; Bassani, Davide; Sturlese, Mattia; Moro, Stefano

Computational approaches supporting the early characterization of fragment 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.