Targeting HsDHODH: Shape and machine-learning guided discovery and structural validation of SARS-CoV-2 antivirals

values ≥ 100 μM). These findings highlight the value of integrated computational-experimental workflows in the discovery of HsDHODH inhibitors, establishing this scaffold as a viable starting point for further development with potential applicability against SARS-CoV-2. Furthermore, by using accessible, shape-based and low-code ML-based modeling platforms, this workflow provides a reproducible and practical template that can be readily adopted by medicinal chemists to accelerate lead identification.