Cov_FB3D: A de novo covalent drug design protocol integrating the BA-SAMP strategy and machine-learning-based synthetic tractability evaluation.

De novo drug design actively seeks to use sets of chemical rules for the fast and efficient identification of structurally new chemotypes with the desired set of biological properties. Fragment-based de novo design tools have been successfully applied in the discovery of noncovalent inhibitors. Nevertheless, these tools are rarely applied in the field of covalent inhibitor de-sign. Herein, we present a new protocol, called Cov_FB3D, which aims to the 'in silico' assembly of potential novel covalent inhibitors by identifying the active fragments in the covalently binding site of the target protein. In this protocol, we propose a BA-SAMP strategy, which combines the noncovalent moiety score with the X-score as MM level, and the covalent can-didate score with the PM7 as QM level. The synthetic accessibility of each suggested compound could be further evaluated with machine-learning-based synthetic complexity evaluation (SCScore). An in-depth test of this protocol against the crystal structures of 15 covalent complexes consisting of BTK-inhibitors, KRAS-inhibitors, EGFR-inhibitors, EphB1-inhibitors, MAGL-inhibitors and MAPK-inhibitors revealed that most of these inhibitors could be de novo repro-duced from the fragments by Cov_FB3D. The binding modes of most generated reference poses could accurately reproduce the known binding mode of most of the reference covalent adduct in the binding site (RMSD