Laurianne David, Anissa Mdahoma, Natesh Singh, Sébastien Buchoux, Emilie Pihan, Constantino Diaz, Obdulia Rabal
Bioinformatics Advances, Volume 2, Issue 1, 2022, vbac090,
https://doi.org/10.1093/bioadv/vbac090
Current covalent docking tools have limitations that make them difficult to use for performing large-scale structure-based covalent virtual screening (VS). They require time-consuming tasks for the preparation of proteins and compounds (standardization, filtering according to the type of warheads), as well as for setting up covalent reactions. We have developed a toolkit to help accelerate drug discovery projects in the phases of hit identification by VS of ultra-large covalent libraries and hit expansion by exploration of the binding of known covalent compounds. With this application note, we offer the community a toolkit for performing automated covalent docking in a fast and efficient way.
The toolkit comprises a KNIME workflow for ligand preparation and a Python program to perform the covalent docking of ligands with the GOLD docking engine running in a parallelized fashion.
