A geometric XAI approach to protein pocket detection

Protein pocket detection is an essential step in structure-based virtual screening methods for identifying potential drug targets. To facilitate efficient molecular docking, an accurate determination of target binding sites is indispensable. In this study, we present GENEOnet, an innovative machine learning model based on Group Equivariant Non-Expansive Operators (GENEOs) for protein pocket detection. Our proposed method sets itself apart from other artificial intelligence techniques in the domain due to its reduced number of parameters, increased transparency, and integration of prior knowledge. The experimental assessment validates GENEOnet’s efficacy with a limited training dataset, surpassing several established state-of-the-art methods based on multiple critical performance indicators computed using extensive public datasets of ligand-protein complexes. GENEOnet, the result of an ongoing collaborative effort between Italian universities and the pharmaceutical company Dompé Farmaceutici S.p.A., is accessible as a web service at https://geneonet.exscalate.eu to enable the scientific community to evaluate the pre-trained model for pocket detection.