New publication on "Pharmacophore-Based Machine Learning Model To Predict Ligand Selectivity for E3 Ligase Binders" from our screening partner site Fraunhofer ITMP

E3 ligases are enzymes that play a critical role in ubiquitin-mediated protein degradation and are involved in various cellular processes. Pharmacophore analysis is a useful approach for predicting E3 ligase binding selectivity, which involves identifying key chemical features necessary for a ligand to interact with a specific protein target cavity.

 While pharmacophore analysis is not always sufficient to accurately predict ligand binding affinity, it can be a valuable tool for filtering and/or designing focused libraries for screening campaigns. In this study, our screening partner site ITMP Fraunhofer presents a fast and an inexpensive approach using a pharmacophore fingerprinting scheme known as ErG, which is used in a multi-class machine learning classification model. The full paper can be downloaded here: