Pharmacophore-based ML model to filter candidate E3 ligands and predict E3 Ligase binding probabilities

Abstract

Among the plethora of E3 Ligases, only a few have been utilized for the novel PROTAC technology. However, extensive knowledge of the preparation of E3 ligands and their utilization for PROTACs is already present in several databases. Here we provide, together with an analysis of functionalized E3 ligands, a comprehensive list of trained ML models to predict the probability to be an E3 ligase binder. We compared the different algorithms based on the different description schemes used and identified that the pharmacophore-based ML approach was the best. Due to the peculiar pharmacophores present in E3 ligase binders and the presence of an explainable model, we were able to show the capability of our ErG model to filter compound libraries for fast virtual screening or focused library design. A particular focus was also given to target E3 ligase prediction and to find a subset of candidate E3 ligase binders within known public and commercial compound collections.

Keywords

• E3 Ligase ligands
• Machine learning
• Virtual screening
• Protein degradation
• ErG fingerprint
• PROTACS