A few techniques, such as global mass spectrometry and CRISPR-Cas9 endogenous tagging, have recently been developed. However, no endorsed method for PROTAC degradation assessment is present.

Finally, we implemented machine learning classification models and showed that AR degradation for VHL-based but not CRBN-based PROTACs can be predicted from simple permeability-related 2D molecular descriptors.Ĭollections of industrial data are being produced, and some of them have been published. Then, we built and characterized ternary complexes to validate previous results. Linker structure degradation relationships were also investigated. Subsequently, we performed Degradation Cliff analysis and identified some key aspects conferring a positive contribution to activity, as well as some methodological limits when applying this approach to PROTACs. The resulting clusters were not informative in terms of structure–degradation relationship. After setting up a dataset of 92 PROTACs with consistent AR degradation values, we employed the Bemis–Murcko method for their classification. This study explores the potential of a few in silico tools to extract drug design information from AR degradation data in the format often reported in the literature.

Large interest in the degradation of the androgen receptor (AR) as a new anti-prostatic cancer strategy has resulted in several papers focusing on PROTACs against AR. Proteolysis-Targeting Chimeras (PROTACs) have recently emerged as a promising technology in the drug discovery landscape.

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