Discovery of Covalent and Non-covalent Small Molecule Ligands for WDR91 by Applying Machine Learning and Computational Chemistry to DNA-Encoded Library Screening Data
X-Chem’s DNA-encoded libraries (DELs) contain hundreds of billions of molecules spanning diverse chemical space. When screened against biological targets, positive and negative structure-activity relationship (SAR) trends within this chemical space emerge, yielding rich datasets on which machine learning thrives. Here, we report the discovery of first-in-class ligands for WD40 repeat-containing protein 91 (WDR91) by applying machine learning models trained on X-Chem’s DEL screening data to synthetically accessible commercial compound catalogues. Initial predictions resulted in the discovery of a WDR91-selective compound with a KD of 6±2 μM, machine learning-driven SAR expansion of the primary hits identified an additional 11 compounds with KD ≤ 25 μM and %Rmax > 60%, and structure-based drug design (SBDD) enabled the discovery of 2 covalent analogues with KD ≤ 45 μM and %Rmax > 50%, for which covalent adduct formation was confirmed by intact mass liquid chromatography−mass spectrometry.
This poster was presented at the 12th International Symposium on DNA-encoded chemical libraries
