DEL2PH4: Predictive 3D Pharmacophores from DNA-Encoded Library Screening Data

DNA-encoded library (DEL) screening enables identification of small-molecule binders from libraries containing billions of compounds, yet much of the resulting structure-activity relationship (SAR) information remains underutilized. Here, we describe DEL2PH4, an automated ligand-based workflow that converts DEL screening data into three-dimensional pharmacophore models by integrating statistically enriched compounds with structurally related unenriched analogs, which serve as negative examples during model construction. The resulting pharmacophores capture consensus interaction features across DEL families and enable the extraction of actionable 3D SAR information from primary DEL screening data, independent of resynthesis or activity measurements. Application to a MerTK kinase DEL screen demonstrates strong enrichment of positives over decoy molecules in retrospective benchmarking, recovery of known experimentally validated actives from external data sets, and consistency with experimentally determined X-ray binding modes. DEL2PH4 provides a general strategy for translating DEL screening outputs into interpretable 3D models that support virtual screening, scaffold hopping, and medicinal chemistry optimization.