A deep learning approach for the discovery of tumor-targeting small organic ligands from DNA-Encoded Chemical Libraries

Abstract

DNA-Encoded Chemical Libraries (DELs) emerged as efficient and cost-effective ligand discovery tools, which enable the generation of protein-ligand interaction data of unprecedented size. In this article, we present an approach that combines DEL screening and instance-level deep learning modeling to identify tumor-targeting ligands against Carbonic Anhydrase IX (CAIX), a clinically validated marker of hypoxia and clear cell Renal Cell Carcinoma. We present a new ligand identification and HIT-to-LEAD strategy driven by Machine Learning (ML) models trained on DELs, which expand the scope of DEL-derived chemical motifs. CAIX screening datasets obtained from three different DELs were used to train ML models for generating novel HITs, dissimilar to elements present in the original DELs. Out of the 152 novel potential HITs that were identified with our approach and screened in an in vitro enzymatic inhibition assay, 70% displayed submicromolar activities (IC50 View larger version (35K): org.highwire.dtl.DTLVardef@105913corg.highwire.dtl.DTLVardef@823522org.highwire.dtl.DTLVardef@6e85e9org.highwire.dtl.DTLVardef@19a611_HPS_FORMAT_FIGEXP M_FIG C_FIG