Permutational Encoding Strategy Accelerates HIT Validation from Single-Stranded DNA-Encoded Libraries.

DNA-Encoded Libraries (DELs) allow the parallel screening of millions of compounds for various applications, including discovery or affinity maturation campaigns. However, library construction and HIT resynthesis can be cumbersome, especially when library members present an unknown stereochemistry. We introduce a permutational encoding strategy suitable for the construction of highly pure single-stranded single-pharmacophore DELs, designed to distinguish isomers at the sequencing level (e.

DNA-encoded chemical libraries enable the discovery of potent PSMA-ligands with substantially reduced affinity towards the GCPIII anti-target.

Prostate-specific membrane antigen (PSMA) is a tumor-associated protein that has been successfully targeted with small organic ligands and monoclonal antibodies. Pluvicto™ is a PSMA-targeted radioligand therapeutic (RLT) recently approved by the FDA for the treatment of metastatic castration-resistant prostate cancer (2022 FDA marketing authorization). Although a large Phase III clinical trial (VISION trial) demonstrated clinical benefits in patients treated with Pluvicto™, the therapeutic window of the drug is narrowed by its undesired accumulation in healthy organs.

Discovery of Glutamate Carboxypeptidase III Ligands to Compete the Uptake of [Lu]Lu-PSMA-617 in Healthy Organs.

Prostate-specific membrane antigen (PSMA)-targeted radio ligand therapeutics (RLTs), such as [Lu]Lu-PSMA-617 (Pluvicto), have been shown to accumulate in salivary glands and kidneys, potentially leading to undesired side effects. As unwanted accumulation in normal organs may derive from the cross-reactivity of PSMA ligands to glutamate carboxypeptidase III (GCPIII), it may be convenient to block this interaction with GCPIII-selective ligands. Parallel screening of a DNA-encoded chemical library (DEL) against GCPIII and PSMA allowed the identification of GCPIII binders.

Deep Learning Approach for the Discovery of Tumor-Targeting Small Organic Ligands from DNA-Encoded Chemical Libraries.

DNA-Encoded Chemical Libraries (DELs) have 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 models trained on DELs, which expand the scope of DEL-derived chemical motifs.