Identification and Validation of New Interleukin-2 Ligands Using DNA-Encoded Libraries

On-DNA Synthesis of Multisubstituted Indoles

The increasing role of the DNA-encoded library technology in early phase drug discovery represents a significant demand for DNA-compatible synthetic methods for therapeutically relevant heterocycles. Herein, we report the first on-DNA synthesis of multisubstituted indoles via a cascade reaction of Sonogashira coupling and intramolecular ring closure. Further functionalization by Suzuki coupling at the third position exploits a diverse chemical space. The high fidelity of the method also enabled the construction of an indole-based mock library.

Small-molecule discovery through DNA-encoded libraries

The development of bioactive small molecules as probes or drug candidates requires discovery platforms that enable access to chemical diversity and can quickly reveal new ligands for a target of interest. Within the past 15 years, DNA-encoded library (DEL) technology has matured into a widely used platform for small-molecule discovery, yielding a wide variety of bioactive ligands for many therapeutically relevant targets. DELs offer many advantages compared with traditional screening methods, including efficiency of screening, easily multiplexed targets and library selections, minimized resources needed to evaluate an entire DEL and large library sizes. This Review provides accounts of recently described small molecules discovered from DELs, including their initial identification, optimization and validation of biological properties including suitability for clinical applications.

Privileged heterocycles for DNA-encoded library design and hit-to-lead optimization

It is well known that heterocyclic compounds play a key role in improving drug activity, target selectivity, physicochemical properties as well as reducing toxicity. In this review, we summarized the representative heterocyclic structures involved in hit compounds which were obtained from DNA-encoded library from 2013 to 2021. In some examples, the state of the art in heterocycle-based DEL synthesis and hit-to-lead optimization are highlighted. We hope that more and more novel heterocycle-based DEL toolboxes and in-depth pharmaceutical research on these lead compounds can be developed to accelerate the discovery of new drugs.

Isolation of a Natural Killer Group 2D Small-Molecule Ligand from DNA-Encoded Chemical Libraries

Natural Killer Group 2D (NKG2D) is a homo-dimeric transmembrane protein which is typically expressed on the surface of natural killer (NK) cells, natural killer T (NKT) cells, gamma delta T (γδT) cells, activated CD8 positive T-cells and activated macrophages. Bispecific molecules, capable of bridging NKG2D with a target protein expressed on the surface of tumor cells, may be used to redirect the cytotoxic activity of NK-cells towards antigen-positive malignanT-cells. In this work, we report the discovery of a novel NKG2D small molecule binder [K D = (410±60) nM], isolated from a DNA-Encoded Chemical Library (DEL). The discovery of small organic NKG2D ligands may facilitate the generation of fully synthetic bispecific adaptors, which may serve as an alternative to bispecific antibody products and which may benefit from better tumor targeting properties.

Discovery of TIGIT inhibitors based on DEL and machine learning

Drug discovery has entered a new period of vigorous development with advanced technologies such as DNA-encoded library (DEL) and artificial intelligence (AI). The previous DEL-AI combination has been successfully applied in the drug discovery of classical kinase and receptor targets mainly based on the known scaffold. So far, there is no report of the DEL-AI combination on inhibitors targeting protein-protein interaction, including those undruggable targets with few or unknown active scaffolds. Here, we applied DEL technology on the T cell immunoglobulin and ITIM domain (TIGIT) target, resulting in the unique hit compound 1 (IC50 = 20.7 μM). Based on the screening data from DEL and hit derivatives a1-a34, a machine learning (ML) modeling process was established to address the challenge of poor sample distribution uniformity, which is also frequently encountered in DEL screening on new targets. In the end, the established ML model achieved a satisfactory hit rate of about 75% for derivatives in a high-scored area.