Combining pharmacophore models derived from DNA-encoded chemical libraries with structure-based exploration to predict Tankyrase 1 inhibitors

Widespread false negatives in DNA-encoded library data: how linker effects impair machine learning-based lead prediction

DNA-encoded chemical libraries (DECLs) have become integral to early-stage drug discovery, yielding active compounds and extensive labeled datasets for machine learning (ML)-based prediction of bioactive molecules. However, the information content of DECL selection data remains scarcely explored. This study systematically investigates for the first time the prevalence of false negatives and the influence of the linker in DECL data. Using a focused DECL targeting the poly-(ADP-ribose) polymerases PARP1/2 and TNKS1/2 as a model system, we found that our DECL selections frequently miss active compounds, with numerous false negatives for each identified hit. The presence of the DNA-conjugation linker emerged as a factor contributing to the underdetection of active molecules. This bias toward false negatives compromises the predictive power of DECL data for prioritizing hits, anticipating target selectivity, and training ML models, as determined by analyzing the effects of undersampling and oversampling techniques in learning the PARP2 data. Conversely, the linker's presence in DECLs offers advantages, such as enabling the identification of target-selective protein engagers, even when the underlying molecules themselves may not be selective. These findings highlight the challenges and opportunities of DECL data, emphasizing the need for best practices in data handling and ML model development in drug discovery.

Challenges and Prospects of DNA-Encoded Library Data Interpretation

DNA-encoded library (DEL) technology is a powerful platform for the efficient identification of novel chemical matter in the early drug discovery process enabled by parallel screening of vast libraries of encoded small molecules through affinity selection and deep sequencing. While DEL selections provide rich data sets for computational drug discovery, the underlying technical factors influencing DEL data remain incompletely understood. This review systematically examines the key parameters affecting the chemical information in DEL data and their impact on hit triaging and machine learning integration. The need for rigorous data handling and interpretation is emphasized, with standardized methods being critical for the success of DEL-based approaches. Major challenges include the relationship between sequence counts and binding affinities, frequent hitters, and the influence of factors such as inhomogeneous library composition, DNA damage, and linkers on binding modes. Experimental artifacts, such as those caused by protein immobilization and screening matrix effects, further complicate data interpretation. Recent advancements in using machine learning to denoise DEL data and predict drug candidates are highlighted. This review offers practical guidance on adopting best practices for integrating robust methodologies, comprehensive data analysis, and computational tools to improve the accuracy and efficacy of DEL-driven hit discovery.

Agonist Discovery for Membrane Proteins on Live Cells by Using DNA-encoded Libraries

Identifying biologically active ligands for membrane proteins is an important task in chemical biology. We report an approach to directly identify small molecule agonists against membrane proteins by selecting DNA-encoded libraries (DELs) on live cells. This method connects extracellular ligand binding with intracellular biochemical transformation, thereby biasing the selection toward agonist identification. We have demonstrated the methodology with three membrane proteins: epidermal growth factor receptor (EGFR), thrombopoietin receptor (TPOR), and insulin receptor (INSR). A ∼30 million and a 1.033 billion-compound DEL were selected against these targets, and novel agonists with subnanomolar affinity and low micromolar cellular activities have been discovered. The INSR agonists activated the receptor by possibly binding to an allosteric site, exhibited clear synergistic effects with insulin, and activated the downstream signaling pathways. Notably, the agonists did not activate the insulin-like growth factor 1 receptor (IGF-1R), a highly homologous receptor whose activation may lead to tumor progression. Collectively, this work has developed an approach toward "functional" DEL selections on the cell surface and may provide a widely applicable method for agonist discovery for membrane proteins.

Encoding and display technologies for combinatorial libraries in drug discovery: The coming of age from biology to therapy

Drug discovery is a sophisticated process that incorporates scientific innovations and cutting-edge technologies. Compared to traditional bioactivity-based screening methods, encoding and display technologies for combinatorial libraries have recently advanced from proof-of-principle experiments to promising tools for pharmaceutical hit discovery due to their high screening efficiency, throughput, and resource minimization. This review systematically summarizes the development history, typology, and prospective applications of encoding and displayed technologies, including phage display, ribosomal display, mRNA display, yeast cell display, one-bead one-compound, DNA-encoded, peptide nucleic acid-encoded, and new peptide-encoded technologies, and examples of preclinical and clinical translation. We discuss the progress of novel targeted therapeutic agents, covering a spectrum from small-molecule inhibitors and nonpeptidic macrocycles to linear, monocyclic, and bicyclic peptides, in addition to antibodies. We also address the pending challenges and future prospects of drug discovery, including the size of screening libraries, advantages and disadvantages of the technology, clinical translational potential, and market space. This review is intended to establish a comprehensive high-throughput drug discovery strategy for scientific researchers and clinical drug developers.

Protein-templated ligand discovery via the selection of DNA-encoded dynamic libraries

DNA-encoded chemical libraries (DELs) have become a powerful technology platform in drug discovery. Dual-pharmacophore DELs display two sets of small molecules at the termini of DNA duplexes, thereby enabling the identification of synergistic binders against biological targets, and have been successfully applied in fragment-based ligand discovery and affinity maturation of known ligands. However, dual-pharmacophore DELs identify separate binders that require subsequent linking to obtain the full ligands, which is often challenging. Here we report a protein-templated DEL selection approach that can identify full ligand/inhibitor structures from DNA-encoded dynamic libraries (DEDLs) without the need for subsequent fragment linking. Our approach is based on dynamic DNA hybridization and target-templated in situ ligand synthesis, and it incorporates and encodes the linker structures in the library, along with the building blocks, to be sampled by the target protein. To demonstrate the performance of this method, 4.35-million- and 3.00-million-member DEDLs with different library architectures were prepared, and hit selection was achieved against four therapeutically relevant target proteins.

Discovery and Optimization of WDR5 Inhibitors via Cascade Deoxyribonucleic Acid-Encoded Library Selection Approach

The DNA-encoded library (DEL) is a powerful hit generation tool for chemical biology and drug discovery; however, the optimization of DEL hits remained a daunting challenge for the medicinal chemistry community. In this study, hit compounds targeting the WIN binding domain of WDR5 were discovered by the initial three-cycle linear DEL selection, and their potency was further enhanced by a cascade DEL selection from the focused DEL designed based on the original first run DEL hits. As expected, these new compounds from the second run of focused DEL were more potent WDR5 inhibitors in the protein binding assay confirmed by the off-DNA synthesis. Interestingly, selected inhibitors exhibited good antiproliferative activity in two human acute leukemia cell lines. Taken together, this new cascade DEL selection strategy may have tremendous potential for finding high-affinity leads against WDR5 and provide opportunities to explore and optimize inhibitors for other targets.

Synthesis of Functionalized Triazoles on DNA via Azide-Acetonitrile "Click" Reaction

Triazoles are privileged structural motifs that are embedded in a number of molecules with interesting biological activities. In this work, we developed a practical and general synthetic strategy to construct a medicinally important 5-amino-1,2,3-triazole moiety on DNA by coupling DNA-conjugated azides and monosubstituted acetonitriles via azide-acetonitrile "click" reaction. Under mild reaction conditions, this reaction displayed a broad substrate scope. Most substrates gave moderate-to-excellent conversions. Thus, this DNA-compatible reaction could be employed in practical DNA-encoded library (DEL) construction and potentially expand the chemical space of DNA-encoded libraries.

Evaluation of the Topology Space of DNA-Encoded Libraries

DNA-encoded libraries (DELs) are widely used in the discovery of drug candidates, and understanding their design principles is critical for accessing better libraries. Most DELs are combinatorial in nature and are synthesized by assembling sets of building blocks in specific topologies. In this study, different aspects of library topology were explored and their effect on DEL properties and chemical diversity was analyzed. We introduce a descriptor for DEL topological assignment (DELTA) and use it to examine the landscape of possible DEL topologies and their coverage in the literature. A generative topographic mapping analysis revealed that the impact of library topology on chemical space coverage is secondary to building block selection. Furthermore, it became apparent that the descriptor used to analyze chemical space dictates how structures cluster, with the effects of topology being apparent when using three-dimensional descriptors but not with common two-dimensional descriptors. This outcome points to potential challenges of attempts to predict DEL productivity based on chemical space analyses alone. While topology is rather inconsequential for defining the chemical space of encoded compounds, it greatly affects possible interactions with target proteins as illustrated in docking studies using NAD/NADP binding proteins as model receptors.