This talk will describe microfluidics-enabled cellular phenotypic DEL workflow—MicDrop. We will introduce cellular DEL screen in droplets, followed by results from a cellular protein degradation screen with a validation library, as well as another set of screens with a prospective library. Our results show the benefits of bead replicates and how this new paradigm of DEL screen can accelerate the field of molecular glue discovery for TPD and beyond.

There is considerable interest in the development of platforms for screening DNA-encoded libraries (DELs) functionally, that is for agonists or antagonists of a given process. The only existing methods require specialized microfluidics infrastructure. We present here a "low tech" platform that allows one-bead-one-compound DELs to be screened for compounds capable of mediating various post-translational modifications, including poly-Ubiquitylation, of a protein of interest.

I will give an overview about the recently created DEL Consortium and present  the advantages and learnings it provides on pre-competitive collaboration in the pharma industry.

I will discuss our progress toward further miniaturizing and automating the split-and-pool synthesis of solid-phase DELs and a new microfluidics-free approach to activity-based and cellular DEL screening.

DEL hits have traditionally been evaluated via off-DNA resynthesis and biological testing. This approach can be time- and resource-intensive, limiting the number of putative hits selected for follow-up, and hits often fail to confirm off-DNA. On-DNA hit resynthesis increases throughput and emulates the original library synthesis, enabling identification of side product binders. Here we share GSK’s application of on-DNA binder confirmation to evaluate and expand hits from DEL screens.

DNA-Encoded Chemistry Technology (DEC-Tec) is a cost-effective, rapidly advancing platform designed to identify drug-like molecules with high-affinity binding to target proteins. Instead of constructing DELs aimed at broadly modulating various targets, our approach initiates with a specific target in mind, creating a smaller, tailored DEL to enhance precision. This targeted library design improves the quality and possibility of positive hits by leveraging structural and binding insights specific to the target protein. 

I will present a platform-science based 'lessons learned' talk from two case studies of screening ‘small’ and ‘big’ DEL libraries. The presentation emphasizes the importance of library size and chemical diversity.

Using a co-factor directed screening strategy and DNA-encoded libraries, a class of MTA-cooperative PRMT5 inhibitors was identified. Structural studies show that the hit series occupies the arginine substrate pocket of MTA-bound PRMT5, while simultaneously exhibiting a hydrophobic interaction to MTA. Further optimisation led to lead compounds, which potently and selectively inhibit PRMT5 in MTAP-deleted cells and show in vivo efficacy in an MTAP-deleted cancer cell model.

DNA-Encoded Libraries and Biochemical screens identified two Respiratory Syncytial Virus polymerase-inhibiting ligands with differentiated chemotype and binding modes. The two binding sites were confirmed by cryo-EMs, of which one was a hitherto undescribed binding pocket. Hit-to-lead effort expanded SAR for both series and confirmed their antiviral activities, while mapping out potential vectors for improvement in potency and other parameters. Scaffold hopping further aided optimization by diversifying the chemical matter.

DNA-encoded libraries (DELs) enable screening billions of ligands against protein targets of interest. To select hits for off-DNA evaluation, quantitative structure-activity relationship (QSAR) modeling is frequently used to find structural features that contribute to enrichment. However, current QSAR typically relies on 2D molecular representations. We leverage machine learning to learn 3D molecular representations for application in hit selection.

Target class–focused drug discovery has a strong track record in pharmaceutical research, yet public domain data indicate that many protein families remain unliganded. Here we present a systematic approach to scale up the discovery and characterization of small molecule ligands for the WD40 repeat (WDR) protein family. A pilot hit-finding campaign using DNA-encoded chemical library selection followed by machine learning (DEL-ML) yielded first-in-class, drug-like ligands for 7 of the 16 WDR domains screened. This study establishes a template for evaluation of protein family ligandability and provides extensive WDR resources to discover ligands for this underexplored target class.

DNA-encoded libraries (DELs) are traditionally used to find potential hit compounds for specific targets. At insitro, we are enhancing this technology to aid in later stages of drug discovery. We employ targeted second-generation DELs for efficient exploration of chemical space surrounding hit structures. By employing affinity-based electrophoretic separations, such as nDexer and capillary electrophoresis, we can rank DEL members, facilitating early structure-activity relationship (SAR) hypothesis formation and machine-learning model training to refine predictive accuracy in this chemical environment. As a practical example, we will showcase the design and construction of three second-generation DELs: one based on a DEL hit, another on a literature compound, and the third on virtual docking.