This presentation will focus on novel, chemically enhanced phage libraries. Phage display is a high-throughput screening platform for peptide discovery, which has, however, been limited to natural peptides. Chemical modification of phage, via novel and highly efficient chemistries, allows access to nonnatural peptide libraries and greatly expands the power of this high-throughput screening technology towards the development of peptide therapeutics.

In vivo phage display enables peptide discovery within native environments, but the large size of phage particles limits extravasation into target tissues, resulting in underrepresentation of relevant binders. Comparative in vivo and ex vivo phage display across fresh tissues defines how tissue accessibility shapes peptide recovery and selection bias. These differences are critical for peptide delivery and for evaluating off-target binding that may be underestimated by extravasation constraints.

Macrocyclic peptides have gained increased attention amid claims they are a “Goldilocks” therapeutic modality that can encode the selectivity of a biologic in a footprint close to that of a small molecule. Here we attempt to find a peptide that binds selectively to NRAS, sparing HRAS and KRAS, while accessing the cytosol via passive cell permeability. To do so, we combine subtractive affinity selection with mRNA display to identify Compound 1, an 11mer macrocyclic peptide which binds NRAS at a novel allosteric site between Helix 3 and Helix 4 of the GTPase domain. Compound 1 has total isoform selectivity and can be tuned to achieve activation-state selectivity with a single amino acid change. While it has preferential affinity for oncogenic NRAS specific mutations, it does not inhibit NRAS function or achieve passive membrane permeability.

In this work, we developed a novel pipeline for engineering de novo miniproteins to target the Interleukin-4 receptor alpha (IL4Ra), a crucial receptor in Th2-mediated inflammatory diseases currently dominated by monoclonal antibodies. To achieve this, we used a scaffolding strategy to extract key cytokine helices and used RFdiffusion to design the connecting loops. Initial binders with sub-micromolar affinities were then optimized using molecular dynamics and phage display libraries for affinity maturation. Ultimately, this combined generative AI and directed evolution approach yielded high-affinity miniprotein binders, proving that diffusion models can successfully replicate complex cytokine interfaces for next-generation therapeutics.

Transcription factors and other intracellular protein-protein interactions remain challenging therapeutic targets. This presentation describes an integrated live-cell discovery platform combining genetically encoded peptide libraries, intracellular cyclisation, functional selection and scalable hit validation. By constraining peptides during selection, the workflow enriches cell-tolerated, biostable antagonists that disrupt transcription factor-DNA and protein-protein interactions. The approach links library design, screening, sequencing-based prioritization, biophysical characterization, and cellular validation to accelerate discovery of functional, cell-active constrained peptides.

At IPI, we’ve developed a flexible automation fleet capable of executing hundreds of antibody discovery campaigns annually. This platform enables us to adapt rapidly and in lockstep with AI tools as they reshape the bottlenecks in the discovery process. This approach allows us to scale antigen screening, antibody production, and respond quickly to changing experimental demands from emerging AI-driven methods.

High-throughput stable pool platforms can bridge the gap between transient protein production from HEK/CHO cells and downstream CLD requirements. Stable pool generation for multiple candidates, early in Discovery, can provide scalable manufacturability assessment across hundreds of molecules. Harmonizing cells, processes, and vector systems with CLD, including a universal vector compatible with transient and stable expression, can improve predictability and expand protein production flexibility and support better biologics candidate selection.

Evaluating T cell–dependent bispecific antibodies with traditional endpoint killing assays fails to capture response kinetics and lacks the throughput needed to screen multiple parameters of activity. To overcome these bottlenecks, we developed an automated, high-throughput 384-well platform that enables concurrent kinetic tracking of target cell cytolysis, T cell activation, and multiplexed cytokine profiling. This integrated workflow leverages customized automation and advanced data analytics to streamline lead candidate selection and rapidly identify optimal combination therapies.

Autonomous protein discovery takes more than connecting an AI model to a robot. The complete design-build-test-learn loop—from sequence design through DNA construction, expression, purification, measurement, and model update—must be engineered to learn from every experiment. Drawing on million-scale sequence-function landscapes and protein-mining, directed-evolution, and de novo design workflows, this talk examines what makes the loop close in practice: modular execution across manual, robotic, and biofoundry environments; machine-readable experimental context; quality control; failure annotation; and intelligent selection of the next experiment. The result is a practical blueprint for building more reproducible protein-discovery workflows.

This presentation describes the integration of automated ÄKTA purification platforms into scalable protein-production workflows. By combining chromatography systems with autosamplers and adaptable, variable-driven methods, these platforms support automated purification across multiple production scales. The approach reduces manual intervention, increases processing capacity, and provides the flexibility needed to accommodate diverse proteins, sample volumes, and project requirements.

We present a fully integrated, end-to-end platform that registers, prepares, measures, tracks, and stores DNA and protein samples across biologics discovery. Built to relieve the inventory and sample-preparation bottlenecks that increasingly limit progress, the platform raises sample throughput, shortens turnaround, and frees scientists to focus on discovery. We share our design philosophy, integration approach, and early learnings from deployment.

The biotherapeutics landscape is rapidly evolving with emerging new modalities and innovative strategies driving discovery. To keep up with this growth, discovery platforms need to be built with adaptability at the forefront. In this talk, we will share strategies leveraged at AbbVie Biotherapeutics & Genetic Medicine to enable forward compatibility. We will address automation, data capture, and custom engineering solutions to adopt our instrumentation to our science. Additionally, we will share how we empower our scientists to adapt our platforms agilely to execute on the pipeline.