Combining the capabilities of a leading antibody-discovery organization with cutting-edge machine learning unlocks transformative opportunities in biotherapeutics discovery. At AbbVie, we are embedding predictive models directly into scientific workflows, addressing all aspects of a target product profile. This innovation enables programs to progress faster through stage gates, while also making better medicines by exploring previously inaccessible sequence space. We will share the principles that guide our building of this powerful predictive engine, which relies on a robust data platform and is powered by cross-functional teams shaping the future of biotherapeutics discovery.

We outline how we are integrating AI into Sanofi's Large Molecule Research portfolio. Three main areas will be presented: (1) Improving accuracy of internal predictive models through a large-scale experimental effort; (2) Overview of AI to speed up cryoEM; (3) Structure-enabled AI for affinity maturation of biologics. This approach will help us use AI to accelerate our work and amplify the impact of rationally designed molecules.

NextGenPLM: a new paradigm in multimodal foundational models that fuses sequence, 3D structure, and interaction data in a single efficient transformer—enabling high-throughput, repertoire-scale antibody–antigen screening. On a diverse benchmark of antibody–antigen complexes, matches Chai-1 and Boltz-1x on contact-map and epitope accuracy at a fraction of the compute cost. In an internal affinity-maturation campaign, ranking mutants by predicted contact probabilities and masked-language-modeling (MLM) log-likelihoods helped achieve up to 17× affinity improvements—demonstrating its potential for rapid, data-driven biologics discovery.

The rapid advancement of biologics, particularly antibody-based therapeutics, has revolutionized the landscape of drug discovery and development. As the complexity and volume of biologics data continue to grow, effective data management strategies have become essential for accelerating research and ensuring data integrity. This presentation explores the intersection of biologics data management and artificial intelligence (AI) in the context of antibody drug discovery.

Operationalizing machine learning and generative AI in biologics discovery means more than building models—it involves embedding them into scientific workflows to run analyses alongside experimental data. This talk will cover strategies for streamlining data usage for model training, integrating in silico-generated data with laboratory results, and overcoming implementation challenges.

Artificial Intelligence (AI)'s promise in accelerating drug discovery has generated plenty of excitement in biopharma. But how exactly is AI being built and leveraged to fulfill the promise of more scalable and efficient therapeutic design, especially in the field of biologics? Please join me and the speakers of the Innovation Showcase to discover how open source infrastructure, big data, and de novo AI models are changing the biologics development landscape.

Miniproteins are a powerful yet underutilized therapeutic modality with a structure that enables binding with high affinity and specificity to their targets and achieves remarkable stability using only canonical amino acids. By combining de novo design with synthetic biology and laboratory automation, we accelerate the discovery and optimization of protein binders and create a vast toolbox of modular miniprotein domains, each with ideal drug-like properties and developability profiles.

What if you could specify an antibody's immune profile—ADCC without CDC, extended half-life with preserved Fc?R binding—and systematically design the Fc to deliver it? Proteinea is building this capability through what we call "immune logic gates": a framework for programming antibody effector function. This talk introduces the concept and shares how we're constructing the grammar of Fc-immune system interactions.

The EDA Toolkit is an open-source Python library designed to standardize and accelerate exploratory data analysis across domains. It combines automated data profiling, visualization, and preprocessing in a single, reproducible framework. This presentation demonstrates how the toolkit simplifies data strategy, enhances collaboration between teams, and promotes trust in AI model development through transparency and reproducibility.