We will discuss the “Lab-in-the-loop” system, a collaboration between Prescient Design and Antibody Engineering at Genentech, to build and integrate state-of-the-art machine learning methods with large molecule design and discovery capabilities. Lab-in-the-loop encompasses generative models, pseudo-oracles, physics-based modeling, large language models, wet-lab assays, and active learning to fundamentally change early-stage drug discovery.

Most therapeutic antibodies are simple antagonists. However, like all proteins, antibodies can be sophisticated nano-machines. Biolojic Design uses AI to program antibodies to become dynamic functional switches affecting biology in new ways. I will describe our AI-design process, and share clinical data from the first AI-designed therapeutic antibody. I will also show preclinical data on multi-specific antibodies illustrating their potential to improve outcome in cancer and autoimmune diseases.

Incorporating predictive modeling into experimental workflows holds great promise for accelerating discovery, guiding optimization, and prioritizing leads. Here we discuss application of ML to design of synthetic libraries for discovery, hybrid experimental-modeling approaches for selection of functionally diverse antibodies, and developability predictions that decrease resource-intensive experiments. Our integration of ML into a larger informatics/data platform enables predictions to inform candidate selection throughout the discovery and lead optimization process.

AI applications have become increasingly powerful, and play an increasing role in today's research and development. At the same time, there is no consensus yet regarding AI methodologies, and how to best integrate them in the discovery and development process. Building internal capabilities and establishing external collaborations can help navigate through this exciting new augmentation of biologics drug development.

In silico antibody discovery is emerging as a powerful alternative to traditional in vivo and in vitro approaches. Many challenges, however, remain open to enabling the properties of designed antibodies to match those produced by the immune system. I am going to describe the role of conformational entropy, as determined by accurate statistical mechanics calculations, in determining the binding affinity of designed antibodies for their targets.

We will discuss how Seismic Therapeutic is using its IMPACT platform to integrate machine learning, structural biology, protein engineering and translational Immunology to accelerate the discovery and development of therapeutics for autoimmune diseases, caused by a dysregulated adaptive immune system.

This presentation will overview the need for foundational high quality digital wet-lab and multi-lingual/inter-disciplinary collaboration for antibody and peptide AI ML. It will then discuss FAIRe(nough) data in Research for AI ML applications and a new method for ultra high throughput data generation to enable ML. Finally, it will illustrate progress in benchmarking computational strategies, FEP+ & LLMs, for affinity predictions and successful generative ML models.

Generative language models trained on protein sequences have proven incredibly powerful for protein sequence design. In this talk, we will demonstrate how protein language models enable discovery of diverse proteins, which often function on par with natural counterparts- despite significant deviation in sequence space. Beyond generation of sequences, protein language models are effective zero-shot predictors of fitness, enabling direct optimization of function.