An exploration of how investors view the potential opportunities in the AI-enabled drug development space, both from the private and public company perspectives. This includes priorities in new investments, how investors think about fair value of AI platforms, and how the space is viewed more broadly.

TuneLab provides access to Lilly-trained artificial intelligence models to help accelerate breakthrough medicines to patients. The platform employs federated learning, a privacy-preserving approach that enables biotechs to tap into Lilly's AI models without directly exposing their proprietary data. The talk will discuss how you can become a participant in this first-of-its-kind, collaborative AI/ML drug discovery platform.

OpenFold3 is an open-source AI model for predicting biomolecular structures across proteins, ligands, and nucleic acids, developed through the OpenFold Consortium. Built through collaboration among academic, biotech, and pharmaceutical partners, it combines shared priorities with coordinated investment in data, compute, and model development. Early benchmarking shows competitive protein–ligand performance, while ongoing fine-tuning is focused on antibody–antigen modeling and other translationally relevant applications for future drug discovery and structural biology use.

The Chatterjee Lab at Penn develops generative AI methods for designing functional biologics. We first built language models that create peptides to bind and control undruggable disease targets, with validation across neurodegeneration, pediatric cancer, and viral infection. We then developed discrete diffusion and flow-matching models to generate peptides, proteins, and mRNAs that balance binding with drug-like properties such as solubility, stability, and safety. More recently, we introduced Schrödinger Bridge models that learn biological trajectories, enabling context-aware design across protein folding, cell-state transitions, and therapeutic intervention. These tools also extend beyond medicine to applications such as heavy metal bioremediation.

We will present an AI-orchestrated lab-in-the-loop framework for oral peptide optimization that coordinates computational assessment and experimental validation across discovery cycles. By selecting the most informative readouts at each step, it enables simultaneous optimization of affinity, membrane permeability, and stability while reducing cost and shortening DMTA cycles. We will also show how hybrid AI + physics-based membrane permeability modeling is translated to GI permeability through delta learning.

Predicting ADMET properties is vital for drug discovery, yet retrospective benchmarks often mask model failures in real-world applications. OpenADMET addresses this through blind challenges using prospective, unpublished data, establishing a transparent baseline for the state of the art. By exposing where current models fail to generalize to novel chemical space, these challenges cut through the noise of "optimistic" internal metrics. This presentation details recent outcomes and identifies the specific data standards and architectural shifts necessary to move beyond the hype, transforming predictive modeling into a robust, reliable tool for prospective drug discovery.