While AI models are effective for developability properties like stability, they fall short on predicting function. Sanofi's Norbert Furtman notes that generalized affinity prediction is a 'holy grail' problem, and predicting interference with a biological pathway is even harder, as function is not solely explained by structure.

To evolve AI from pattern matching to understanding physics for protein engineering, structural data is insufficient. Models need physical parameters like Gibbs free energy (delta-G), obtainable from affinity measurements, to become truly predictive and transformative for therapeutic development.

Unlike traditional methods that simulate physical interactions like a key in a lock, ProPhet's AI learns the fundamental patterns governing why certain molecules and proteins interact. This allows for prediction without needing slow, expensive, and often impossible physical or computational simulations.

By analyzing information flow, Quantitative Complexity Theory (QCT) pinpoints 'hotspots'—the specific atoms or amino acids that dominate a molecule's dynamics. These hotspots, which carry the largest information footprint, essentially direct the biological 'orchestra.' They provide medicinal chemists with precise targets for re-engineering a molecule's function.

Increasing a biologic's binders from two or four to six or twelve is not an incremental improvement. It creates 'emergent properties of scale.' This high valency allows for sophisticated control over 3D spatial geometry at the cell surface and eliminates the design trade-offs inherent in simpler multispecific molecules.

Current tools excel at the static binding problem. To advance to creating true therapeutics, models must incorporate the physics of displacing solvent and ions from an interface—currently neglected but one of the "biggest enemies" of strong binding in a physiological context.

Moving beyond traditional models focused on structural fit, Expedition's platform incorporates quantum chemistry. It uses Density Functional Theory (DFT) to model electron density and predict the actual probability of a covalent bond forming, enabling the design of specific molecules for previously "undruggable" targets.

When all pharma companies use similar AI models on similar data, competitive moats vanish. The next edge will come from creating superior training data. This means moving beyond raw data to datasets enriched with physical insights, such as a database of 'complexity hotspots' for all known proteins, teaching AI the underlying dynamics.

Effective drug design must move beyond treating targets as simple points on a cell. The cell surface is a complex "kelp forest" where receptor biophysics—target proximity, orientation, epitope location, and protein flexibility—are critical variables. Understanding this 3D complexity is key to creating powerful, next-generation therapeutics.