Instead of building from scratch, ProPhet leverages existing transformer models to create unique mathematical 'languages' for proteins and molecules. Their core innovation is an additional model that translates between them, creating a unified space to predict interactions at scale.
ProPhet's CEO notes his conviction in AI wasn't a sudden breakthrough. Instead, it was a growing understanding that machine learning's ability to handle noisy, incomplete data at scale directly solves the primary bottlenecks of traditional pharmaceutical research.
ProPhet uses its AI not just for efficacy (finding a molecule for a target protein) but also for safety. By reversing the query—taking a promising molecule and asking which other proteins it might bind to—they can identify potential off-target interactions, a primary source of toxicity.
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.
ProPhet was founded through Ion Labs, a venture studio created by AstraZeneca, Merck, Pfizer, and Teva. This model allows established pharmaceutical giants to identify acute internal challenges and recruit external talent to build dedicated startups aimed at solving them.
ProPhet's strategy is to focus on 'hard-to-drug' proteins, which are often avoided because they lack the structural data required for traditional discovery. Because ProPhet's AI model needs very little protein information to predict interactions, this data scarcity becomes a competitive advantage.