I think one of the most promising aspects of AI for drug discovery is the benefit of multi-modality and the ability to build models for many distinct tasks. In his 2012 essay outlining Eroom's Law (drug discovery's exponentially declining R&D efficiency), one of Jack Scannell's "diagnoses" for the problem was the 'basic research–brute force' bias. We have a tendency to overestimate the impact of scaling early-stage discovery technologies. Often times, these assays have low "predictive validity" of clinical success. One way that AI models help solve this problem is that they can incorporate more translationally relevant predictions into the very earliest stages of discovery. I think this is the genius of Brandon and Alex's vision at Axiom. By reducing the cost, time, and friction associated with toxicity testing, it can be pulled much earlier in the discovery process—as soon as you have a molecule. This is one of the most radical departures of molecular machine learning relative to early computational chemistry efforts. A large number of different discovery criteria can be accounted for in a single forward pass.