The long-term strategy for AI in drug discovery is a two-step process. First, create an AI platform to design effective drugs. Second, after a dozen or so AI-designed drugs succeed, use that data to convince regulators to trust AI predictions, potentially allowing future drugs to skip steps like animal testing and accelerate trials.

The push away from animal models is a technical necessity, not just an ethical one. Advanced therapeutics like T-cell engagers and multispecific antibodies depend on human-specific biological pathways. These mechanisms are not accurately reproduced in animal models, rendering them ineffective for testing these new drug classes.

While AI promises to design therapeutics computationally, it doesn't eliminate the need for physical lab work. Even if future models require no training data, their predicted outputs must be experimentally validated. This ensures a continuous, inescapable cycle where high-throughput data generation remains critical for progress.

The future of AI in drug discovery is shifting from merely speeding up existing processes to inventing novel therapeutics from scratch. The paradigm will move toward AI-designed drugs validated with minimal wet lab reliance, changing the key question from "How fast can AI help?" to "What can AI create?"

AI's primary value in early-stage drug discovery is not eliminating experimental validation, but drastically compressing the ideation-to-testing cycle. It reduces the in-silico (computer-based) validation of ideas from a multi-month process to a matter of days, massively accelerating the pace of research.

It's impossible to generate human data at the scale of in silico experiments. The key is to create highly accurate simulations of human physiology (digital twins) and then validate their predictions with limited, strategic human data. If the model proves reliable, it could drastically accelerate R&D.

While AI for novel drug discovery has lofty goals, its most practical value lies in accelerating development. This includes applying AI to de-risked assets for new indications, improving delivery methods, and designing faster, more effective clinical trials, which is where the real bottleneck lies.

Generative AI is not viewed as a standalone solution for drug discovery. Alloy's perspective is that its primary value is in enhancing and automating existing workflows. The model requires a 'lab in the loop' and 'human in the loop,' where AI assists scientists by making them more efficient and improving data analysis, rather than replacing the core wet lab process.

The FDA is eliminating mandatory animal testing because it's often misleading—90% of drugs passing animal studies fail in humans. The agency is embracing modern alternatives like computational modeling and organ-on-a-chip technology to get faster, more accurate safety data.