How are biotech startups using AI for drug discovery?

The archive reveals a sharp divide between theory and outcome in AI-driven drug discovery. Companies marketing themselves as "AI first" have made little clinical progress; not a single drug developed by a purely AI-driven drug discovery company has cleared phase two trials [#420]. The constraint that matters is not algorithmic sophistication but integration with domain expertise and clinical reality.

The successful model couples AI tooling with deep therapy-area knowledge. Rather than running algorithms across massive datasets to identify targets and generate molecular structures in isolation, the better-resourced programmes use AI to expose hidden relationships in existing data [#215] and inform go/no-go decisions within a rigorous clinical development strategy [#304]. This is less glamorous than "AI drug discovery" suggests, and explains why the venture premium around AI-native platforms has not translated to approval velocity. The currency of biotech remains what it always has: medicines that improve lives, not novel technology. AI is an accelerant within that framework, not a replacement for it.