AI Drug Discovery
Beyond Storage: Building a Data Fabric for AI-Driven Drug Discovery
Storage is not strategy. Pharmaceutical organizations that treat data management as a storage problem — how to accumulate and preserve the largest possible volume of data — are building the wrong foundation for AI-driven drug discovery. The organizations seeing real AI results have moved beyond storage to something more architecturally demanding: a data fabric that […]
Architectural Constraints and Failure Modes in AI-Driven Drug Discovery Programs
AI programs in pharmaceutical R&D fail for specific, architectural reasons. Understanding these failure modes before building is not theoretical caution — it is the difference between programs that produce actionable outputs and programs that consume budget without advancing drug development. This article documents the most common architectural constraints encountered in AI-driven discovery programs and maps […]
AI-Assisted Drug Discovery: Why Governed Data Is the Rate Limiter, Not Model Capability
The pharmaceutical industry has invested heavily in artificial intelligence over the past decade. The results have been uneven — not because the models are inadequate, but because the data feeding those models is. In project after project, the root cause of AI failure in drug discovery is not model architecture. It is the quality, consistency, […]