Pharma
Architectural Constraints and Failure Modes in AI-Driven Drug Discovery Programs
Executive Summary (TL;DR) AI-driven drug discovery failures are rarely algorithmic first. Data validity, measurement bias, and biological misalignment break earlier. Binding affinity predictions do not equate to therapeutic effect. Misinterpreting this distinction propagates costly false positives. Model interpretability constraints directly affect regulatory defensibility, reproducibility, and cross-team adoption. Infrastructure complexity emerges from data heterogeneity, not scale […]
Computer-Aided Drug Discovery (CADD): Architectural Decision Framework for Data, Models, and Scientific Throughput
Executive Summary (TL;DR) CADD initiatives are constrained less by algorithms than by data reliability, validation latency, and workflow friction. Prediction accuracy without experimental translation fails to produce operational value. Infrastructure throughput, storage architecture, and environment stability directly affect scientific cycle time. Regulated environments introduce lineage, reproducibility, and auditability requirements that reshape modeling choices. Trust breakdown […]
Stop Re-Inventing the Wheel: How Semantic Content Libraries Accelerate Drug Repurposing
The Value of “Old” Drugs Discovering a new chemical entity (NCE) is risky 90% fail. Drug Repurposing (finding new uses for existing drugs) is the strategic shortcut. These drugs have already passed toxicity screens; their safety profiles are known. A famous example of this is Baricitinib. Originally a rheumatoid arthritis drug, it was identified by […]