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Saram ConsultingThoughts and strategies on the intersection of Biotech, Artificial Intelligence, and Regulatory Compliance.
Diffusion models now design ligands directly inside a binding pocket, and protein language models design binders that natural evolution never produced. Then PoseBusters, GenBench3D, BOOM and Runs N' Poses all hit the same wall: the models are interpolating. A verified map of the molecular design stack — with a ledger of which headline numbers are reproducible and which are marketing.
Read Article →Pharma AI's intellectual case is now well-evidenced. Its business case is the least verifiable thing in the stack: three market forecasts measuring three different things quoted as one series, two ROI case studies with no named company, productivity percentages resting on a single trade-press commentary, and two funding figures that are simply wrong. What actually verifies is the capital.
Read Article →AI now designs molecules, predicts ADMET, matches trial patients, and drafts regulatory documents across the pharma value chain — and has not moved a single drug to approval. The binding constraints are data readiness, validation architecture, and workforce qualification, not model capability. Here is the map, with the numbers checked against primary sources.
Read Article →TypeSafe's Jev removes an entire class of pipeline failure — but its 0% error rate is a construction guarantee, not a measurement, and it loses 17 points on document-heavy work. Here is where the confidence-gated decision layer actually belongs in a GxP architecture.
Read Article →FDA issued its first AI-focused cGMP warning letter. MHRA caught AI-generated inspection responses citing nonexistent regulations. EMA is writing Annex 22 specifically for AI in GMP. The audit profession is fundamentally rewiring how it evaluates AI — and most biopharma companies aren't ready.
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