My notes on agentic AI, drug discovery, and teaching
- machine learning
- drug discovery
- target discovery
- book
- agents
- teaching
- writing
- launch
- target discovery
- book
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Likelihood of approval, phase transitions, and the crowded-vs-abandoned map of therapeutic areas
Phase I likelihood of approval is 6.7% industry-wide. Oncology, rare disease, and biomarker-stratified programs tell different stories. A 2026 map.
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Tissue specificity as a safety filter: GTEx, Human Protein Atlas, and scRNA-seq for target prioritization
Tissue specificity predicts on-target, off-tissue side effects before a drug is ever dosed. A practitioner's guide to GTEx, Protein Atlas, and scRNA-seq.
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Novel vs repurposed targets: quantifying novelty and extending drug-repurposing methods
Drug target novelty is measurable, and the trade-off with drug repurposing is the commercial story most pipelines never quantify. A practitioner's map.
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Druggability, ligandability, and modality choice in the AlphaFold 3 era
Druggability assessment used to ask "does this protein have a pocket?" In 2026 it asks "which of six modalities fits this target best?" A field guide.
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How to tell a drug target matters: evidence frameworks for target–disease linkage
Target-disease association evidence is what Phase II efficacy hinges on. A practitioner's guide to 5R, GOT-IT, the omics stack, and knowledge graphs.
