📘 The Digital Pharmacist
From Genetic Codes to Market-Ready Medicines using AI and Bioinformatics.
Part I: Foundations & The Repurposing Revolution
Chapter 1: The Biological Lock and Key (Proteins, Ligands, and basic cell signaling).
Chapter 2: The $2 Billion Problem (Why traditional R&D is failing and how repurposing saves 10+ years).
Chapter 3: From Serendipity to Strategy (The history of repurposing: from Viagra to COVID-19).
Part II: The AI-Powered Laboratory (The Technical Core)
Chapter 4: The Virtual Screening Workflow (Step-by-step with DrugRep).
Chapter 5: AlphaFold 3 and the "Dark Proteome" (Predicting structures for proteins that have never been seen).
Chapter 6: Deep Learning Docking (Moving beyond physics with DiffDock and ArtiDock).
Chapter 7: Generative Chemistry (Using AI to "write" new chemical variations of existing drugs).
Chapter 8: The Cheat Sheet (Traditional vs. AI-First workflows: Pros and Cons).
Part III: Safety, Success, and the Patient (The Filter)
Chapter 9: ADMET-AI: The Toxicity Shield (Predicting liver, heart, and brain safety in seconds).
Chapter 10: Personalized Repurposing (Using Multi-omics—Genomics + Proteomics—to find a drug for one specific person).
Chapter 11: Real-World Evidence (RWE) (Using AI to scan millions of hospital records to see what drugs are already working off-label).
Part IV: Ethics, Regulation, and Business (The Market)
Chapter 12: The Ethics of the "Black Box" (Addressing bias in AI data and the transparency of medical decisions).
Chapter 13: 2026 Regulatory Landscape (Navigating the new FDA & EMA Guiding Principles for AI in drug development).
Chapter 14: The Y Combinator Blueprint (How startups like PostEra and Recursion pitch and build the future).
Chapter 15: The Autonomous Lab (The rise of "Cloud Labs" where AI directs robots to mix the chemicals).
Visualizing the 2026 Regulatory Barrier
In Chapter 13, you would use a diagram like this to explain the "new" requirements for any AI-discovered drug:
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