Generative AI for Drug Discovery | Bio basics | Module 0.2

💖 Support BrainOmega ☕ Buy Me a Coffee: https://buymeacoffee.com/brainomega 💳 Stripe: https://buy.stripe.com/aFa00i6XF7jSbfS9T218c00 💰 PayPal: https://paypal.me/farhadrh 🎥 Feeling lost when biology terms pop up in GenAI drug discovery; like proteins, domains, active sites, and kinetics metrics (Km, Ki, IC50); and you’re not sure what they mean in practice? In this Bio Basics lesson, we build the “minimum biology intuition” you need to follow modern AI-for-drugs workflows, using clear visuals, simple 3D protein viewing, and beginner-friendly dose; response plots. No heavy bio jargon; just the concepts that directly explain targets, binding, and potency numbers you’ll see in papers and datasets. We’ll walk through what proteins are (and why they’re the main drug targets), how domains act like functional modules inside proteins, what an active site pocket really means, and how to interpret the three most common discovery numbers: Km (enzyme behavior), Ki (inhibitor strength), and IC50 (assay potency). 💻 Code on GitHub: https://github.com/frezazadeh/genai-drugdiscovery/blob/main/Module_0_2.ipynb ⸻ 📚 What You’ll Learn (in this lesson) • Proteins: the 3D “machines” drugs bind to • Domains: protein modules with different functions • Active sites: pockets/grooves where binding or catalysis happens • Enzyme kinetics: what Vmax and Km mean (and how to read the curve) • Inhibition: what Ki means (lower = stronger inhibitor) • Dose–response: what IC50 means (50% effect point, assay-dependent) • Why log scales are used (nM → µM → mM ranges) ⸻ ✅ Why Watch This Video? • Beginner-Friendly — biology explained like a story, with visuals • Directly Relevant to GenAI — targets, pockets, and potency numbers drive datasets & labels • Practical Intuition — understand what Km/Ki/IC50 actually tell you • Hands-On — simple plots + optional 3D protein viewer you can reuse anywhere ⸻ 🧬 What We’ll Go Deeper Into Next (in the course) • Protein structure levels: primary → sec