Post by Maryam Abdulfattah🧡

🧬 Week 2 at The Insilico Lab — and the science just got deeper! I'm continuing my journey through the 3-Week Virtual Training in Computational Drug-Aided Discovery (CADD), Cohort 4, and Week 2 brought three powerful, hands-on tasks that sit at the very heart of early-stage drug development. 💊 Task 1 — Drug-likeness Screening (Lipinski's Rule of Five) Using PubChem and SwissADME, I screened three clinically relevant kinase inhibitors — Erlotinib, Gefitinib, and Lapatinib — for oral drug-likeness by applying Lipinski's Rule of Five. 🔬 Key findings: • Erlotinib (MW: 393.44 Da) → 0 violations ✅ • Gefitinib (MW: 446.9 Da) → 0 violations ✅ • Lapatinib (MW: 581.06 Da) → 1 violation (MW > 500) — still acceptable ✅ This task taught me that drug-likeness is not just a checkbox — it's a filter that saves years of failed development by predicting oral bioavailability early. 🖥️ Task 2 — Molecular Docking & Interaction Analysis Using PyRx (AutoDock Vina) and the prepared 1M17 protein from Week 1, I docked all three compounds into the target binding site and recorded their binding affinities: 🏆 Docking scores (kcal/mol): • Erlotinib → -6.8 • Gefitinib → -7.9 • Lapatinib → -8.6 (strongest binder!) I then visualized the protein–ligand interactions in Biovia Discovery Studio, generating both 3D interaction views and 2D ligand interaction diagrams showing hydrogen bonds and hydrophobic contacts. Lapatinib's superior docking score aligned with its richer interaction network in the binding pocket — a powerful lesson in how structure drives affinity. @TheInsilicoLab ⚗️ Task 3 — ADMET Prediction Using ADMETlab 3.0, I evaluated Absorption, Distribution, Metabolism, Excretion, and Toxicity profiles for all three compounds. The results revealed a fascinating trade-off: • Erlotinib showed the best overall ADMET profile — better Caco-2 permeability, lowest hERG inhibition risk, and superior solubility. • Lapatinib, despite its strongest docking score, had the poorest bioavailability profile due to high molecular weight, poor BBB penetration, and higher cardiac toxicity risk. This was perhaps the most important lesson of the week: the best binder is not always the best drug. ADMET profiling is what bridges the gap between computational prediction and clinical reality. The Insilico Lab This week reinforced that drug discovery is a multi-layered optimization problem — and I'm grateful to The Insilico Lab for making these cutting-edge tools accessible and practical. More to come in Week 3! 🚀 #CADD #DrugDiscovery #ComputationalBiology #VirtualResearch #TheInsilicoLab #Chemoinformatics #MolecularDocking #ADMET #Bioinformatics #DrugDesign