Post by Dr. Riyaz Syed

𝐏𝐚𝐫𝐭 𝟑 𝐨𝐟 𝐨𝐮𝐫 𝐏𝐑𝐎𝐓𝐀𝐂 𝐬𝐞𝐫𝐢𝐞𝐬 dives deep into how 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐈) is transforming degrader discovery. While traditional physics-based simulations remain powerful, their computational cost and time demands often limit scalability. AI now complements these methods bridging medicinal 𝐜𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲, 𝐜𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐛𝐢𝐨𝐥𝐨𝐠𝐲 through data-driven reasoning. 🧩 𝐓𝐡𝐢𝐬 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐞𝐱𝐩𝐥𝐨𝐫𝐞𝐬: • AI-augmented ligand and warhead discovery using 𝐆𝐫𝐚𝐩𝐡 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐆𝐍𝐍𝐬) • Generative linker design for quicker ideation • 𝐏𝐑𝐎𝐓𝐀𝐂-𝐑𝐋 for reinforcement-learning–guided design • Machine learning–based degradation prediction and ternary-complex scoring. We also discuss how 𝐟𝐮𝐬𝐢𝐨𝐧 𝐨𝐟 𝐌𝐞𝐝𝐢𝐜𝐢𝐧𝐚𝐥 𝐂𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲, 𝐩𝐡𝐲𝐬𝐢𝐜𝐬 𝐛𝐚𝐬𝐞𝐝 𝐬𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐀𝐈 are shaping the next generation of  𝐭𝐚𝐫𝐠𝐞𝐭𝐞𝐝 𝐩𝐫𝐨𝐭𝐞𝐢𝐧 𝐝𝐞𝐠𝐫𝐚𝐝𝐞𝐫𝐬 making design cycles faster, smarter, and more druggable. 📘𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐏𝐑𝐎𝐓𝐀𝐂 𝐛𝐥𝐨𝐠 𝐬𝐞𝐫𝐢𝐞𝐬: 🔹 𝐏𝐚𝐫𝐭 𝟏: Unlocking the Potential of PROTACs: Drugging the "Undruggable" with Targeted Protein Degradation (https://lnkd.in/gxdqrz97) 🔹 𝐏𝐚𝐫𝐭 𝟐: Rational PROTAC Development : Integrating Docking, MD, and Energetics for Efficient Drug Discovery (https://lnkd.in/gMqg4ZX5) 🔹 𝐏𝐚𝐫𝐭 𝟑: AI Approaches in PROTACs: The New Frontier in Targeted Protein Degradation (https://lnkd.in/g8mUQ42w) Dr. Poornachandra Y | Venkateswarlu Nandimandalam | Centella AI Therapeutics #CentellaAI #AIinDrugDiscovery #PROTACs #TargetedProteinDegradation #MachineLearning #Bioinformatics #DrugDiscovery #ComputationalChemistry #MedicinalChemistry