Deepak Damodarr’s Post

This year's Nobel Prize in Chemistry celebrates a game-changing advancement powered by AI. The award went to three brilliant scientists who have reshaped our understanding of proteins with groundbreaking discoveries. David Baker, from the University of Washington, created entirely new kinds of proteins, opening doors to remarkable possibilities. Meanwhile, Demis Hassabis and John Jumper from DeepMind (part of Alphabet) developed AlphaFold, an AI model that can predict protein structures with astonishing accuracy. AlphaFold’s neural networks analyze vast data sets, unlocking secrets of protein structures that once took years to uncover—now in just minutes! The model has already predicted more than 200 million protein structures across humans, plants, bacteria, and more. This is a powerful example of how AI is driving human progress today. 💡 #NobelPrize #AI #AlphaFold #Science #Innovation #ProteinResearch #DeepMind #Chemistry #Breakthroughs #AIinScience https://lnkd.in/dZWKcV8a

For the first time, and probably not the last, a scientific breakthrough enabled by AI has been recognized with a Nobel Prize. AI is pushing the boundaries of science! This year's Nobel Prize in Chemistry highlights how AI can now predict the complex structure of proteins, something that’s been a major challenge for years! #NobelPrize #AI #Innovation #Science https://lnkd.in/eny4NCRb

🧬 How has AI transformed the world of protein science? Three years ago, Google’s AlphaFold2 transformed molecular research with over 90% accuracy in predicting protein structures. Learn how this breakthrough is shaping the future of medicine and biological exploration below. #AI #ProteinScience #AlphaFold

DeepMind's AI program AlphaFold3 can predict the structure of every protein in the universe — and show how they function https://lnkd.in/gwRc2Eui

AlphaFold's revolutionary AI is transforming health research by predicting protein structures in minutes, paving the way for new treatments and breakthroughs. 🧬 Faster Protein Analysis: AlphaFold predicts the 3D structures of proteins in mere minutes, saving researchers months or years of work. 🥼 Nobel-Winning Achievement: This AI, co-developed by Google DeepMind, earned the 2024 Nobel Prize in Chemistry for unlocking the secrets of molecular biology. 💊 Revolutionizing Drug Discovery: By understanding protein dynamics, AlphaFold accelerates the development of new medicines and vaccines. 🔬 Expanding Boundaries: The latest version, AlphaFold3, now predicts more complex molecular structures, including DNA, driving scientific research forward. ⚙️ Bridging Lab and AI: Combining AI predictions with experimental data allows scientists to validate and refine their research in groundbreaking ways. #AI #NobelPrize #HealthTech 🧪 Cutting-edge tools like AlphaFold can drastically reduce time spent in protein analysis, opening new doors for health research. 🎮 Researchers find AlphaFold’s addictive precision a game-changer in predicting complex molecular interactions. ♻️ Repost if you enjoyed this post and follow me, César Beltrán Miralles, for more curated content about generative AI! https://lnkd.in/g8HDVA8k

Artificial Intelligence (AI) profoundly transforms science and research by enabling faster, more accurate analysis of complex data sets. AI techniques like machine learning and deep learning are applied across various fields, from genomics and neuroscience to climate science and physics, to uncover patterns and insights beyond human capability. AI accelerates hypothesis generation, enhances predictive modeling, and facilitates novel discoveries. Moreover, AI-driven tools optimize research methodologies, leading to more efficient experimental designs and data collection and analysis automation. This revolution fosters a new era of innovation and knowledge expansion in scientific research. We will discuss this with Dr. Kamil Filipek in this week's ATHENA Talks of the Athena European University on May 17th, 2024, at 1200 CET. Registration: https://lnkd.in/dytmNgz7

𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐢𝐬 𝐦𝐚𝐤𝐢𝐧𝐠 𝐡𝐞𝐚𝐝𝐥𝐢𝐧𝐞𝐬 𝐭𝐨𝐝𝐚𝐲! 🏆 🔬 The 2024 The Nobel Prize in Physics has been awarded to John Hopfield and Geoffrey Hinton "for foundational discoveries and inventions that enable machine learning with artificial neural networks”. 💡 Their work paved the way for the AI systems we develop in Rulex, including our proprietary 𝐞𝐗𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐀𝐈 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦, the Logic Learning Machine. To read the full news: Press release: The Nobel Prize in Physics 2024 - NobelPrize.org To learn more about Rulex’s AI: https://lnkd.in/dgtcpn4f #AI #MachineLearning #NobelPrize

🚀 𝗔𝗜'𝘀 𝗣𝗼𝘄𝗲𝗿 𝗶𝗻 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗕𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝘀: 𝗧𝗵𝗲 𝗡𝗼𝗯𝗲𝗹 𝗣𝗿𝗶𝘇𝗲 𝗠𝗼𝗺𝗲𝗻𝘁 🏅 For the first time, AI has helped secure a Nobel Prize! 🎉 The 2024 Chemistry Nobel was awarded to the pioneers behind AlphaFold, the AI tool revolutionizing protein structure prediction. This is just the beginning of AI's transformative impact on science, unlocking discoveries that were once thought impossible. 🌐 Are you ready to embrace the future of AI-driven innovation? Let's explore how AI can reshape your industry! 👉 https://lnkd.in/gEptxSXz

Serokell is on its way to develop matrix representation of Elsevier biology #knowledgegraph for AI projects. This article describes their approach using PyTorch library for graph embedding and #drugrepurposing, which is a case of more general link prediction algorithm. #drugrepurposing relies on several types of relationships in the #knowledgegraph. Abstract regulatory relations can be also predicted by link prediction, while physical interactions can be predicted using protein sequence similarities, which can be added to #knowledgegraph as new relation type (paralogs). We hope #GNN will synergistically combine predictions of physical and regulatory interactions to improve #drugrepurposing.

Artificial Intelligence (AI) profoundly transforms science and research by enabling faster, more accurate analysis of complex data sets. AI techniques like machine learning and deep learning are applied across various fields, from genomics and neuroscience to climate science and physics, to uncover patterns and insights beyond human capability. AI accelerates hypothesis generation, enhances predictive modeling, and facilitates novel discoveries. Moreover, AI-driven tools optimize research methodologies, leading to more efficient experimental designs, data collection, and analysis automation. This revolution fosters a new era of innovation and knowledge expansion in scientific research. We will discuss this with Dr. Kamil Filipek in this week's ATHENA Talks of the ATHENA European University, held on May 17th, 2024, at 1200 CET. Registration: https://lnkd.in/d3YF8Rcn