Kiran Prakash’s Post

The article highlights that DeepMind's CEO, Demis Hassabis, and Director, John Jumper, were awarded the 2024 Nobel Prize in Chemistry for developing AlphaFold. This AI system predicts the 3D structure of proteins, which has revolutionized computational biology and facilitated discoveries across numerous fields. Their work has provided a significant tool for researchers worldwide, enhancing scientific advancements in protein research, drug discovery, and more. You can read the full article here: https://lnkd.in/emyJAryp

🏆 Nobel Prize in Chemistry Awarded for AI in Protein Prediction The 2024 Nobel Prize in Chemistry was awarded to Demis Hassabis, John M. Jumper, and David Baker for their groundbreaking work in AI-driven protein structure prediction. This prestigious recognition highlights the transformative impact of DeepMind's AlphaFold, a tool capable of predicting the 3D structures of nearly all known proteins. This advancement is poised to revolutionize fields such as drug discovery, molecular biology, and bioengineering by providing unprecedented insights into protein folding and function. AlphaFold's ability to predict protein structures with remarkable accuracy addresses one of the most significant challenges in biology. Proteins are fundamental to virtually all biological processes, and understanding their structure is crucial for deciphering their function. This development not only accelerates scientific research but also opens new avenues for therapeutic interventions, potentially leading to breakthroughs in treating diseases that were previously difficult to target. The Nobel Committee's decision underscores the growing importance of AI in scientific research and its potential to solve complex problems that were once considered insurmountable. As AI continues to evolve, its applications in various scientific domains will likely expand, offering innovative solutions to global challenges. This recognition serves as a testament to the power of interdisciplinary collaboration and the role of AI as a catalyst for scientific progress. Source The AI Track: https://lnkd.in/edYq4iQF #ai #deepmind #chemistry #sciencediscovery

Did you hear? We are witnessing a monumental shift in scientific research! The 2024 Nobel Prize in Chemistry has been awarded for pioneering work in AI-driven protein structure prediction. Google DeepMind's AlphaFold has achieved the remarkable feat of predicting the 3D structures of almost all known proteins from their sequences within minutes—an endeavor that previously took decades. This breakthrough not only addresses a longstanding challenge in biochemistry but also has profound implications for drug discovery, materials science, and sustainability efforts. As we celebrate this achievement, we look forward to the innovations that will emerge from this transformative technology. How do you see AI shaping the future of scientific research? #NobelPrize2024 #AIinScience #ProteinFolding #Innovation

🔬 AlphaFold2: A Nobel-Worthy Game Changer in Biology? 🏆 The Nobel Prize committee is expected to consider #AlphaFold2 for the 2024 prize, and for good reason. This #AImodel, developed by Google DeepMind has reshaped protein folding prediction, delivering a breakthrough that biologists, chemists, and drug developers have pursued for decades. Here’s why AlphaFold2 deserves Nobel consideration and what it means for science: 1️⃣ Predicting Protein Structures with Unprecedented Accuracy: AlphaFold2 has predicted the 3D structures of over 200 million proteins, with an accuracy rivaling experimental methods. For years, deciphering a protein’s structure was an intricate, time-consuming task. Now, AlphaFold2 can model these structures in mere minutes, a feat that could enable faster drug development and more targeted therapies. 2️⃣ Transformative Impact on Research and Medicine: With AlphaFold2’s freely available database, scientists worldwide now have access to structural predictions for nearly every known protein. This development has already accelerated research in disease mechanisms, antibiotic resistance, and genetic disorders. The implications for personalized medicine are profound, potentially allowing for treatments tailored to a patient’s unique protein structures. 3️⃣ Cross-Disciplinary Collaboration: AlphaFold2’s success demonstrates the power of AI and biology converging. It opens the door for more collaborative efforts between AI researchers, molecular biologists, and medical professionals, potentially sparking further innovation in protein engineering, biomaterials, and even synthetic biology. But is this enough for a Nobel? Critics argue that AlphaFold2, as software, may not meet the traditional criteria set by the Nobel committee. And yet, the model has achieved what many once considered impossible, posing a compelling case for expanding our understanding of Nobel-worthy achievements. What are your thoughts? Should AlphaFold2 claim the #NobelPrize in #Chemistry or #Medicine, or should AI-driven achievements remain outside traditional honors? #AlphaFold2 #DeepMind #Biotechnology #AIinScience #ProteinFolding #DrugDiscovery #InnovationInMedicine #FutureOfScience

Did you hear? We are witnessing a monumental shift in scientific research! The 2024 Nobel Prize in Chemistry has been awarded for pioneering work in AI-driven protein structure prediction. Google DeepMind's AlphaFold has achieved the remarkable feat of predicting the 3D structures of almost all known proteins from their sequences within minutes—an endeavor that previously took decades. This breakthrough not only addresses a longstanding challenge in biochemistry but also has profound implications for drug discovery, materials science, and sustainability efforts. As we celebrate this achievement, we look forward to the innovations that will emerge from this transformative technology. How do you see AI shaping the future of scientific research? #NobelPrize2024 #AIinScience #ProteinFolding #Innovation

AI is getting real !

Incredible to witness how AI in the form of AlphaFold2 has predicted protein structures. It's a reminder of the power of interdisciplinary collaboration and what can be achieved when AI meets biology. The future is here, and it’s powered by AI!

In a year marked by tectonic shifts in the worlds of science and technology, it’s no surprise that the The Nobel Prize committees have leaned into the #AI wave. On one hand, you’ve got the brains behind AlphaFold2, bagging the Nobel Prize in Chemistry for cracking open one of the most vexing problems in biology: how proteins fold. And on the other, Geoffrey Hinton, the godfather of deep learning, walking away with the Nobel Prize in Physics for his pioneering work that laid the foundation for today’s AI explosion. What’s fascinating is how these two achievements reveal the different faces of AI—and what they tell us about where the future is headed. AlphaFold2 is like a sniper, laser-focused on solving a specific, life-altering problem: predicting the 3D structure of proteins from amino acid sequences. For decades, this puzzle left biologists banging their heads against the wall. But AlphaFold, armed with a deep neural network designed explicitly for this task, crushed it. It didn’t just shuffle through possibilities—it found a way to “see” the relationships between amino acids in a way humans simply couldn’t. It’s precise, verifiable, and could reshape everything from drug discovery to personalized medicine. Contrast that with Geoffrey Hinton’s LLMs, which are more like polymaths—jack-of-all-trades models that churn through mountains of data to spit out coherent language, whether it’s a medical paper, an op-ed, or a love letter. These models don’t care about the specifics of protein folding. They care about the statistical patterns in human language. And while they may seem a bit more mystical in their operations—often leaving us scratching our heads as to how they arrived at an answer—they are the engines behind everything from ChatGPT to automated customer service. And here’s the kicker: While AlphaFold’s models are computationally demanding, the sheer scale of LLMs is mind-boggling. The training for GPT-4? It makes AlphaFold look like a warm-up lap. The computational needs of these systems are insane, which is why OpenAI, Google, and others are throwing entire data centers at them. So what’s the big takeaway? It’s the fundamental difference in how we apply AI today. #AlphaFold is narrow, specific, and explainable. It’s a tool for the experts. You feed it a sequence, it gives you a structure—biologists can check that against the real world. But LLMs? They’re the wild cards, generating answers from patterns buried in text. They might give you the right answer, or they might hallucinate a totally wrong one. You can’t cross-check them in the same way. It’s an entirely different beast. Today’s news isn’t just a celebration of AI’s achievements—it’s a sign of how deeply it’s infiltrating every part of our lives, from the molecules that make us human to the language we use to describe them. And if you think this is the peak? Buckle up, because we’re just getting started. —Raghu Gullapalli, Action is the Antidote

I actualy loved the decision to give half the Chemistry Nobel to the two individuals who led AlphaFold. I followed some of the computational efforts to predict 3D structures of proteins prior to AlphaFold. Those used first principles from Physics and some hints from known protein structures. The accuracy was still at a level with much to be desired. AlphaFold brought the accuracy to basically the same level as one would achieve by direct experiments. A fantastic accomplishment. Was this worth a Nobel Prize? I guess the answer is easily an “Yes” if one considers Arno Penzias’ words on his Physics Nobel Prize (for the discovery of cosmic microwave background radiation). In an interview, Penzias stated that he was never very comfortable with his Physics Nobel. He felt he was not in the same league of Einstein’s or Planck’s. But then he realized the Nobel prize is *not* given to the best scientist. It’s given to the *best experiment*. The experiment that most advanced our understanding of the natural world. The one that brought the most benefit to humankind. That allowed him (Penzias) make his peace with his Nobel prize, he said. Well, in this sense, showing that we can use AI to predict the 3D structures of proteins with comparable accuracy of direct experimental determination was certainly among the best experiments and clearly brought great benefit to humankind. The Nobel prize was thus well deserved here indeed.

At first, I don't get what makes this year's chemistry nobel prize special. I understand the why after reading the story. Hassabis and Jumper developed an AI model called Alphafold2 that can predict protein structure from its amino acid sequence. Meanwhile, Baker developed a software called Rosetta that can predict amino acid sequence from the protein structure. So it's a two-way prediction. Another fact is not less interesting: none of them are chemists. They are basically software developers 😂 Hassabis: former chess player and game developer Jumper: theoretical physicist Baker: started his study in philosophy but ended up developing software for biological research purposes