AI in Molecular Prediction

For 50 years, a key protein behind heart disease, among the leading cause of death worldwide remained a scientific mystery. It was too large and complex for traditional methods; its structure was invisible to us. Now, researchers have combined cryo-electron microscopy with DeepMind's AlphaFold to reveal the atomic structure of that protein: apoB100, the very scaffold of "bad cholesterol." This marks a deeper shift in how we approach science.  When we can see biology at this level of detail, healthcare moves from managing symptoms to engineering interventions at the molecular root. AI starts to function as a new kind of microscope, one that reveals the invisible machinery of life and allows entirely new questions to be asked. This is the kind of progress that matters.     AI as an instrument for understanding, precision, and prevention. It’s a glimpse into a future where compute and science converge to tackle humanity’s hardest health challenges at their source.    Read the full story: https://lnkd.in/gbum2dKu #AIInHealthCare #AIForGood

I’m often asked where I see AI make a tangible, real impact in the world today. To that, I answer with #AlphaFold, the revolutionary AI model from Google DeepMind, that is able to predict the structure of a protein simply from its amino acid sequence. 5 years ago, AlphaFold solved the 50-year grand challenge of protein folding, followed by the equally meaningful decision to make 200 million protein structures freely available to the scientific community. Since then, Demis Hassabis and John Jumper have been recognized with a Nobel Prize for their work on AlphaFold, and we see over 3.3 million users of it globally, with more than a third of users right here in Asia-Pacific. Here is just a snapshot of those applications: 🔬 Dr. Su Datt Lam at the National University of Malaysia (UKM) is learning more about Melioidosis to better fight the silent killer. 🧬 Researchers Lim Jackwee lim and Yinxia Chao at Singapore’s A*STAR - Agency for Science, Technology and Research and National Neuroscience Institute (NNI) are visualizing proteins linked to Parkinson’s. 🔍 Professor Ji-Joon Song’s team at the Korea Advanced Institute of Science and Technology lead to cancer and other diseases. 🪢Dr. Danny Hsu at Academia Sinica, Taiwan is advancing our understanding of exceptionally complex protein “knots”. ♨️ Dr. Syun-ichi Urayama’s team is uncovering new evolutionary insights from microbes in Japan’s hot springs! Listen to one of their stories below, and read more about all of them here: https://lnkd.in/d7wyACpK #GoogleDeepMind #AIforGood 

AI Tool from Mayo Clinic Identifies 9 Types of Dementia with One Scan, Boosting Speed and Accuracy of Diagnosis: 🧠Mayo Clinic has developed an AI tool called StateViewer that can identify nine types of dementia, including Alzheimer’s, from a single FDG-PET scan 🧠 In testing, StateViewer correctly identified the dementia type in 88% of cases and helped clinicians analyze scans twice as fast, with up to 3x greater accuracy than standard workflows 🧠 The AI was trained on over 3,600 scans from both patients with dementia and people without cognitive issues, allowing it to detect subtle brain activity patterns linked to specific dementia types 🧠 The tool compares how the brain uses glucose for energy against a large database of confirmed diagnoses, pinpointing activity patterns tied to memory, attention, movement, language, and behavior 🧠 Color-coded brain maps help explain the AI’s interpretation to all clinicians, including non-specialists, potentially expanding diagnostic access beyond top neurology centers 🧠 Accurate early diagnosis is essential as new treatments emerge, especially when multiple brain conditions overlap and symptoms are complex or misleading #digitalhealth #ai

Five years ago, AlphaFold solved the protein structure prediction problem at CASP14, cracking a 50-year grand challenge in biology. It has been an absolute honour and privilege to have been part of this journey alongside Demis and John. Over 3 million researchers across 190 countries have since used AlphaFold to predict the structure of more than 200 million proteins. The impact spans from revealing apoB100's structure, advancing heart disease research, to supporting endangered honeybee conservation in Europe. Protein structure prediction was the root node problem in structural biology. By solving it, we opened up entirely new avenues for discovery. What AlphaFold demonstrated is that AI can accelerate scientific progress when applied to the right foundational challenges. We've since expanded this approach across biology. AlphaMissense and AlphaGenome are helping researchers understand genetic mutations and disease. AlphaProteo is designing new protein binders for targets in cancer and diabetes. We're applying similar thinking to challenges in fusion energy, materials discovery and climate science. Today, we're sharing The Thinking Game, following our team through the journey that made AlphaFold possible. To understand more about AlphaFold's impact, see the blog here: https://lnkd.in/eiPSAeKc #AlphaFold #AIforScience

The promise of large language models is to allow patients and physicians to interact with AI through human-like discussions, text. The promise of machine learning models is to elevate how we deal with repetitive, data-based medical tasks. But what if we combine the two? Authors of a new study developed a Digital Twin—GPT (a sort of LLM) to extend LLM-based forecasting solutions to clinical trajectory prediction. "Benchmarking on non-small cell lung cancer, intensive care unit, and Alzheimer’s disease datasets, DT-GPT outperformed state-of-the-art machine learning models, reducing the scaled mean absolute error by 3.4%, 1.3% and 1.8%, respectively." Essentially, it creates virtual patient “digital twins” from electronic health records to forecast disease progression and treatment outcomes in real time. Source: https://lnkd.in/e2tuu8A5

AI and Protein Localization! 🧬🔬 The journey from understanding protein structures to predicting their precise locations within cells has taken a monumental leap forward. Introducing ProtGPS, a cutting-edge machine-learning model developed by researchers at the Whitehead Institute and Massachusetts Institute of Technology's CSAIL, led by Professor Richard Young and his team. Why is this a game-changer? 🤔 🔹 Predictive power: ProtGPS accurately forecasts where proteins will localize in cells, crucial for understanding both their functions and the mechanisms of diseases. 🔹 Disease insight: By examining over 200,000 proteins with disease-associated mutations, ProtGPS uncovers profound links between mis-localization and disease, paving the way for novel therapeutic strategies. 🔹 Generative potential: Beyond predictions, ProtGPS creates new proteins, designing sequences to target specific cellular locales. This innovation could revolutionize drug design by enhancing precision and minimizing side effects. 🔹 Experimental validation: Unlike many AI models, ProtGPS's predictions have been validated in real cell experiments, bridging the gap between computational design and biological application. The potential applications? Endless.....From developing targeted therapies to uncovering fundamental cellular mechanisms, the implications of this research are vast. ProtGPS isn't just a tool; it’s the start of a new era in biological exploration and therapeutic innovation. #AI #MachineLearning #Biotechnology #Proteomics #ResearchInnovation #Therapeutics #MIT #WhiteheadInstitute

This AI model can predict your risk of Alzheimer’s just by scanning your eyes. Research led by Prof. Ruogu Fang and her team at the University of Florida suggests this could help turn a routine eye exam into an early screening tool for brain health. Here's how it works: ▶️ AI looks for patterns invisible to the human eye Using more than 40,000 retinal photographs, researchers trained the AI to analyse blood vessels, the optic nerve and other retinal structures. It then links those patterns to biological and lifestyle-related risk factors associated with Alzheimer's disease. ▶️ It uses a test millions of people already get Retinal photographs are already part of routine care for people with diabetes, glaucoma, and cataracts. And they are: - Inexpensive - Non-invasive - Widely available So no specialised scanning technology is needed if this approach is validated for clinical use. Alzheimer's disease develops over decades, but diagnosis often comes after significant brain damage has already occurred. Researchers believe identifying high-risk patients earlier could enable lifestyle changes, further testing or preventive therapies before symptoms begin. The technology still requires further clinical validation. But if future studies confirm these findings, a simple eye photograph could become a practical way to identify people who may benefit from earlier intervention. Do you think routine eye exams could eventually become part of Alzheimer's screening? #Entrepreneurship #healthtech #innovation

AI just ran its own multidisciplinary tumor board. And nailed the diagnosis + treatment. This was a full-stack oncology reasoning engine—pulling from imaging, pathology, genomics, guidelines, and literature in real time. A new paper in Nature Cancer describes how researchers built a GPT-4-powered multitool agent that: • Interprets CT & MRI scans with MedSAM • Identifies KRAS, BRAF, MSI status from histology • Calculates tumor growth over time • Searches PubMed + OncoKB • And synthesizes everything into a cited, evidence-based treatment plan In short: it acts like a multidisciplinary team. Results : • Accuracy jumped from 30% (GPT-4 alone) to 87% • Correct treatment plans in 91% of complex cases • Every conclusion backed by a verifiable citation This is bigger than oncology. Any field that relies on multi-modal data and cross-domain reasoning—like my field of GI ( GI + Mental Health+ Nutrition + Excercise ) could benefit from this collaborative AI architecture. Despite the visual, it doesn’t replace the human team—it augments it. Providers still decide. But now, they do it faster, with more context, and less cognitive fatigue. #AI #HealthcareonLinkedin #Healthcare #Cancer

Exciting progress in AI x Biology you should know about: EvolutionaryScale's ESM3, a new language model, simulates 500 million years of protein evolution. As someone working in AI x Biology, I dove into this right away. (1) This model, trained on an extensive dataset, can generate diverse protein sequences, structures, and functions. ESM3 has already demonstrated its capability by creating esmGFP, a green fluorescent protein analogous to evolving over half a billion years. This achievement underscores ESM3’s potential to revolutionize programmable biology and protein design. (2) ESM3 integrates multimodal reasoning, allowing precise control over protein creation. This opens doors to significant advancements in medicine, biological research, and sustainable energy solutions. The model’s ability to reason across different modalities sets a new benchmark for AI in scientific research. (3) Moreover, EvolutionaryScale’s commitment to open science ensures that ESM3’s models and data are accessible, fostering collaboration and responsible AI development. This transparency is vital for accelerating scientific discoveries and practical applications. (A) I find the quality of ESM3’s work impressive, showcasing a sophisticated understanding of protein biochemistry. Its capacity to generate high-quality, functional proteins far removed from existing variants illustrates its transformative potential. Future research could explore ESM3’s application in developing specific therapeutic proteins or industrial biocatalysts, paving the way for innovative solutions across various fields. (B) However, again with powerful generative AI models, a key area for improvement is optimizing the model’s efficiency to balance complexity and performance, making it more accessible for broader scientific and industrial use. I believe ESM3 stands as a testament to the power of AI in advancing biological research and technology. Its implications are far-reaching, promising a future of accelerated scientific breakthroughs and innovative applications. Paper: https://lnkd.in/gvv8FKPA Blog Post: https://lnkd.in/gQRQKExG #GenAI #Biology #ArtificialIntelligence

Delighted to share new Arc Institute work from our group on AI-accelerated lab-in-the-loop, in Science Magazine today. One of the most remarkable things about biology is that it's digital. DNA, RNA, proteins: these are all sequences, and their function is directly encoded in their sequence of letters. But a protein of length N has 20^N possible variants and the vast majority are non-functional. Evolution spent billions of years finding the functional needles in this haystack through random exploration and natural selection. For modern biomedicine, we need to solve this in days to weeks. The process of scientific research is fundamentally a search problem, and we basically do guess and check. We've trained predictive models of biology, like our Evo series of DNA language models, to learn the evolutionary constraints on biological sequences. Such models learn a fitness landscape of what evolution has explored. But the fitness landscape is not the same as the function you actually care about: whether an enzyme catalyzes faster, whether an antibody binds tighter, whether a CRISPR tool edits better. The core question is how do you connect the knowledge of these models to the functional search that has to happen in the physical lab? MULTI-evolve is one of our first answers. It's a full-stack, AI-lab-in-the-loop framework that "jumps" directly to hyperactive multi-mutant proteins via ML-guided evolution. We combine an ensemble of protein language models pretrained on all proteins across evolution to discover beneficial mutations, then systematically measure pairwise combinations to learn the epistatic landscape (e.g. which mutations are synergistic vs. antagonistic), and extrapolate to predict powerful higher-order combinations of 5-7+ mutations. We also built MULTI-assembly, a molecular biology method to physically construct these complex multi-mutants cheaply and quickly, regardless of protein length (previously a major bottleneck). We applied this to three very different proteins: APEX (a proximity labeling enzyme), CRISPR-Cas13d (for RNA trans-splicing), and a therapeutic anti-CD122 antibody, achieving up to 256-fold improvement. For CD122, we simultaneously optimized both binding affinity and antibody expression, navigating real trade-offs between competing developability objectives. We built Arc Institute to be a full-stack biology and AI research organization. We've been training frontier AI models for biology, like Evo, Evo 2, State, Stack, etc. But the whole point of having frontier AI capabilities and experimental biologists under a single physical roof is to close the loop between computation and the wet lab. MULTI-evolve is one of Arc's first examples of AI lab-in-the-loop, and there will be much more to come. This work was a wonderful collaboration with Silvana Konermann and Brian Hie, and led by the remarkably driven and persistent Vincent Tran with Matthew Nemeth, Liam Bartie, Sita C., Alison Fanton, and Chad Moon.