HTANALYSTS’ Post

A new machine learning model has uncovered an important checkpoint in the process of muscle cell development. The model showed that when an enzyme's activity was inhibited, the muscle cells got "stuck" in a ready-to-fuse stage, unable to complete the fusion process that forms new muscle fibers. This discovery not only advances our understanding of muscle biology, but also demonstrates the power of AI and computational models to uncover critical insights in complex biological systems. The researchers believe this approach could be applied to study other dynamic biological processes. Interestingly, this discovery required collaboration between researchers at the Weizmann Institute of Science, and other scientific teams. Working together across disciplines was essential to fully understand this important biological checkpoint. https://lnkd.in/ePZgp5UQ #AI #Biology #MuscleResearch #TeamScience

🔬Quantum Computing in Medicine: Revolutionizing Healthcare🏥 Quantum computing is not just a technological marvel; it's poised to transform the medical field in profound ways. From drug discovery to personalized medicine, the potential applications are vast and revolutionary. Here’s how quantum computing is set to make an impact: 1. Accelerating Drug Discovery: Traditional drug discovery processes are time-consuming and expensive, often taking years to develop new treatments. Quantum computers can simulate molecular interactions at an unprecedented scale and speed, drastically reducing the time needed to identify and develop new drugs. 2. Personalized Medicine: Every individual’s genetic makeup is unique, and quantum computing can analyze vast amounts of genetic data quickly and accurately. This enables the development of personalized treatment plans tailored to an individual’s specific genetic profile, improving the effectiveness of therapies. 3. Optimizing Clinical Trials: Quantum algorithms can optimize the design and analysis of clinical trials by efficiently managing variables and analyzing complex datasets. This leads to more effective trial designs, quicker patient recruitment, and faster results, accelerating the overall drug development process. 4. Enhanced Medical Imaging: Quantum computing can improve the processing and analysis of medical images, such as MRIs and CT scans. By enhancing image resolution and reducing noise, quantum algorithms can provide clearer and more accurate diagnostics, leading to better patient outcomes. 5. Genomic Research: Quantum computers can handle the enormous datasets involved in genomic research, identifying patterns and correlations that are difficult for classical computers to detect. This can lead to breakthroughs in understanding genetic diseases and developing targeted gene therapies. 6. Protein Folding: Understanding how proteins fold is crucial for many biological processes and disease mechanisms. Quantum computing can model protein folding more accurately than classical methods, aiding in the development of new treatments for diseases like Alzheimer’s and Parkinson’s. 7. Epidemiology and Public Health: Quantum computing can analyze complex models of disease spread and interactions, helping public health officials make more accurate predictions and better prepare for and respond to epidemics and pandemics. 8. Complex System Simulations: Quantum computers can simulate biological systems in ways that classical computers cannot, providing deeper insights into complex biological processes and systems. This can lead to new discoveries in cell biology, neuroscience, and other fields. The integration of quantum computing in medicine holds the promise. As we continue to explore and harness the power of quantum technology, the future of medicine looks brighter than ever. 🌟🔗 #QuantumComputing #MedicalInnovation #HealthcareRevolution #FutureOfMedicine #TechInHealthcare #QuantumLeap

Why do we use imaging data to understand biology? In a new video, Imran Haque, SVP of AI and Digital Sciences, walks us through the treasure trove of data found in images, explaining how we use algorithms to structure pixels, how we are leading discovery in the field, and the massive advances that have been made in training foundation models to make new predictions of promising therapeutic targets. 🔹 Some highlights: ◾ Imaging and algorithms can provide as much or more information than RNA sequencing and cost far less money, so you can run more experiments – and you don’t have to kill the cells to do it.  ◾ With ML foundation models acting as a lever, we only need a small amount of data to perform as well as a much larger set. With areas as vast as chemistry and biology, you want to build as much as you can to break these scale barriers.  ◾ The models – called masked autoencoders – can reconstruct some 75-90% of the images of cells that have been masked. ◾ Once trained, we use these models to turn an unmasked image into a list of numbers and then match these to the database of images of all the cells we’ve taken in the past to see how similar or different the biology is that is created by different conditions (or perturbations) for those cells..  ◾ This technique – called similarity comparison – is central to our digital maps of biology. It’s how we discovered RBM39 provided a new means to inhibit CDK12, a promising therapeutic target for cancer. Full video here: https://lnkd.in/ebdHGfZG #ai #ml #tech #techbio #chemistry #biology #models

When Schrödinger launched more than 30 years ago, computational biology was a nascent technology. Schrödinger, a pioneer in the field, relied heavily on the Protein Data Bank (PDB), a collection of solved protein structures that got its start in the 1970s. “Getting a structure in the lab used to be—and still is—difficult,” says Karen Akinsanya, Schrödinger’s president of therapeutics R&D. Back then, a single protein structure would potentially comprise a person’s entire PhD or postdoctoral research, taking “years of work,” she says. The entire field of structure-based drug design took a giant leap forward with the advent of #AlphaFold, the protein prediction software that launched in 2020 and this week became a Nobel Prize–winning technology. AlphaFold, which is owned by Google’s DeepMind, built on the PDB and other protein sequence databases, has used a neural network to predict the structures of now millions of proteins. Schrödinger and its ilk use AlphaFold to help dream up drug candidates with a higher degree of specificity than what was previously possible. Akinsanya says her team is using a better understanding of how proteins fold to design, for instance, small molecules that change how those proteins interact with each other. Recently, a Schrödinger team used structure predictions of a protein encoded by the human ether-à-go-go–related—or hERG—gene, to see how 14 compounds would bind with it, then design drugs that would avoid the protein since inhibiting it can elicit severe cardiotoxic side effects (Cell 2024, DOI: 10.1016/j.cell.2023.12.034). “It’s accelerating the work of humans. There’s no doubt about that,” Akinsanya says of AlphaFold. “We are in the century of the protein.” It’s also the century of the algorithm. More in C&EN: https://lnkd.in/eBXX_D9U

🌟 Exciting Advances at the Intersection of AI, Biology, and Chemistry 🌟 In a groundbreaking study by MIT researchers, the future of biology and chemistry is being reshaped through a novel computational technique that simplifies the engineering of proteins. This innovation has the potential to revolutionize fields from neuroscience to gene therapy, by enabling the rapid development of proteins with enhanced functions. As we stand on the brink of a new era where AI-driven discoveries promise to transform our understanding and interaction with the natural world, the importance of foundational education in these technologies cannot be overstated. At Camp Integem, we're committed to preparing the next generation to navigate and contribute to this exciting future. Our programs in AI, holographic AR, coding, robotics engineering, art, and design, offer K-12 students a unique opportunity to explore the frontiers of science and technology. The collaboration between computational scientists and biologists exemplifies the interdisciplinary approach necessary for the advancements ahead. It’s a thrilling time to be at the intersection of these fields, and an even more exciting time to inspire young minds to embark on this journey of discovery. #AI #Biology #Chemistry #Innovation #STEMEducation #CampIntegem #FutureLeaders https://lnkd.in/git-YwiP

How good are LLMs at science? Our Research team developed a benchmarking dataset to evaluate LLM capabilities on complex, nuanced questions in STEM subjects. Then we tested 5 LLMs: Qwen2-7B-Instruct, Llama-3-8B-Instruct, Mixtral-8x7B, Gemini-1.0-pro, and GPT-4o. How the benchmark helps reveal strengths and weaknesses of models: 🔹 Dataset of 180 challenging, open-ended questions (no multiple choice!) in 10 subdomains 🔹 Collaboration with researchers and domain experts in fields like high-energy physics, immunology, and cell biology 🔹 Results vary across subdomains — surprisingly, Llama pulled ahead of GPT-4o in Bioinformatics What did we find? All the models fell short of evaluators’ expectations. Notably, the model responses appear correct, but they can be misleading to non-experts. Consumers should be extra cautious about using LLMs for complex questions. Check out our blog for insights into our research, the dataset, and the implications for the future of AI in science. 👉 https://bit.ly/40iQMVs Working on LLM eval in a specific domain? Let’s chat https://bit.ly/3YUM67F #AI #STEM #LLM #Benchmarking #LLAMA #GPT4o

"Noting that recent advances in artificial intelligence and the existence of large-scale experimental data about human biology have reached a critical mass, a team of researchers from Stanford University, Genentech, and the Chan-Zuckerberg Initiative says that science has an "unprecedented opportunity" to use artificial intelligence (AI) to create the world's first virtual human cell. Such a cell would be able to represent and simulate the precise behavior of human biomolecules, cells, and, eventually, tissues and organs. "Modeling human cells can be considered the holy grail of biology," said Emma Lundberg, associate professor of bioengineering and of pathology in the schools of Engineering and Medicine at Stanford and a senior author of a new article in the journal Cell proposing a concerted, global effort to create the world's first AI virtual cell. "AI offers the ability to learn directly from data and to move beyond assumptions and hunches to discover the emergent properties of complex biological systems."" #ai #digitaltwin #virtualcell

🔬 From Fundamental Discoveries to AI-Driven Innovation: How Nobel-Winning Research Transformed Biophysics and Biochemistry 🌟 The evolution of biophysics and biochemistry has been remarkable, moving from groundbreaking discoveries to the cutting-edge application of Artificial Intelligence (AI)! This journey is exemplified by several Nobel Prizes that highlight the fusion of foundational concepts with AI-enhanced research. 1953: Hermann Staudinger won the Nobel Prize for macromolecular chemistry, laying the groundwork for understanding biomolecules like proteins and DNA. 🧬 1962: Francis Crick, James Watson, and Maurice Wilkins unveiled the structure of DNA, opening the door for AI in biomolecular studies. 🔍 1997: Paul D. Boyer, John E. Walker, and Jens C. Skou were honored for their work on ATP synthesis, paving the way for AI-enhanced modeling of molecular machines. ⚙️ AI's impact has accelerated further: 2017: Jacques Dubochet, Joachim Frank, and Richard Henderson revolutionized structural biology with cryo-electron microscopy, with AI improving data processing. ❄️ 2020: Emmanuelle Charpentier and Jennifer Doudna's CRISPR-Cas9 breakthrough has been optimized by AI for gene editing. ✂️ 2023: Katalin Karikó and Drew Weissman advanced mRNA technology, which AI now enhances for stability and efficacy. 💉 The 2024 Nobel Prizes further showcase AI's influence, with John J. Hopfield and Geoffrey E. Hinton recognized for their contributions to artificial neural networks—foundational to modern AI systems! 🧠 As we embrace an AI-driven era, scientists must focus on continuous learning, interdisciplinary collaboration, data literacy, and ethical AI use to lead the next wave of scientific breakthroughs! 🌊 #AI #Biophysics #Biochemistry #Innovation #Research #Science 🚀

Before our "Meetup #3: Frontiers of Biology - AlphaFold", we want to give you a quick teaser and explain the invention of AlphaFold. 📍 Register here: https://lu.ma/c89d9tjf AlphaFold is the revolutionary system initially developed by Google DeepMind. It is essentially an AI model that predicts the 3D structure of proteins from their amino acid sequences. Demis Hassabis and Dr. John Jumper were recently honored with the 2024 Nobel Prize in Chemistry for their work on AlphaFold. But what makes AlphaFold so transformative, and why is it a milestone in scientific discovery? ⚫ The Protein Puzzle Proteins are the workhorses of biology, performing countless functions that sustain life. Their ability to function is determined by their 3D structure, which until recently was notoriously difficult and time-intensive to predict. Researchers often relied on expensive experimental techniques like X-ray crystallography or cryo-electron microscopy. AlphaFold changed this narrative. By using deep learning, the system can accurately predict protein structures in minutes — a process that once took years. Its most advanced iteration, AlphaFold 3, has expanded its capabilities to include predictions for interactions involving DNA, RNA, and small molecules, unlocking new potential for understanding biological systems. 🟠 Accelerating Discovery Across Disciplines AlphaFold’s impact has been profound across multiple fields: Drug Discovery: By simulating molecular interactions, AlphaFold accelerates drug design and development. It enables researchers to identify potential drug candidates, predict side effects, and even personalize treatments based on individual genetic profiles. Protein Engineering: From designing novel proteins to optimizing enzymes for industrial applications, AlphaFold supports breakthroughs in synthetic biology and biomaterials. Understanding Diseases: AlphaFold has provided insights into disease mechanisms, including neurodegenerative conditions and COVID-19, offering a deeper understanding of potential therapeutic targets. 🟠 Global Accessibility AlphaFold’s predictions, made freely available through its Protein Structure Database, have already been used by more than 2 million researchers worldwide. This open access has democratized cutting-edge science, enabling discoveries that range from enzyme design to understanding genetic mutations AlphaFold probes how AI can revolutionise science, not only by solving long-standing problems but also by opening doors to new possibilities. How will tools like AlphaFold redefine the future of biology? Let’s discuss tomorrow at 6:30 in Yolk Workspace & Community! 📍 Register here to hear more from us and experts like Agata Szymanek and Jan Majta, PhD: https://lu.ma/c89d9tjf #AlphaFold #AI #ScientificInnovation #ProteinStructure #DrugDiscovery

This year’s Nobel prizes in physiology/medicine, physics, and chemistry were awarded for research in fields that shape the future of healthcare. Congratulations to: - The Nobel Prize winners in physiology/medicine: Victor Ambros and Gary Ruvkun for the discovery of microRNA which opened up a new dimension in gene regulation. The essential function of microRNA in protein production influences all processes in the human body and helps explain the development of cancer and diabetes. - the Nobel Prize winners in physics: John J. Hopfield and Geoffrey E. Hinton for their discoveries that enable machine learning with artificial neural networks. In the 1980s, they laid the foundation for Large Language Models (LLM) and AI-assisted medical image analysis. - the Nobel Prize winners in chemistry: David Baker for computational protein design that led to new drugs and vaccines; Demis Hassabis and John Jumper for developing the AI model called “AlphaFold2” to solve a 50-year-old problem and predict the structure of virtually all the 200 million proteins that researchers have identified. Their groundbreaking research could help explain antibiotics resistance and use enzymes to break down plastic.   Such discoveries enable quantum leaps in healthcare, like it was the case with mRNA vaccines during the Covid19 pandemic. Remarkably, AI was an integral part of this research. And I am convinced that AI will lead to further quantum leaps in healthcare, massively speeding up the development of vital medicines – and make them more accessible.   Just another reason why we will host a panel at this year’s World Health Summit to discuss how we can increase trust into new technologies and AI among patients and healthcare professionals. These three Nobel Prizes are leading perfectly into our discussion on Monday next week. Looking forward to it! #NobelPrize #CommittedToLife Photo: Clément Morin © Nobel Prize Outreach.