Isomorphic Labs’ Post

From BRIQUE's Desk!!! Google has revealed a pioneering AI-driven technology aimed at forecasting the behavior of human molecules, marking a significant milestone in the realm of artificial intelligence and molecular science. Leveraging advanced machine learning algorithms, the innovation enables researchers to predict the interactions between various molecules with unparalleled precision and efficiency. The technology, termed "AtomAI," is a testament to Google's commitment to advancing the frontiers of AI and its application in diverse domains beyond traditional computational tasks. By harnessing the power of AI, AtomAI revolutionizes the field of molecular dynamics, offering scientists and researchers invaluable insights into the behavior and properties of complex molecular systems. One of the key features of AtomAI is its ability to simulate the behavior of molecules in real-world scenarios, providing a virtual laboratory for studying molecular interactions and phenomena. This capability holds immense potential for accelerating drug discovery, materials science, and other critical areas where understanding molecular behavior is paramount. Moreover, AtomAI's predictive capabilities extend beyond mere simulations, enabling researchers to identify novel molecular structures and properties that may have previously eluded detection. This opens up new avenues for innovation and discovery in fields ranging from pharmaceuticals to renewable energy. Google's unveiling of AtomAI underscores the transformative impact of AI on scientific research and underscores the company's ongoing efforts to push the boundaries of what is possible with artificial intelligence. As AtomAI continues to evolve and mature, it is poised to catalyze groundbreaking discoveries and drive innovation across a wide range of industries. #GoogleAI #MolecularScience #Innovation #ArtificialIntelligence #BRIQUEsDesk Source: https://lnkd.in/gSG24xek

🔍 Introducing Our COVID-19 Detection System Project! 🔍 Thrilled to share that our team — myself, Pranshu Verma, Mandeep singh Oberoi, and Raghav Sharma — is working on a machine learning model to detect COVID-19 from chest X-ray images. By leveraging Convolutional Neural Networks (CNNs), our goal is to support healthcare professionals with rapid and accurate preliminary diagnoses. 💻✨ Objective: To create an automated detection system that reduces diagnostic time and aids medical staff, especially in areas with limited resources. Stay tuned for updates as we dive deeper into our model’s architecture and results! 📊🔍 #COVID19 #MachineLearning #DeepLearning #HealthTech #ArtificialIntelligence #HealthcareInnovation #XRayImaging #COVIDDetection #TechForGood

Google DeepMind's #AlphaFold3 launched yesterday, and I have never seen so much coverage for a LifeSciences paper! And it's not only on LinkedIn or within the scientific community ... Alphafold is in the mainstream media! when is the last time this happened for a scientific discovery (#CRISPR? ... and then it got The Nobel Prize 😎 ). Here are just a few links to the media coverage: 📌 Financial Times "Google DeepMind unveils AI model for living cells" - https://lnkd.in/e7-Bmt9W 📌 Fast Company "Google DeepMind's AlphaFold 3 could speed up drug discovery for diseases" - https://lnkd.in/euuSDw8R 📌 DER SPIEGEL "Einblick in den Maschinenraim des Lebens" [ Insight into the engine room of life]- https://lnkd.in/eGiSCfqA 📌 TIME "Google DeepMind’s Latest AI Model Is Poised to Revolutionize Drug Discovery" - https://lnkd.in/e8MGTmkw 📌 MIT Technology Review "Google DeepMind’s new AlphaFold can model a much larger slice of biological life" - https://lnkd.in/e2HqMKac 📌 Fortune "Google DeepMind and Isomorphic Labs reveal AI able to predict large swathes of molecular biology" - https://lnkd.in/eVCjHn_y 📌 TechCrunch "Google DeepMind debuts huge AlphaFold update and free proteomics-as-a-service web app" - https://lnkd.in/eKNsqy2x Congratulations to everyone who made this possible, it's truly impressive and definitely a landmark not only in AI applied to life sciences but for sciences in general - Joshua Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Isomorphic Labs, Demis Hassabis

As an AI engineering student, I’m fascinated by the insights of AI leaders like David Silver and Regina Barzilay. Their groundbreaking work showcases how AI is transforming industries: 🚀 David Silver (Google DeepMind) explains reinforcement learning, where AI learns from experience, just like us! From mastering complex games like Go to helping robots perform sophisticated tasks, the possibilities are endless. 🧬 Regina Barzilay (MIT) shared how AI is revolutionizing healthcare by detecting early-stage breast cancer, something even doctors might miss. Her team also discovered a new antibiotic using AI—marking the first breakthrough in 30 years. AI is not just about automation. It's about creativity, intuition, and solving problems we thought were impossible. Listening to these pioneers makes me more excited about the future of AI and my role in it! 🔗 How do you see AI shaping the future? #ArtificialIntelligence #TechRevolution #AIInnovation #MachineLearning #AIHealth #DeepLearning #FutureOfAI #BBC #HCC #HoustonCommunityCollege #Google #HealthcareinAl

Microsoft Research President Peter Lee leads the way in merging technology and healthcare. His team's innovative use of generative AI is transforming medical imaging, genomics, and clinical notetaking. Explore how these breakthroughs are shaping the healthcare landscape! #MSFTadvocate #healthcare #innovation #ai

"Many large language models in life science have now moved to a hyper-accelerated phase." – Eric Topol Recently, Stanford researchers introduced a "Virtual Lab"* of five domain-specific AI agents (see image), tasked them with designing nanobodies against SARS-CoV-2, and in record time, they delivered two potential candidates. It makes me wonder... What use cases in care delivery (not life science) could benefit from a Mixture of Experts (MoE) set-up? And how do we ensure clinicians are trained to leverage these experts effectively? Big questions, no answers from me yet (thinkering). *Source = The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation

Just had a captivating Fireside Chat on "AI on Healthcare" with industry leaders Amy Munson, Anurag Asthana, and Tarunjeet Gujral! Here are some key takeaways: - Deep Learning for Medical Diagnostics: AI algorithms, especially deep learning neural networks, are transforming diagnostics by analyzing medical images accurately, leading to improved treatment strategies. - Precision Medicine with AI: Exciting possibilities exist for AI to customize medical treatments based on individual patient characteristics, paving the way for personalized healthcare with enhanced effectiveness. - Predicting Disease Outbreaks: AI's potential for proactive public health interventions was emphasized. Leveraging AI techniques can help predict and detect disease outbreaks at earlier stages. - Virtual Health Assistants: The discussion delved into the expanding role of AI-powered virtual assistants in enhancing patient care, increasing accessibility, and streamlining healthcare delivery. Grateful to our esteemed panelists for their valuable insights and engaging discussion! Looking forward to hosting more stimulating conversations at the intersection of AI and healthcare. #AIonHealthcare #FiresideChat #HealthTech #Innovation #MATHAIML MATH - ML & AI Technology Hub | ImpactXcelerate | T-Hub | TalentFarm.ai | Idealabs FutureTech Ventures RAHUL PAITH | HARIKA REDDY | Sai Shanthanu Madduri | Roopa Yeddula | Reynold O' Connor | meghana Vankayalapati | Vaishnavi Mallak | Lahari Pydikondala | Druva Bhagavatula | Suresh Kumarasubramaniam | Pankaj Diwan | Sudarsan Thyagarajan |

An important interview from the founder and CEO of Google Deepmind on the importance of AlphaFold 3. He expects AI developed drugs within the next two years, healthcare is likely to be one of biggest beneficiaries of generative AI in the coming years and Isomorphic Labs could be a $100 billion company in the coming years. A few soundbites: "The number one thing AI can do for humanity will be to solve 100's of terrible diseases, I can't imagine a better use case for AI". "revolutionise the drug discovery process and make it 10x faster and more efficient... that has to be of enormous commercial value". We agree, the investment opportunities are phenomenal. #AI #Healthcare #AlphaFold https://lnkd.in/e3dWUe7z Edit Here's further fascinating detail from Google themselves on what AlphaFold 3 can deliver now: https://lnkd.in/eXC3kpPC TLDR, it's 50% more accurate than traditional methods and its predecessor AlphaFold 2 has already been used to predict hundreds of millions of structures which would have taken hundreds of millions of researcher years to deliver. Exponentially powerful stuff !

"How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities" by Charlotte Bunne, Yusuf Roohani, Yanay Rosen, Ankit Gupta, Xikun Zhang , Theodore Alexandrov, Mohammed AlQuraishi, Patricia Brennan,  Andrea Califano, Jonah Cool, Abby Dernburg, Kirsty E., Emily Fox, Matthias Haury, Amy E. Herr, Ph.D., Eric Horvitz, Patrick Hsu, Viren Jain, Gregory Johnson, David Kelley , Shana Kelley, Anna Kreshuk, Tim Mitchison, Stephani Otte, Jay Shendure, Nicholas Sofroniew, Fabian Theis, Christina Theodoris, Marc Valer, Bo Wang, Eric Xing, Serena Yeung, Marinka Zitnik, Theofanis Karaletsos, Emma Lundberg, Jure Leskovec, Stephen Quake, D.Phil. et al. "The cell is arguably the smallest unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of AI-powered Virtual Cells, where robust representations of cells and cellular systems under different conditions are directly learned from growing biological data across measurements and scales. We discuss desired capabilities of AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions is within reach." Paper: https://lnkd.in/dAhY6krZ #machinelearning #drugdesign

By 2030, the global AI market is expected to reach USD 1.8 trillion. As the AI in healthcare market continues to grow, ML plays a crucial role in applications ranging from medical imaging to drug discovery. Key algorithms powering this transformation include: - Logistic Regression, used for disease diagnosis and condition detection. - Decision Trees and Random Forest, which enhance the interpretability of diagnoses and handle complex datasets. - Support Vector Machines (SVM), crucial for accurate disease classification and risk stratification. What other AI innovations do you think will shape the future of healthcare? Drop your thoughts below. #AIinHealthcare #MachineLearning #HealthcareInnovation #DigitalTransformation #MedicalAI #Diagnostics #PatientCare