Roberto Giovannoni’s Post

It requires a lot of infrastructure and logistics to build complex AI systems. Today we’re sharing an initial set of cell models in a centralized place to accelerate gains from machine learning in cell biology. This includes scGenePT and SubCell, models built and supported by CZI and our collaborators. They come with ready-to-run notebooks, access to both the code and processed data to train and validate new models, in support of open science at the interface of AI and bio. It’s exciting to support our mission to integrate AI into biomedical research, where this hard work will transform possibilities into real scientific breakthroughs. https://lnkd.in/gqaiGHyR

When AI, cell biology, and open science converge, we unlock groundbreaking possibilities! Proud to be part of this journey and excited to see the launch of virtual cell models by #cziscience--paving the way for transformative insights in biomedical research. 🚀 #OpenScience #VirtualCellModels #AIInBiology"

Cells are the smallest units of life, and despite all the scientific research done to date, there’s still so much we don’t know about them—especially how they change when we get sick. That’s why we’re building AI-powered virtual cells to help scientists explore the behavior of healthy and diseased cells and better understand human health. But how do we get there? Read more about how the scientific community can move forward in creating the future of AI-powered biomedical research:

This is really cool. The notion of solving biology by simulating it in computers has been around for a very long time, but mostly as a fantastical proposition far far away in the future. These days, with the Turing test suddenly being obsoleted, many notions that were previously fantastical, can quickly become reality. Here is a proposal from some of the most credible scientists in the world, to build an AI Virtual Cell. This is meant to be a “learned simulator” of biology across three physical scales: molecular, cellular and multi-cellular. The proposed system would integrate many types of biological data to drive toward a comprehensive understanding and mastery of cellular mechanisms. The concept is now within reach due to the arrival of technologies to measure biology at massive scale (ahem) and all the recent advances in AI. Ultimately, you can start seeing a path toward putting a lot of biological research on rails, toward understanding the workings of the cell, and toward curing diseases. We could well be on our way toward front-loading the Century of Biology.

Stanford researchers just introduced the Virtual Lab, an AI research platform where specialized AI agents collaborate with human scientists to tackle complex scientific challenges — successfully designing and validating new nanobodies against recent COVID variants. The details: The system uses multiple AI agents with distinct specialties (immunologist, ML specialist, computational biologist) coordinated by an AI Principal Investigator. The AI team members hold structured "meetings" to discuss and refine their work, requiring only light guidance from human scientists. Over 90% of the AI-designed molecules were stable and worked as intended when produced in the lab. Lab testing identified two promising candidates from 92 designed proteins that can attach to both new COVID variants and the original virus. https://lnkd.in/daBBUPK4

🌟🤖 The exploration of innovation deepened as Talia Grace Haller, Strategic AI Adviser to the Chief Medical and Scientific Officer | All of Us Research Program, The National Institutes of Health, delivered an eye-opening keynote. Talia shared invaluable lessons from crafting the AI strategy for one of the largest biomedical research initiatives, highlighting the transformative power of AI in life sciences. From streamlining operations to gaining a competitive edge in project and portfolio management, this session left attendees inspired to harness AI for excellence. 🚀✨ #AIInLifeSciences #PortfolioTrack #Portfolio2024 #InnovationInBiotech #LeadershipInProjects

Researchers at Stanford University recently developed “The Virtual Lab,” which is a framework that enables four specialised AI agents to collaborate on scientific challenges. “The Virtual Lab” relies on the Principal Investigator (PI), which coordinates tasks between the specialised AI agents. The PI holds structured meetings, allowing the specialised AI agents to discuss and refine their work. Here’s what the team looks like: - Immunologist: Proposes a scientific hypothesis related to the immune system, a complex network of cells, tissues, and organs that protect the body from harmful infections. - Scientific Critic: Acts as a critical thinker, evaluating the strengths and weaknesses of scientific arguments and experimental designs. - Data-Driven Specialist: Extracts meaningful insights from complex datasets by identifying reoccurring patterns. - Computational Biologist: Applies computational techniques to biological problems. Over 90% of the AI-designed molecules in “The Virtual Lab” were stable and worked as intended when produced in labs by human scientists and successfully designed 92 Nanobody candidates to fight the SARS-CoV-2 Virus, a strain of the Coronavirus (COVID-19). See the link in the comments for more information. #leadershipspeaker #leadershipdevelopment #bradtwynham #technologyspeaker #conferencespeaker #keynotespeaker #radicalthinking #radicalleadership #radicalculture

Exciting news! Last week, Viacheslav Dubovitskii and I had the privilege of presenting Algorithmiq's cutting-edge software platform for quantum life sciences at the “Artificial Intelligence and Network Medicine: Path to Precision Medicine” workshop. This prestigious event was organized by Prof. Joseph Loscalzo of Harvard Medical School and Prof. Alberico Catapano of the University of Milan, and sponsored by Fondazione Menarini. Historically, scientific reductionism has shaped our understanding of diseases. However, many diseases are inherently complex, involving intricate molecular interactions influenced by genomics and environmental factors. Network medicine tackles this complexity with a holistic approach, leveraging big data and multi-omic datasets to analyze molecular interaction networks. By integrating deep learning and AI, this multidisciplinary field combines applied mathematics, biomedicine, computer science, engineering, and network science to create innovative analytical strategies. Algorithmiq is at the forefront of merging quantum computing with network medicine, revolutionizing disease prevention and drug discovery. Quantum computing’s unparalleled processing power enables us to manage and analyze the complex data associated with disease mechanisms and drug interactions more efficiently, offering deeper insights into disease etiology and potential cures. Stay tuned for our upcoming announcements, where we'll showcase powerful demonstrations of this groundbreaking integration. The future of precision medicine is here, and Algorithmiq is leading the way! #quantumcomputing, #quantumlifesciences, #networkmedicine

MIT researchers have developed Boltz-1, a groundbreaking open-source AI model that matches AlphaFold3's capabilities in predicting protein structures. This innovation promises to accelerate biomedical research and democratize access to advanced structural biology tools. https://lnkd.in/e5gcZyr4

🔮 Exciting News in the World of AI and Drug Development! 🚀 🧬The latest upgrade to AlphaFold by Google DeepMind promises to revolutionize the study of proteins even further. This breakthrough not only signals a major advance in drug development but also holds tremendous potential for DNA modeling. ✨Imagine the possibilities this enhancement will unlock for solving complex medical challenges and designing innovative therapies. The fusion of AI technology with molecular biology is truly reshaping the healthcare landscape, paving the way for transformative discoveries. ✨This remarkable feat exemplifies how collaboration between tech innovators and scientists can spark groundbreaking advancements that benefit society as a whole. #AI #DrugDevelopment #ProteinStructure #HealthcareInnovation Source: https://lnkd.in/grzv3vtg