Computational Biology Resources

Today in Cell, we published new research showing how AI can help accelerate cancer discovery. With GigaTIME, we can now simulate spatial proteomics from routine pathology slides, enabling population-scale analysis of tumor microenvironments across dozens of cancer types and hundreds of subtypes.   Developed in partnership with Providence and the University of Washington, our hope is that this work helps scientists move faster from data to insight, revealing new links between genetic mutations, immune activity, and clinical outcomes, and ultimately improving health for people everywhere. https://lnkd.in/dSpPdtzz

I’ve worked on AI my whole life because I’ve always believed it could unlock the ability to answer some of the biggest and most intractable problems in science. Our first big science breakthrough happened five years ago when we announced our solution to the protein structure prediction problem: AlphaFold 2. It has been incredible to see its impact since then. More than 3 million researchers across 190 countries have used this tool for disease understanding, drug discovery and more. And it was an honour of a lifetime for our work to be recognised last year with a Nobel Prize. One of our greatest ambitions is for AI to aid in accelerating drug design and help cure all diseases. This is what led me to found Isomorphic Labs, which is already making amazing progress. We’ve also expanded AlphaFold to predict the interactions of all of life’s molecules. But AlphaFold represents more than a solution to a biological puzzle. It demonstrated how AI can crack ‘root node’ problems - where a single breakthrough unlocks entire new avenues of research. It is a critical step towards a long-held dream of mine: building a virtual cell. Imagine running ‘in silico’ experiments orders of magnitude faster than in a wet lab. Scientists could rapidly test hypotheses, model complex pathways and see how a drug affects a cell. It would be an incredible boon not only for fundamental biology but also for medicine. Although for me, AlphaFold was never just about biology. It was the first major proof point for a much larger thesis: that AI could be the ultimate tool for advancing science. By processing data or helping us come up with new hypotheses, I think AI will help us tackle some of humanity’s greatest challenges and answer fundamental questions about the universe. From materials design to fusion energy to mathematics, I believe we’re on the cusp of a new golden age of discovery. We’re just getting started. Read more about AlphaFold’s impact: https://lnkd.in/eNeqxqQp

Right now, every CEO is wondering the same thing: “How can artificial intelligence help maximize our impact?”   Delivering on the promise of AI isn’t just good business, it has the potential to help us address some of society’s most pressing challenges. So today, I wanted to offer a closer look at how AI is helping us discover new medicines at Novartis.   The process of identifying a new drug, running patient clinical trials, and bringing it to market takes over a decade. Each new medicine costs on average $2 billion to develop, and we know nearly 9 in 10 of the treatments we work on will fail before they ever reach patients.   A major early step in that process is identifying individual targets in the body that we want to design a drug for. Once we identify that target, which most commonly is a protein, we look for molecules that might address the target’s underlying issue – ultimately those molecule structures form the basis for every successful treatment.   Unlocking the right protein and molecular structures is complex stuff – each step often takes years to get right and our scientists consider billions of potential chemical structures that might lead to effective and safe drug candidates.   AI offers us the chance to accelerate that process. Working with partners at Isomorphic Labs – including members of the Google DeepMind team that were awarded the Nobel Prize this year – we’re now able to do things like model how a protein folds and interacts with the molecules we design. AI models also make it possible for us to analyze different chemical structures simultaneously. It has the potential to add up to significant time savings for our drug development scientists and their work to predict what molecules might treat specific diseases better and faster.   We’re just at the beginning of what this technology can do. As we incorporate AI throughout Novartis’ work, I’m excited to see all the ways it helps us unlock the mysteries of human biology, so we can deliver better medicines that improve and extend patients’ lives.