Recursion reposted this
Building the largest Generative AI community | Advisor @ Fortune 500 | 2 Million Followers | Keynote Speaker
A Seismic Shift in Drug Discovery⚕ I recently heard a talk from NVIDIA CEO Jensen Huang that made me realize – the tech transformation of pharma over the next decade may be one of the most seismic shifts we will see in any category. It’s an industry ripe for disruption. It takes 10 years and costs billions of dollars to bring a drug to market – with over 90% of drugs failing in clinical trials. While tech has made other industries more efficient, drug discovery has become slower and more expensive over time. But now, the scale of data and compute in drug discovery is reaching an inflection point. “We understand biology,” NVIDIA CEO Jensen Huang said in conversation with Recursion CEO Chris Gibson, “but it’s so hard to understand interrelatedness. You need AI. You’re producing intelligence that leads to the production of drugs.” What does this mean? Scientists will no longer have to rely on physical lab experiments alone to unlock new insights – we can train AI models to make predictions that allow scientists to make more informed decisions about experiments to run and targets to go after.. Jensen said he once thought ray tracing – modeling light in 3-D graphics – would take decades to achieve with existing computing power. But they only had to simulate one pixel – AI could guess the other 64. In a similar way, he says “You don’t have to brute force simulate the physics for every single protein and every single cell.” This new category, TechBio, looks to turn drug discovery into a search problem where connected datasets train AI models to make accurate predictions. To achieve this, scalable, relatable, and reliable data are critical. For example, Recursion has spent years building its proprietary dataset – including images of billions of cells – amassing over 50 petabytes of biological, chemical and patient data. It’s one of the largest datasets of its kind in the world. Experiments and data collection happen in a highly automated wet lab nearly round the clock and this data is optimized to train AI models. (For context, the company does a PhD’s worth of lab work in about 15 minutes.) The second part of the equation to analyze the data is compute power. Jensen said NVIDIA was “the first chip company to build supercomputers for our own use” while Tesla was the “first car company to build supercomputers.” Now, with NVIDIA, Recursion has built the largest supercomputer in pharma. https://lnkd.in/gtv9_3Dq A decade in, with compute, data, and foundation models, Recursion continues to industrialize across the drug discovery and development chain – getting to lead drugs 3x faster than industry average at 2.5X less cost. And this is only the beginning. Watch this space. #technology #innovation #biotech #pharma #drugdiscovery
True. Prediction over simulation is how they brought ray-tracing to real-time graphics (ray-tracing is something that's done in Hollywood/3D cinema; its all pre-rendered but can't be achieved for real-time rendering applications. Bringing real-time ray-tracing to graphics cards was initially thought to be decades away). But somehow, Nvidia was able to achieve real-time ray-tracing way in advance through predictive algorithms back in 2018. I wonder what other problems you would be able to solve now that were seen to be decades away using this approach (other than drug discovery)?
I wish AI indeed accelerate the drug development by making some of the process steps efficient and cut trials time and reduce failures. Although it will take some time to see ho we benefit exactly.
I'm excited to see what discoveries and cures are around the corner in regards to medical treatment.
Thank you Steve Nouri - great insights!
Absolutely game-changing view, Steve. The potential for tech to revolutionize drug discovery is staggering. The future is indeed promising.
Exciting to see the potential of AI in drug discovery and the impact it will have.
It is incredibly exciting to witness these advancements. I truly believe that for the first time, we will start accelerating discoveries in health and actually begin to cure the incurable. The integration of AI and data in drug discovery is a game-changer for the pharma industry!
This should be a number focus The rest is nice to have 🙏
Visionary (CEO) at EOS Worldwide | Empowering Businesses with the Entrepreneurial Operating System | Author of People: Dare to Build an Intentional Culture
2wThis is promising, but isn't it a bit dangerous? AI is only as good as the data it's trained on. Right? I spent nearly 20 years in the pharma business, and we had a saying: "The drug you take is the one not tested."