Framing this for my grandchildren

Tune in to @latentspacepod to hear @Mattmcpartlon1 and @realdealpatil talk about the transformational moment bio AI is going through, and why you don't need a background in biology to make an impact in the space.

To date, finding a drug has been a process of guess & check… screening millions of molecules hoping one binds. @chaidiscovery is changing the paradigm… describe the molecule you want, and the model designs it. @joshim5 and @Mattmcpartlon1 took antibody hit rates from roughly 1 in 1,000 to 1 in 7. That's the jump from search to engineering. On this episode of Training Data, they get into where the field actually is, why 2024 was the moment to start, what drug discovery looks like when the computer does the design, and more. 00:00 Introduction 01:52 From Discovery to Design 03:25 Protein AI Breakthroughs Timeline 06:04 Why Start in 2024 10:13 Diffusion Models Intuition 11:41 Building the Avengers Team 15:22 Hit Rates and Scaling Laws 25:01 Molecular CAD Vision 25:24 Faster Design Loops 26:32 Future Drug Discovery 28:37 Platform Business Model 31:14 Partnering Reality Check 33:44 Data Flywheel Explained 37:16 Staying Ahead at Scale 39:44 Culture and What's Next

Work that traditionally took weeks or months now takes days, and @jinaycodes says that means researchers can stop deciding on one idea upfront and instead try all of them. "With these image generation models, you can now generate so many more ideas. It opens up a whole new problem space and ways of thinking about drug design that isn't just about the condensed time period." "Something that traditionally would take weeks or months, you can now do on the order of days. A lot of times you would just have to decide that beforehand, but now with Chai, you can just try all these things." "Previously with GPT-2 or 3, you would have these things generating almost like a stream of consciousness of any idea they have. It's not really that useful until you can start asking it questions and having it do exactly what you want. That's what we're trying to do here."

Thank you @Sachin_and_Adam for stopping by and letting me share a glimpse into our product! The video is a great intro to what we're working on at Chai - no bio background needed.

What a week at @chaidiscovery. In the last 3 days: - Unveiled our collaboration with @Novartis - Announced our $400M Series C - A new agreement with @argenxglobal

nature is about to lose several longstanding advantages

We’ve raised $400M at a $3.8B valuation to further advance AI-driven molecular design. The round was led by @IndexVentures, alongside @KleinerPerkins, @Sequoia, @_DimensionCap, and others.

We’re delighted to announce our collaboration with @Novartis. Building on a year-long technical engagement, we’re expanding our collaboration with Novartis to support broader deployment of our technology across multiple therapeutic programs. Novartis will gain access to our latest models for prediction and design, including Chai-3.

Miracles will become infrastructure

Incredibly proud of the Chai team. It's a privilege to wake up every day and go to work on models that are actively used by some of the world’s leading pharma companies, as they go about designing the medicines of the future. There is no higher calling.

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I’m delighted to announce @chaidiscovery's collaboration with @pfizer. Their scientists will deploy our AI platform to accelerate drug discovery, including early access to our latest frontier model Chai-3. You can learn more about this partnership and our momentum in @amyfeldman's feature in @Forbes out today forbes.com/sites/amyfeldman/…

Today we are announcing our collaboration with Pfizer to put Chai's frontier AI—including our latest model, Chai-3—directly into the hands of one of the world's leading pharmaceutical teams.

"We have this long term vision of turning biology from something which is trial and error and experimental and make it something that looks more like an engineering discipline in the next century." New Episode with @jackdent ! 0:00 Intro 02:28 Inside @stripe w/ @patrickc, at <100 People 08:45 @sama role in @chaidiscovery origin 16:20 Chai-2 Antibody Breakthrough 19:24 Approaching biology as an engineering problem 24:45 Using AI models to treat people on an individual scale 26:52 Robotics in labs 27:22 Pharma industry today 29:49 The clinical trial bottleneck 32:53 Personalized biology models & cancer vaccines 36:24 Longevity, peptides, and trillion-dollar drugs 39:02 Does it matter that the government cut exploratory science grants? 41:00 What next-gen Chai models will do 42:08 Can frontier AI labs build this themselves? 43:26 Biology is hard

Seeing one of the world's largest pharma companies move so quickly has been eye-opening for me. Thrilled to have Lilly scientists using Chai's models. Their excitement is both contagious and a huge motivator.

Visualizing UMich classes in 3d space: kvnshu.github.io/course-mani… Came across soarxiv.org a while back from @jinaycodes and got inspired to do something similar with the classes offered at my university:

Thank you @NasdaqExchange!

I can't wait for the day these breakthroughs transform the lives of real people. In hindsight, $130M will have been a small price to pay. We're hiring!

after months of antibody design papers that only work on single chains, we are seeing much-needed progress on full IgG congrats to the chai team!