⚡️ My PhD thesis is on arXiv! ⚡️ To quote my examiners it is "the textbook of neural differential equations" - across ordinary/controlled/stochastic diffeqs. w/ unpublished material: - generalised adjoint methods - symbolic regression - more! arxiv.org/abs/2202.02435 v🧵 1/n

Can confirm, Adaptyv are amazing. 100% you should work on frontier science from the beautiful shore of the largest lake in Switzerland.

Introducing Strong Stochastic Flow Maps TLDR: Stochastic Flow Maps where we learn the stochastic solution path. Work led by Sam McCallum, @zwblasingame, with Timothy Herschelll, @AlexanderTong7, and @JamesFosterBath Arxiv: arxiv.org/pdf/2606.01086 Code: github.com/sammccallum/ssfm

Alright chaps, I am in Boston for PEGS, here to chat all things AI antibody engineering. Send me a message if you're around! 🧬 (PS I'm hiring! Pharma x AI startup, come make drugs with us.)

We're growing the AI team at @ManifoldBio, starting with a role to train protein foundation models on our proprietary data. I believe Manifold is the most interesting place to work on protein design. We're designing and testing millions of binders per month, including in vivo, and accelerating. No one else has data like this. If you have deep experience pretraining or fine-tuning protein models and want to work somewhere the data actually lets you push beyond what public datasets can enable, please reach out.

Who's the most talented software engineer you know that should stop building SaaS products and instead join a company that builds in the physical world? Refer them to me and get a 2000 USD referral bonus if we end up hiring them!

🚀 Exponax v0.2.0 — fast & differentiable PDE solvers in JAX New: 3D Navier-Stokes on a single GPU, wave equation stepper, improved dealiasing & memory efficiency 4096² / 256³ on 24GB consumer GPUs 10k² / 512³ on A100/H100 📦 pip install exponax github.com/Ceyron/exponax

Excited to announce our next In Silico event on February 25th, 6pm at @localglobevc in King's Cross! Don't miss great talks from @McclainThiel from UCL , @PatrickKidger from @cradlebio, @kpetrovvic from Oxford, and Ivan, PD at @ARIA_research. Reg here: luma.com/gb3uso7t

Heck yeah, this is awesome :D Integrated into everyone's favourite de novo tooling (Boltz, ...) when?

Heck yeah! :D

we have nine roles open @ Loyal right now - come be a part of the final sprint to bring to market the first FDA-approved longevity drug.

Interesting post from @owl_posting asking whats next for antibody design. I agree with him that in vivo properties are likely the answer, but the question is how do we get there? The PDB has enabled de novo binding models to be incredibly successful. Properties like developability likely are implicit in the models, given the data they are trained on is well formed antibody crystal structures. Fwiw at @ManifoldBio using our open source mBER de novo design approach, we also see very similar affinity/developability properties for our molecules, when we get around to it we might update the preprint to reflect this. Of course you are welcome to try out mBER as it is open source: github.com/manifoldbio/mber-… Back to in vivo properties, the challenge here is that unlike the PDB, we don't have an accurate and high throughput dataset for antibodies in terms of PK/PD or ADAs etc. Using standard methods, these traits are quite hard to measure in high throughput, which make learning them very challenging. And arguably, these properties are more important than binding for making a successful drug. These properties are complex, essentially you are asking "I have a binder that I know binds a target of interest in vitro: but does it get to the right place, bind the right target (and not the 20k other possibilities in the body), last long enough to enact its function (i.e. PK) and then perform the function on the target (inhib, activate etc) in a highly complex living system that is nothing like the petri dish that the molecule likely was initially tested in before?" To some extent, binding has been a solved problem for quite a while. If you talk to folks like Dane Wittrup (inventor of yeast display, founder of Adimab), he will tell you that using yeast display Adimab can design binders to specific epitopes / gpcrs, whatever you want. They will bind with high affinity and specificity in vitro. These new de novo methods indeed speed this process up by a couple months, but fundamentally the really question is still, does your molecule work in a living system, and have you optimized for all the properties (PK,PD, ADA) that will enable clinical success. This is exactly what why we built @ManifoldBio. We saw the need for high throughput in vivo data to unlock the real power of AI. AI / de novo design are only as good as the data they are trained on, without in vivo data, we will never learn in vivo properties! So we built a measurement engine to solve that. We have generated PK data on over 12,000 molecules to over 100 targets of interest to date. We have generated in vivo tissue enrichment data on half a million molecules to almost a thousand targets. This is the data we believe will unlock the true promise of AI. Lot's of folks talking about virtual cells, but perhaps we should start thinking about virtual organisms. Even if you had a virtual PK model, this would be a huge benefit to drug discovery. In fact, we already are building such a model, more details soon :)

Next company to release cool protein designs on @proteinbase! Check out @cradlebio's competition winning EGFR binders. Many more data drops on the way 👀

We’re excited to share a new multi-year collaboration with @TakedaPharma, building on the success of our first engagement. Under the agreement, Nabla will receive double-digit millions in upfront and research payments and is eligible for success-based payments exceeding $1 billion. The partnership deploys Nabla’s AI-driven JAM platform across Takeda’s early-stage programs to include de novo design of antibodies in parallel for multiple targets, multispecifics, challenging targets, and other custom therapeutics. Read more below

🚀 New talk! "Automated ML-guided lead optimization: surpassing human-level performance at protein engineering" ▶️ youtube.com/watch?v=mEhBBI0j… ✨🧪 This was a talk I gave at the recent AIxBIO conference in Cambridge UK. A 10-minute pitch for what we do at Cradle!

Today we’re releasing real-world experimental data for over 1000 novel AI-designed proteins on our new platform @proteinbase!

A reminder that there is now *1 week* left until the MLSB deadline on October 1st! Send in your 🧪bio 🤖ML papers, for either 🇺🇸San Diego, 🇩🇰Copenhagen, or ✨both✨!

Taking a first step towards hibernation pods :)  Just announced a $58M Series A led by @foundersfund to back the core roadmap reversibly cryopreserve human organs -> help transplant patients build sustainable business -> accelerate R&D for whole body cryo

Today we're dropping the "beta" tag from Adaptyv, launching our new website and announcing our $8M seed round. When we started Adaptyv a few years ago, our core belief was: AI models for biology are only as good as the experimental data they're trained on and the hypotheses they can test in the real world. Now, after a year of working with many great partners, we’ve scaled our infrastructure to the point that we're now open to anyone who wants to use our platform! Overall, this year, over 30 companies started using Adaptyv to validate their protein designs - from some of the biggest pharmas to frontier AI labs to many, many techbio startups. We've run hundreds of experiments, tested well over 10,000 proteins this year and are generating the data that validates the best AI models currently in development.