🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Introducing our second cohort of Programme Directors – a group of world-class scientists, engineers and entrepreneurs, with proven track records for building ventures, communities and technologies of societal and economic significance. 🚀 (1/3)

we made a map! gap-map.org is a tool we built to help you explore the landscape of R&D gaps holding back science - and the bridge-scale fundamental development efforts that might allow humanity to solve them, across almost two dozen fields

I'm excited to release what I've been cooking up the past few months at Arc Institute BINSEQ is a family of binary file formats for sequencing data built with paired records and parallel processing in mind with big performance gains (2x-40x) over gzip-fastq with similar storage

This is a wild paper. The anaerobic biochemistry is definitely worth your while (and beyond me to explain), but let me try to explain why this is also an evolutionary bombshell. This is about how nitrogen fixation evolved as we know it.

New paper out in Nature Catalysis! We used a retrobiosynthesis approach to engineer PKSs for the production of new-to-nature valerolactam analogs. Later, we developed a strategy to polymerize these monomers to create novel nylon-5 derivatives. Check it out nature.com/articles/s4192…

💯 “while the infrastructure for data creation, storage, and analysis is exponentially growing, the cultural infrastructure of how we fund and collaborate in science hasn’t kept pace. There is a colossal opportunity to test novel systems to support the scientific research process

Yes! More power to Archaea and Fungi!

🌎🦠 Microbes + AI = Climate Resilience! Our latest in Nature Reviews Microbiology explores how artificial intelligence can supercharge microbial solutions to climate change, speeding up innovation and safeguarding ecosystems. #ClimateChange #AI #OpenScience 🔗 doi.org/10.1038/s41579…

What could Alphafold 4 look like? (Sergey Ovchinnikov, Ep #3) 2 hours listening time (links below) To those in the (machine-learning for protein design) space, Dr. Sergey Ovchinnikov (Sergey Ovchinnikov) is a very, very well-recognized name. A recent MIT professor (circa early

Documenting and sharing research in real-time is underrated in discussions about open science. Hiya Jain and I think software can help change problem selection, collaboration, and funding. We write about how and why we should create real-time, open lab notebooks.

It’s official!🎉I’m thrilled to announce that I will be joining Massachusetts Institute of Technology (MIT) as an assistant professor in a shared appointment between MIT Biology, MIT EECS and MIT Schwarzman College of Computing this fall. My lab will couple ML and high throughput experimentation to harness the remarkable functional diversity

Great question! I have been encouraging philanthropists to support datasets, although we often need rapid iteration between computational and experimental researchers. See below for examples but I have a stack of unfunded and partially funded proposals – DM me if interested.

The plan at FutureHouse has been to build scientific agents and use them to make novel discoveries. We’ve spent the last year researching the best way to make agents. We’ve made a ton of progress and now we’ve engineered them to be used at scale, by anyone. Today, we’re launching

Applications are now open for a special cohort of the Brains Accelerator targeting ambitious AI research programs, with a special focus on security and governance capabilities! 1/

🧵 Today, IMPRINT, a pioneering new nonprofit initiative, is announcing $15 million in philanthropic funding to advance our work to decode the body's immune memory and uncover the causes of chronic diseases. imprint.org/press-release-… With teams in Europe and North America, we are

Our new preprint, "SoftAlign: End-to-end protein structure alignment," is now on bioRxiv! biorxiv.org/content/10.110…

An exciting and important resource!🎉

Bio traditionally focuses on building deterministic models, presuming that we can exert perfect experimental control of static model organisms. As you can see, these ideas are eroding. For the next bio breakthrough, we’re betting on data from a wide range of non-model

Guide your favorite protein generative model with experimental data? Meet ProteinGuide - a method to condition pre-trained models on properties without retraining. We validated it both in silico by guiding ProteinMPNN and ESM3 on 3 tasks and in vitro by engineering base editors.

This week, we published 4 preprints reporting: 1⃣ New new approaches for multiplexing & de-multiplexing proteomic data. 👉 Together, they scale throughput while preserving depth & accuracy. 2⃣ Protein degradation dysregulation in Alzheimer's 🧵 parallelsq.org/blog/proteomic…