Jonathan Funk @JonathanFunk12
PhD student - AI for Protein Design Institute for Protein Design Seattle, Washington Joined March 2018-
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✨ ReaSyn ✨ A generative model designed for predicting molecular synthesis pathways. ReaSyn reframes molecule synthesizability as a chain‑of‑thought (CoT) problem. ReaSyn’s step‑wise ‘reasoning’ generates designable molecules, crushing prior SOTA in reconstruction & hit…
We asked ourselves what could we do if we worked cooperatively as a team to create a single model that could design any biomolecular interactions. Yesterday, we reported details of RFdiffusion3, the next step towards building functional proteins straight from the computer.
Great to finally have this available for people 🙌 Thank you to everyone involved, especially @nscorley & @r_krishna3! We'll be pushing a few smaller updates in the coming days to enhance usability for everyone (including some docs on what you can do with it) - so stay tuned (:
Great to finally have this available for people 🙌 Thank you to everyone involved, especially @nscorley & @r_krishna3! We'll be pushing a few smaller updates in the coming days to enhance usability for everyone (including some docs on what you can do with it) - so stay tuned (:
RFDiffusion3 generates all atom bound conformation, making it significant for flexible targets like DNA. An excellent teamwork to achieve something impossible by any one of us in just few months. @butcher_jasper @r_krishna3 biorxiv.org/content/10.110…
Very excited to share our paper "De novo Design of All-atom Biomolecular Interactions with RFdiffusion3", now on BioRXiv. biorxiv.org/content/10.110… 1/n
Accelerating Biomolecular Modeling with AtomWorks and RF3 🚀 New preprint from David Baker!🚀 1. A new framework called AtomWorks has been introduced to revolutionize biomolecular modeling. AtomWorks provides a unified and modular platform for developing state-of-the-art…
Excited to share our preprint “BoltzDesign1: Inverting All-Atom Structure Prediction Model for Generalized Biomolecular Binder Design” — a collaboration with @MartinPacesa, @ZhidianZ , Bruno E. Correia, and @sokrypton. 🧬 Code will be released in a couple weeks
De novo design of miniprotein agonists and antagonists targeting G protein-coupled receptors 🚀 New preprint from David Baker!🚀 1. This paper introduces a computational and experimental approach for designing miniproteins targeting G protein-coupled receptors (GPCRs) with high…
Announcing Neo-1: the world’s most advanced atomistic foundation model, unifying structure prediction and all-atom de novo generation for the first time - to decode and design the structure of life 🧵(1/10)
New paper: turns out you can train deep nets without normalization layers by replacing them with a parameterized tanh()
New paper: turns out you can train deep nets without normalization layers by replacing them with a parameterized tanh()
DisDock: A Deep Learning Method for Metal Ion-Protein Redocking 1. DisDock introduces a deep learning model designed to predict protein-metal docking with high accuracy, particularly focusing on the interaction of metal ions with proteins using a distance-based approach. 2.…
Build human-like memory for your Agents! Every agentic and RAG system struggles with real-time knowledge updates and fast data retrieval. Zep AI solves these issues with its continuously learning and temporally-aware Knowledge Graph—think of it as human memory for AI Agents.…
Top 5 advances in medicine this week (🧵) 1. AI-designed snake anti-venoms These inhibit the 'three-finger toxins' that make the venom of snakes like cobras and mambas lethal - cheaper, with higher affinity and more scalability than current therapies nature.com/articles/s4158…
The best way to change the world is to start by changing yourself.
Do not allow yourself to become bitter, no matter how difficult life becomes.
Advancing enzyme engineering with an active learning method Many modern applications in catalysis, green chemistry, and biotechnology hinge on enzymes that are precisely tailored for specific tasks. Directed evolution has been a major driver of enzyme optimization, but…
someone has finally done it test time compute + diffusion models a really interesting one for sure 🧵
Large Language Model is Secretly a Protein Sequence Optimizer 1/ This paper demonstrates that large language models (LLMs), originally trained on massive text datasets, can be effectively used as protein sequence optimizers. By integrating them into a directed evolutionary…
In biology we measure downstream variables like genes, neurons, or species. But we can’t always measure their underlying causes. In @PhysRevX I present a physics-based algorithm for learning causal drivers from time series (1/n) go.aps.org/3PwXhxe

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ChimeraX (bsky: chime... @UCSFChimeraX
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Nate Corley @nscorley
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