"Claude, show me these scorpion toxin binders in VR"
Kind of neat to view structures this way
This Stereofold repo can help Claude cast structures to your headset (Quest 2 shown) and you can go back and forth with Claude to update the scene. The viewer is built on Mol*
The repo has also been updated with a tour of these recently designed scorpion toxin binders
github.com/jvogan/stereofold
We're excited to share results from our latest wet lab campaign with @AnthropicAI and @citrini.
Claude designed novel proteins to neutralize scorpion toxins and we tested 211 of these protein designs in our automated wet lab. 17 of them showed binding to the toxins in our experiments, with all data now available on @proteinbase.
This is a great example of what we're building towards: A world where anyone can run drug discovery campaigns on our automated wet lab.
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“Grok, design protein binders for prostate cancer research”
In this binder design workflow test, Grok used various comp bio tools on neocloud resources to generate potential binder candidates against the AF-2 site of the androgen receptor (as many prostate cancers are AR driven).
Grok created > 1K candidate sequences using BindCraft2, RFdiffusion3, and BoltzGen, along with ProteinMPNN sequence generation.
ESMFold2-Fast and Boltz2 were used to predict the binder–target structures and evaluate their interfaces. A subset was also screened with ESMFold2-Full and Protenix to further assess binder–target placement.
Next, to test a refolding workflow, new predictions of 30 candidate binder–target complexes were made via ESMFold2-Fast, ESMFold2-Full, Boltz2, and Protenix, where 5 random seeds were used per tool (another 600 predictions total).
For a sequence diversification workflow, given starting predicted binder structures, new sequences were generated for 8 backbones using ProteinMPNN and the recent ProtonPotts, which takes into account protonation states.
These seq gen tools created 64 redesign attempts. A subset of these was then evaluated with fresh structure predictions using ESMFold2-Fast, ESMFold2-Full, Boltz2, and Protenix.
A pH-aware workflow continued with taking a subset of these, along with their parents, then using PDB2PQR / PROPKA and APBS to predict protonation states and calculate electrostatic contributions to the binder–target interactions, over a pH range of 6.5 -8.0.
Ballpark compute costs ranged roughly ~1-26 cents per structure prediction or design attempt.
The skill repo, BioSymphony Structure Factory, was used as a foundation for Grok to use these tools. Modal was used for the cloud resources.
And, Claude Sonnet 5.5 made this video of the workflow test using ChimeraX, HyperFrames, and ElevenLabs.
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To test similar computational binder design workflows or have your agent create/run your own, you can point your agent at this BioSymphony Structure Factory skill repo for a good foundation.
This contains various learnings and a frequently updated tool list, including recent releases such as ProtonPotts, BindCraft2, Anthropic’s accelerated kits for various tools, use of NVIDIA’s BioIR, and more
github.com/BioSymphony/struc…
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Besides very nice interactive visuals *in* Codex, you can also have Codex send your molecules over to a VR headset like Meta's Quest or Apple's Vision Pro to view and interact with them in 3D. It feels very 'Jarvis-like' to talk to Codex in voice mode and see it update the view, pull up structures, and perform analyses in near real time.
Here's an example of Codex sending structures to a Quest 2 using Stereofold, an open-source plugin I put together.
It builds on the Mol* molecular viewer and its WebXR support, adding tools and skills that let Codex share structures with the headset and keep editing them while you wear it.
I tested this on a Quest 2 headset over Wi-Fi, but it should also work for other headsets like Apple's Vision Pro.
In honor of Dev Day, try out the Molecular Structure Viewer plugin (newly updated and improved!). You can do some really cool stuff with it! Videos coming soon
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Here's what it looks like when Codex sends a labeled KRAS G12D–cyclophilin A–daraxonrasib complex (PDB 9AX6) to a Quest 2 headset in passthrough mode
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To view your own structures in a headset and keep editing them from Codex, you can point your agent at the Stereofold repo below.
I rec talking to Codex in voice mode while you're in the headset for a 'Jarvis' like experience
github.com/jvogan/stereofold
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For Claude Science users who want to design binders with a workflow similar to the computational part of Anthropic's binder study, you can use this Claude Binder Lane skill below
You and Claude Science pick:
Target and site → tool mix, including whether to follow Anthropic’s published stack closely or swap in other tools → where each tool runs (API, plugin, local, or cloud compute such as Modal) → your budget → metrics and iterations → then get your binder sequences and predicted structures.
The skill can help Claude Science make use of >30 tools for your own binder design workflow. This includes developments since Anthropic's original binder report, such as BindCraft2 and Anthropic’s recent accelerated ESMFold2 and Boltz-2 kits.
Sonnet 5.5 made this video covering the skill and workflow
Many drugs work by binding to a specific target in the body and blocking or changing what it does. An important first step in the drug development process is designing a molecule that can bind tightly to its target. Traditionally, that's meant weeks or months of expert work per target, sifting through a large number of candidates to identify the few that work.
We wanted to test if Claude could successfully design novel protein binders from scratch (also called de novo design). With a protein design prompt written by a human expert, Claude autonomously designed protein binders against 14 out of 15 targets.
We then worked with Adaptyv Bio and Twist Bioscience, who independently built and tested the proteins Claude designed.
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Here’s an example of a modified shorter route you could use with the Claude Binder Lane skill in Claude Science
Claude Science used BindCraft2 on Modal to generate candidate binders against PD-L1, a cancer immunotherapy target, then it checked a subset of the computational binders with Anthropic’s accelerated Boltz-2 and ESMFold2 kits.
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For researchers wanting to try this out, you can point Claude Science at this Claude Binder Lane skill repo to get started. Or, just create your own workflow from this skill as a template with your preferred AI agent.
Example starting point is to pick your target and binding site, your license restrictions if any, your budget, where you want to run the tools (e.g. API or cloud compute like on Modal), decide on metrics to optimize, then get going with your computational binder generation.
github.com/jvogan/claude-bin…
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Here's a test of GPT-6 Sol using Microsoft Research's new RetroChimera tool to generate potential synthesis routes for three light-emitting molecules:
1) JF646, a far-red fluorescent dye used in cell imaging
2) PF555, an ultra-photostable orange fluorescent dye used in cell imaging
3) ν-DABNA, a blue emitter for OLED displays
Codex with GPT-6 Sol chained together RetroChimera with Retro* route search via Syntheseus (also from Microsoft Research), using a ZINC derived starting material inventory that was minimally expanded when synthesis obstacles were hit.
Several potential, complete synthesis routes were generated: one for JF646, two for PF555, and one for ν-DABNA. That’s as far as I took this tool calling demo.
The video attached covers the workflow. Codex also created this accompanying ChatGPT site:
biosymphony-retrochimera.bio…
Synthesis is a major bottleneck in drug discovery. AI synthesis planning can help propose better routes and make generative molecular design tractable.
With our retroChimera model we have been pushing the boundaries of these models further - out in @Nature today. 1/2
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Here's a high level overview of the Codex + RetroChimera generated routes, which had starting inventory constraints, and the published reference synthesis routes.
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For others interested in trying similar workflows with their preferred AI agent, this BioSymphony repo contains learnings and skills for powering agentic use of small molecule tools like RetroChimera
github.com/BioSymphony/small…
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Opus 5.5 made a holiday animation about kinesin, microtubules, and the gift of cell-cell communication
"It's Christmas Eve inside a cell, and Kiki the kinesin has a gift to deliver. Fueled by ATP, she sets off down a microtubule, 8 nanometers at a time, saying hi to every neighbor she passes.
But up ahead, the microtubule's growing tip is losing its glow. Halfway there, her microtubule suffers a catastrophe, and the collapse comes racing back toward her feet.
Will Kiki survive to make her delivery to the cell surface?"
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Opus 5.5's digital painting ability is incredible. I asked it to "paint" cell biology in the style of the masters using python and then turn that into a video.
Several Opus 5.5 agents and a Fable 5.1 agent painted:
Starry Night: stars in mitosis, the cypress a neuron
The Great Wave: a macrophage engulfing bacteria
La Grande Jatte: a blood smear in 438,788 dots
The Kiss: fertilization
Water Lilies: Tetrahymena
Ink plum: a neuron
Hilma af Klint: the cell cycle
Haeckel: radiolaria
for the past few months i've been asking our models to paint. opus 5.5 is very skilled at emulating different styles
every image here is a python program generated pixel by pixel. there is no image model, and no off-the-shelf art software. instead, it's about 7,500 lines of code using standard libraries to emulate different brush styles. the agents don't use any pictures as reference, instead working only from what they know about each painter
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“Codex, please design some small molecules to fight malaria.”
In this tool calling demo, Codex with GPT-6 Astra used LDDM to generate candidate molecules which might bind Plasmodium falciparum's dihydroorotate dehydrogenase, an enzyme the malaria parasite needs to make pyrimidines. Disrupting this enzyme can stop the parasite from replicating.
Codex used LDDM in two ways:
1) de novo molecule generation
2) extending a fixed fragment of the existing dehydrogenase inhibitor, DSM265
DSM265 was reported by Coteron et al in 2011. This demo uses its experimentally determined binding pocket in PDB 4RX0. The attached vid shows the LDDM generation steps.
The LDDM generated candidates had no exact matches in Astra's PubChem and ChEMBL searches, and the de novo molecule had no >85% similar hits.
Codex used retrosynthesis tools (AiZynthFinder, Syntheseus, and SynPlanner) to search for routes to make these, but no complete routes were found during the demo.
Codex used RDKit and PoseBusters to check geometry and protein/cofactor clashes (no severe clashes). Codex also used AutoDock Vina to score fit in the pocket and these computational candidates scored similarly to DSM265.
Large Drug Discovery Model is out! I am exited to introduce our new generative SBDD framework. We experimentally validated LDDM across 5 targets and successfully designed novel, validated hits with structural accuracy confirmed by X-ray.
Preprint: biorxiv.org/content/10.64898…
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Also, this skill repo is useful if you want your own AI agent to give this a shot, as it contains various small molecule tool-calling learnings.
github.com/BioSymphony/small…
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Oh, muse can pull in protein structures, show alignments, and make diagrams.
Here's a demo of muse pulling in protein structures via the mol* viewer, showing a KRAS sequence alignment, displaying the structure of the anti-cancer drug daraxonrasib, and making diagrams about KRAS + daraxonrasib mechanisms.
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