Ok let me explain how cool this is from my perspective , please RT! I developed one of the first methods to probe RNA structure in high throughput inside living cells, and it takes tons of time and $$ to apply on thousands of viruses - it’s practically impossible. This ML approach is just as good and works in seconds for nearly free ! Not only that, it highlights the MOST IMPORTANT structures that track with function. It’s the future of RNA strucute prediction and discovery across all families 🔥 biorxiv.org/content/10.64898…

Anthropic is a Public Benefit Corporation. this is not new.

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Claude Science is an amazing tool. But, as @pdhsu has pointed out, these binders weren’t designed by Claude, but rather by domain-specific models, most of which are publicly available, like RFdiffusion. Maybe this is a good time to explain the whole science behind this a bit, as well as the history of these models. A “binder” in this case is simply a protein or a fragment of a protein engineered to stick tightly and selectively to another protein. One example is a molecule that can latch onto a receptor and prevent it from interacting with its normal partner, thus blocking its function. Optimising "binders" is just one small part of the drug discovery pipeline. We already have AI models that are very good at the task of binder design. These models have been extensively trained on relevant domain specific data: int his case, protein structure data. One example is RFdiffusion, a de novo protein design model which was published in 2023. Unlike AlphaFold, which predicts existing protein structure from sequence, RFDiffusion can generate new candidate protein structures, including structures shaped to bind a specified patch on another protein. RFDiffusion was built by taking RoseTTAFold, a protein-structure-prediction model, (so similar in nature to AlphaFold, which was released in 2021, and retraining it as a diffusion model. What researchers did in practice was take experimentally determined protein structures from the Protein Data Bank, added noise to their coordinates, and taught the model to reconstruct them. The original RFdiffusion paper was already demonstrating experimentally validated binders with nanomolar affinities and a 19% hit rate on several test targets. So the core of this technology was not created through Claude. The key models for protein design are now three to five years old. What Claude adds is still amazing useful and labour saving (I've written about how I'm excited about it before), but imo it acts more like a very useful interface and orchestrator.

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.

As promised, I put together my thoughts on the ZEUS failure of IL-6 inhibition in atherosclerotic cardiovascular disease and what it means for drug development efforts centered around human genetics. 🔗Link: thecodon.substack.com/p/why-…