This is a very poor one line summary of the article by @DaphneKoller IMO. The point is not just data volume. The right kind of data scaled in the right axis with the right experimental designs for well defined tasks + using the models to discover biology (not eval maxxing)

This time, I am publishing in full the album authored by TsKBEM in 1969, dedicated to the mock-up article N1-L3 №1M1, built for the comprehensive development of the rocket-space system. 1/16

What if AI could invent enzymes that nature hasn’t seen? 👩‍🔬🧑‍🔬 Introducing 🪩 DISCO: Diffusion for Sequence-structure CO-design 14 rounds of directed evolution and over a year of wet lab work. That's what it took to engineer an enzyme for selective C(sp³)–H insertion, one of the most challenging transformations in organic chemistry. DISCO surpasses this with a single plate. No pre-specified catalytic residues, no template, no theozyme, no inverse folding, just joint diffusion over protein sequence and structure. 📝 Blog: disco-design.github.io/ 📄 Paper: arxiv.org/abs/2604.05181 💻 Code: github.com/DISCO-design/DISC…

Progress in AI modeling of proteins leaves major gaps affecting most proteins and especially functional analysis. The opportunities to transcend them beacon: AI models can now predict static protein structures with high accuracy. This achievement is rightly celebrated. It is equally important to recognize what remains unresolved and why those gaps matter hugely for biology. 1. Modeling intrinsically disordered regions (IDRs) is a central limitation. Roughly 30–40% of amino acid residues in the human proteome fall into this category, and ~70% of proteins contain substantial disordered segments. These regions do not adopt a single stable structure; instead, they exist as dynamic ensembles that often become structured only upon binding or under specific cellular conditions. Current AI models -- trained on static structures -- do not predict these ensembles. Instead, they either assign low confidence or produce arbitrary conformations. This is not a minor edge case; it is a large and functionally critical fraction of proteome space, deeply involved in signaling, regulation, and disease. 2. A second key limitation concerns protein function. Biology ultimately depends on changes in conformation, interactions, and state. Many key biological processes arise from shifts between multiple conformations or from subtle perturbations induced by amino acid substitutions, post-translational modifications, or binding partners. Current models are optimized to predict a single, most likely structure. They are not designed to capture how that structure changes under perturbation, nor how populations of states shift. As a result, predicting function -- arguably the central goal -- remains a weakness in many cases. Outlook These two challenges point to a deeper issue: proteins are not static objects but dynamic systems governed by energy landscapes. What is needed next is not just better structure prediction, but models that can capturing ensembles, relative state populations, and the effects of perturbations on those distributions. This will likely require accurate and scalable measurements of proteins, integrating generative models, explicit or learned energetics, and dynamic sampling into a unified framework. In this sense, the field is entering a new phase. Predicting “the structure” was a milestone. Understanding how proteins move, adapt, and function -- especially in the large, disordered fraction of the proteome -- remains the frontier.

People keep saying FAM is turning into to The Expanse, but it's actually going to become the Red Mars trilogy. And a space elevator on Mars was actually a major plot point for a Mars rebellion in that series.

TIL about the existence of blebbisomes (large extracellular vesicles that contain everything but a nucleus) and they are named this because they, in the technical parlance, "bleb around" it's so WHIMSICAL i'm so happy bleb bleb bleb

Yep! We have so many drug targets. Finding targets is not the issue. The issue is testing them and going through the rigamarole to get them produced and on the market. AI-in-medicine people are missing this and talking about an area that just isn't the bottleneck to new drugs.