Over the past few months, AI-based tools have emerged that enable scientists to design original antibodies on the computer for the first time. A year ago, none could reliably do this computationally. But now companies like Nabla Bio, Chai Discovery, Latent Labs, Manifold Bio and, most recently, DeepMind-spinoff Isomorphic Labs have allowed high success rates. There are even open source tools, such as BoltzGen and Germinal, that deliver similar performance. The rapid progress in antibody design matters because these molecules are among the most versatile tools in biology. Many medicines ' including Humira and Adalimumab ' are antibodies, and cheap diagnostics, including $1 COVID tests, rely on them as well. These Y-shaped proteins make excellent binders, as the two arms can latch onto proteins or other molecules and block their activity. Before these AI tools existed, scientists searching for a useful antibody would first need to screen billions of candidates in laboratory assays to identify just a handful with high affinity for a target. BindCraft, released in 2024, changed this. For many targets, a suitable binder can now be found after just tens of attempts rather than billions. BindCraft uses the AlphaFold 2 model, but inverts it: the model creates a protein structure expected to fit onto a chosen target, then converts that 'shape' back into an amino acid sequence that can be synthesized and tested in the laboratory....

Nearly two years after Google DeepMind released an updated AlphaFold3 geared at drug discovery, its biopharmaceuticals spin-off, Isomorphic Labs, announced an even more powerful artificial-intelligence model ' and they're keeping it all to themselves. Isomorphic Labs, based in London, touted the capacities of its 'drug-discovery engine' ' which it calls IsoDDE ' in a 27-page technical report, released on 10 February. Achievements, including precise predictions of how proteins interact with potential drugs and antibody structures, have impressed scientists working in the field. Yet unlike the AlphaFold AI systems for predicting protein structure ' which were made accessible to other researchers and described in depth in journal articles1,2 ' IsoDDE is proprietary, and the technical paper offers scant insight into how to achieve similar results. 'It's a major advance, on the scale of an AlphaFold4,' referring to an unreleased future generation of Google DeepMind's technology,says Mohammed AlQuraishi, a computational biologist at Columbia University in New York City who is working to develop fully open-source versions of AlphaFold. 'The problem, of course, is that we know nothing of the details.'...

The interior of a cell is densely packed with millions of molecules vibrating, jostling, and moving about. Sugar molecules fly through a cell at 250 miles per hour, ricocheting off of ribosomes, organelles, cytoskeletal fibers, and enzymes. Indeed, every protein in the cell is hit by about 1013 water molecules each second. This chaos makes biology seem hopelessly convoluted. With everything moving so quickly, how can we begin to understand biomolecules' As with other hard-to-intuit quantities in science, one could look up biological rates using resources like PubMed or BioNumbers, to discover facts like 'water flows through aquaporin at 100 million molecules per second,' or 'yeast transcribes RNA at 0.12 molecules per minute.' But knowing a number doesn't necessarily give one a feel for it. Are those rates' fast' How do they compare to protein folding' Or enzymatic activity' Or squeezing a muscle' We can answer this question with a quantitative metaphor, by visualizing the most important goings-on of a typical cell slowed down to speeds that are still accurate relative to one another, but matched to what we experience in the everyday world. The slowdown factor we pick should make it easy to understand the molecular machines that run our cells ' proteins. Ideally, we would scale the fastest functionally important protein event to match the shortest unit of human perception....

When the researchers at Google DeepMind unveiled AlphaFold3 in Nature in May 2024, they did something controversial. Instead of releasing the code to enable other researchers to verify and build upon their protein structure prediction model, they restricted access via a web server. The computational biology community erupted. More than 1,000 researchers signed an open letter condemning the decision as a failure to follow scientific norms. Roland Dunbrack, a computational structural biologist who reviewed the paper, called the decision 'an incredible disservice to science.' The backlash worked, and DeepMind released the code in November 2024. When it comes to wet lab research, however, we've been withholding our 'code' for centuries with no outcry. Unlike computer code, which captures a model's process in machine-readable format, biological protocols operate through layers of human interpretation and tacit knowledge. Seemingly trivial details, such as the brand of plastic tubes used, are often lost in translation from bench to page and can hinder attempts to reproduce the results....