Artificial intelligence is already proving it can accelerate drug development and improve our understanding of disease. But to turn AI into novel treatments we need to get the latest, most powerful models into the hands of scientists. The problem is that most scientists aren't machine-learning experts. Now the company OpenProtein.AI is helping scientists stay on the cutting edge of AI with a no-code platform that gives them access to powerful foundation models and a suite of tools for designing proteins, predicting protein structure and function, and training models. The company, founded by Tristan Bepler PhD '20 and former MIT associate professor Tim Lu PhD '07, is already equipping researchers in pharmaceutical and biotech companies of all sizes with its tools, including internally developed foundation models for protein engineering. OpenProtein.AI also offers its platform to scientists in academia for free. 'It's a really exciting time right now because these models can not only make protein engineering more efficient ' which shortens development cycles for therapeutics and industrial uses ' they can also enhance our ability to design new proteins with specific traits,' Bepler says. 'We're also thinking about applying these approaches to non-protein modalities. The big picture is we're creating a language for describing biological systems.'...

Billions of years ago, simple organic molecules drifted across Earth's primordial landscape ' nothing more than basic chemical compounds. But as natural forces shaped the planet over hundreds of millions of years, these molecules began to interact and bond in increasingly complex ways. Along the way, something spectacular emerged: life. 'Life is, to some degree, magical,' says computational biologist Sergei Kotelnikov. Simple organic compounds congregate into polymers, which assemble into living cells and ultimately organisms ' the whole being greater than the sum of its parts. 'You can write formulas on how a molecule behaves,' he says, referring to the world of quantum mechanics. 'But yet somehow, a few orders of magnitude above, on a bigger scale, it gives rise to such a mystery.' Kotelnikov builds models to analyze and predict the structure of these biomolecules, particularly proteins, the fundamental building blocks of every organism. This year, he joined MIT as part of the School of Science Dean's Postdoctoral Fellowship to work with the Keating Lab, where researchers focus on protein structure, function, and interaction. Using machine learning, his goal is to develop new methods in protein modeling with potential applications that span from medicine to agriculture....

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.'...