Traditional methods like crystallography are slow and analyze purified proteins outside their native environment. A-muto's platform uses proteomics and AI to analyze thousands of protein conformations in living disease models, capturing a more accurate picture of disease biology and identifying novel targets.
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AI modeling transforms drug development from a numbers game of screening millions of compounds to an engineering discipline. Researchers can model molecular systems upfront, understand key parameters, and design solutions for a specific problem, turning a costly screening process into a rapid, targeted design cycle.
The power of AI for Novonesis isn't the algorithm itself, but its application to a massive, well-structured proprietary dataset. Their organized library of 100,000 strains allows AI to rapidly predict protein shapes and accelerate R&D in ways competitors cannot match.
The cost to generate the volume of protein affinity data from a single multi-week A-AlphaBio experiment using standard methods like surface plasmon resonance (SPR) would be an economically unfeasible $100-$500 million. This staggering cost difference illustrates the fundamental barrier that new high-throughput platforms are designed to overcome.
The company focuses on disease-specific 3D protein conformations, which exposes new binding sites (epitopes) not present on the same protein in healthy cells. This allows for highly selective drugs that avoid the toxicity common with targets defined by genetic sequence alone.
Unlike traditional methods that simulate physical interactions like a key in a lock, ProPhet's AI learns the fundamental patterns governing why certain molecules and proteins interact. This allows for prediction without needing slow, expensive, and often impossible physical or computational simulations.
Instead of building from scratch, ProPhet leverages existing transformer models to create unique mathematical 'languages' for proteins and molecules. Their core innovation is an additional model that translates between them, creating a unified space to predict interactions at scale.
A major challenge in phenotypic drug screening is determining a compound's mechanism of action. AI models can analyze the complex visual data of cellular condensates after drug treatment, extracting maximal information to understand how the drug is actually working inside the cell.
ProPhet's strategy is to focus on 'hard-to-drug' proteins, which are often avoided because they lack the structural data required for traditional discovery. Because ProPhet's AI model needs very little protein information to predict interactions, this data scarcity becomes a competitive advantage.
Following the success of AlphaFold in predicting protein structures, Demis Hassabis says DeepMind's next grand challenge is creating a full AI simulation of a working cell. This 'virtual cell' would allow researchers to test hypotheses about drugs and diseases millions of times faster than in a physical lab.
The company's core technology, AlphaSeq, uses engineered yeast mating as a proxy for protein binding. The rate of mating corresponds to the binding affinity of proteins on the cell surfaces. By sequencing the resulting cells, the company can count genetic barcodes to quantitatively measure millions of protein-protein interactions at once.