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Medicine meets machine — drug discovery, protein folding, antibiotics designed from scratch, and the Nobel Prizes that followed.
Google AI Co-Scientist generates and validates novel biomedical hypotheses
Google DeepMind unveiled AI Co-Scientist, a multi-agent system designed as a virtual scientific collaborator. The system autonomously generates novel research hypotheses and proposals across biomedical domains, then validates them using internal reasoning and literature synthesis before presenting them to human researchers.
Moved AI upstream in the scientific process from analysis tool to active hypothesis generator, demonstrating a new collaborative model for accelerating biomedical research.
Google Research BlogFirst AI-designed drug shows positive Phase IIa results in humans
Rentosertib (ISM001-055), a drug designed entirely by AI for idiopathic pulmonary fibrosis, reported positive Phase IIa clinical trial results showing improvement in lung function. The compound was discovered in approximately 18 months at a cost under $2.6 million — a fraction of typical drug development timelines and budgets.
Provided the first clinical evidence that an AI-discovered and AI-designed small molecule can produce measurable therapeutic benefit in humans, moving AI drug discovery beyond benchmarks into real patient outcomes.
AION LabsGenerative AI designs novel compounds that kill drug-resistant bacteria
MIT researchers used generative AI to design entirely new chemical compounds effective against drug-resistant bacteria. Unlike previous AI-assisted antibiotic discovery that screened existing libraries, this approach generated novel molecular structures from scratch, targeting specific bacterial vulnerabilities identified by the model.
Demonstrated that AI can move beyond screening known molecules to designing entirely new ones, opening a generative approach to antibiotic development against superbugs.
MIT NewsAI designs first personalised mRNA cancer vaccine for a dog
Sydney tech entrepreneur Paul Conyngham used ChatGPT to identify candidate tumour antigens and AlphaFold to model the resulting proteins, producing the design for a personalised mRNA cancer vaccine for his dog Rosie in under two months. UNSW researcher Pall Thordarson synthesised and administered the vaccine, and by February 2025 Rosie's leg tumour had shrunk by 75%.
The first known case of an AI-designed personalised cancer vaccine being successfully used in a live animal, demonstrating that the same mRNA platform behind COVID-19 vaccines could be rapidly adapted to individual cancer profiles at low cost.
FortuneNobel Prizes in Physics and Chemistry awarded to AI pioneers
The 2024 Nobel Prize in Physics was awarded to John Hopfield and Geoffrey Hinton for foundational work on artificial neural networks. The Chemistry prize went to David Baker for computational protein design, and to Demis Hassabis and John Jumper of DeepMind for AlphaFold — an AI system that solved the 50-year protein structure prediction challenge. It was the first time AI methods earned Nobel recognition in two disciplines simultaneously.
Established AI as a Nobel-calibre scientific methodology, recognising both the foundational neural network research and the applied scientific breakthroughs it enabled.
Nature Machine IntelligenceAlphaFold 3 predicts protein-DNA, protein-RNA, and drug interactions
DeepMind released AlphaFold 3, expanding beyond protein structure prediction to accurately model interactions between proteins and DNA, RNA, and small-molecule drugs. The system achieved a 50% accuracy improvement over AlphaFold 2, enabling prediction of the molecular interactions that underpin most biological processes.
Extended AI's reach from predicting single protein shapes to modelling the full complexity of molecular biology, accelerating drug discovery and fundamental research across the life sciences.
NatureAI identifies a new structural class of antibiotic candidates
MIT researchers used deep learning to identify an entirely new structural class of antibiotic compounds effective against methicillin-resistant Staphylococcus aureus (MRSA). The AI model not only found the candidates but also explained its reasoning, revealing the chemical substructures responsible for antimicrobial activity — a key advance in interpretable AI for drug discovery.
Showed that AI-driven drug discovery can be both effective and interpretable, addressing a major criticism of black-box approaches in pharmaceutical research.
MIT NewsAI discovers abaucin — a new antibiotic targeting a critical superbug
MIT researchers used AI to discover abaucin, a novel antibiotic compound effective against Acinetobacter baumannii — classified by the WHO as a critical-priority superbug. The AI screened thousands of compounds and identified one that kills the pathogen by disrupting its lipoprotein trafficking, a mechanism the model identified without human guidance. The compound was validated in mouse models.
Demonstrated that AI can discover antibiotics with narrow-spectrum activity against specific superbugs, an approach that reduces resistance risk and addresses one of the most urgent threats in global public health.
MIT NewsAlphaFold predicts the structure of virtually every known protein
DeepMind released predicted structures for nearly all 200 million proteins known to science — the entire protein universe catalogued in UniProt. The AlphaFold Protein Structure Database expanded from 1 million to over 200 million entries in a single release, freely accessible to researchers worldwide.
Gave every biologist on Earth instant access to protein structures that would have taken centuries to determine experimentally, fundamentally altering the pace of biological and medical research.
NatureAlphaFold 2 released open-source — solving the protein folding problem
DeepMind open-sourced AlphaFold 2 and its full training code, making its protein structure prediction capabilities freely available to the global research community. The system predicts 3D protein structures to near-experimental accuracy, a problem that had resisted solution for 50 years. The work later contributed to the 2024 Nobel Prize in Chemistry.
Democratised one of the most significant scientific breakthroughs of the century, enabling researchers worldwide to predict protein structures in minutes rather than months or years of lab work.
NatureAI discovers halicin — a new antibiotic effective against resistant bacteria
MIT researchers trained a machine learning model to identify chemical structures that could inhibit bacterial growth, then screened over 100 million compounds. The model discovered halicin, a molecule with a novel mechanism of action effective against drug-resistant strains of E. coli, C. difficile, A. baumannii, and M. tuberculosis. The compound was named after HAL from "2001: A Space Odyssey."
Marked the first time AI autonomously identified a genuinely novel antibiotic compound, proving that machine learning could find drugs that traditional methods had missed — and launching the field of AI-driven antimicrobial discovery.
MIT News