Summer is peak tick season, and with it comes familiar threats like Lyme disease and Rocky Mountain spotted fever. But scientists say another group of tick‑borne pathogens is quietly gaining ground: nairoviruses, a diverse family of negative‑sense RNA viruses carried by ticks across Asia, Europe, Africa—and now the western United States. Several nairoviruses can infect humans, causing high fevers, severe headaches, and, in some cases, organ dysfunction. One member of the family, Crimean‑Congo hemorrhagic fever virus (CCHFV), is often fatal and considered a global‑level threat.
Nairoviruses are found in ticks that feed on wildlife, livestock, and people. Recent human infections have been documented in China and Japan, including Songling virus (SGLV), Tacheng tick virus 1 (TTV1), and Yezo virus (YEZV). A related virus, Beiji virus (BJNV), caused an outbreak involving more than 100 patients in northeastern China. And on the U.S. West Coast, researchers recently identified Pacific Coast Tick nairovirus (PCTNV) in Dermacentor occidentalis, a tick already known to transmit Rocky Mountain spotted fever. As the paper noted, “Pacific Coast Tick nairovirus… was recently identified in Mendocino, California, from tick species known to harbor human pathogens and having a large presence across the state.”
The new study, titled “Insights into the Structure and Function of the OTU Protease Virulence Factors from Emerging Human Nairoviruses,” was published in ACS Infectious Diseases and reveals how these emerging viruses may slip past human immune defenses. All orthonairoviruses encode a specialized enzyme called ovarian tumor protease (OTU), which can remove small protein tags—ubiquitin and ISG15—from human proteins. Those tags normally act as alarm signals that activate immune responses, and removing them effectively evades the immune system. As the paper explained, “OTUs exhibit varying levels of deubiquitinating (DUB) and deISGylating activities that facilitate viral immune evasion, establishing them as key virulence factors.”
In the work, researchers isolated OTU proteases from four emerging nairoviruses—SGLV, TTV1, YEZV, and PCTNV—and compared their ability to strip immune‑signaling proteins. The standout was PCTNV, whose enzyme showed the strongest ability to remove both ubiquitin and ISG15. That suggests PCTNV may be unusually adept at evading human immunity, raising concerns because the virus is carried by a human‑biting tick common along the Pacific Coast.
The team also resolved high‑resolution crystal structures of several OTU proteases. These structural insights allowed the researchers to train computational models that begin to predict which nairoviruses may pose the greatest threat. “The biochemical and structural insights provide a path forward for predicting OTU activity among current and emerging nairoviruses,” the authors wrote.
Such predictive tools could help public‑health agencies monitor new tick‑borne viruses before they spread widely. As corresponding author Scott D. Pegan, PhD, of the University of California, Riverside, noted, “This study reinforces the need to be vigilant about not just tick bites but the type of ticks that an individual has been bitten by, as they may carry diseases beyond what we have been used to looking for.”
Background: Medical and welfare facilities in the Noto region of Japan were severely affected by the 2024 Noto Peninsula earthquake and subsequent torrential rains. Staff working in these facilities were disaster survivors and frontline caregivers with limited psychological support. Nonverbal social robots may provide companionship and emotional comfort; however, their effects on the health-related quality of life (QoL) and well-being of care staff in disaster-affected settings remain unclear. Objective: This study explored whether introducing a nonverbal artificial intelligence communication robot was associated with changes in health-related QoL and well-being among care facility staff working under disaster conditions. Secondary objectives were to evaluate safety, acceptability, and intention to continue use. Methods: This pragmatic, exploratory pilot study used an ABAB design conducted between February 2025 and June 2025. After a 2-week baseline period, staff in dementia care, general care, and short-stay units underwent 2-week intervention, withdrawal, reintervention, and withdrawal phases. Questionnaires were administered at each phase end. The primary outcomes were health-related QoL (EQ-5D-5L), well-being (World Health Organization–5 Well‑Being Index), and positive mental health (Mental Health Continuum–Short Form). Friedman tests compared outcomes across the 5 phases, and effect sizes were expressed as Kendall . Safety, acceptability, and intention to continue use were compared between the first and second intervention phases using Wilcoxon signed rank tests with Bonferroni adjustment and rank-biserial correlations as effect sizes. Results: Of the 58 staff who completed the baseline assessment, 49 (84.5%) were included in the analytic sample (25 in dementia care, 12 in general care, and 12 in short-stay units). Among these participants, 40 (81.6%) were women, and 38 (77.6%) reported disaster-related damage to their homes or families. In the pooled analysis, no phase effect was observed for the EQ-5D-5L (=.10; Kendall =0.032, negligible), the World Health Organization–5 Well‑Being Index (=.70; Kendall =0.016, negligible), or the Mental Health Continuum–Short Form (=.44; Kendall =0.022, negligible). No robot-related adverse events were reported. In the dementia care unit, nominal unadjusted differences were observed for “made me feel calm” (=.045; rank-biserial correlation =0.571, large), “like” (=.03; =0.559, large), and “felt at peace” (=.02; =0.718, large); however, none remained statistically significant after Bonferroni correction. Conclusions: The short-term use of a nonverbal artificial intelligence communication robot did not measurably improve health-related QoL or well-being among staff in disaster-affected care facilities. Deployment appeared feasible and was not associated with reported adverse events, but efficacy as a mental health support intervention remains unproven. Exploratory acceptability and interaction signals may inform future adequately powered studies.
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Background: Smartphone-based digital phenotyping has emerged as a promising approach for monitoring mental health using passive behavioral data. Prior studies have linked smartphone-derived features to depression and anxiety severity; however, knowledge regarding whether short-term changes in symptoms can be captured using passive smartphone data in general population samples remains limited, as does the understanding of how such findings should be interpreted vis-à-vis behavioral patterns and demographic variability. Objective: This study aimed to model short-term changes in depression and anxiety severity using passive smartphone data, examine model performance across demographic subgroups, and identify behavioral patterns associated with symptom changes. Methods: We collected 2 weeks of smartphone usage data from 95 adults in the general population and assessed depressive and anxiety symptoms using the clinician-rated Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale, respectively. Behavioral features—including physical activity, app use, and screen usage metrics—were extracted and compressed using an autoencoder and principal component analysis. The resulting features—along with age, sex, and baseline Hamilton scores—were used to train random forest classifiers predicting symptom score changes (increase, decrease, or unchanged). Additionally, we examined whether model performance differed across demographic subgroups and whether models excluding baseline scores retained predictive performance, as baseline severity was expected to be a strong predictor. To add explanatory value beyond prediction, behavioral subtypes associated with symptom changes were identified by applying unsupervised clustering. Results: The model exhibited moderate performance in predicting changes in the Hamilton Depression Rating Scale (mean accuracy=0.70, mean area under the receiver operating characteristic curve=0.74) and Hamilton Anxiety Rating Scale (mean accuracy=0.65, mean area under the receiver operating characteristic curve=0.69) scores. Performance varied according to demographics, with reduced accuracy among younger adults and females, although these differences were not significant in permutation tests. Excluding baseline Hamilton scores diminished performance substantially, suggesting that baseline symptom severity accounted for a substantial proportion of the predictive performance. Clustering revealed 4 distinct behavioral subtypes according to smartphone usage patterns. A cluster characterized by structured, daytime-focused smartphone use and lower temporal entropy demonstrated greater improvement in depressive symptoms, whereas clusters with lower and irregular usage patterns exhibited minimal improvement or worsening. Conclusions: Passive smartphone-derived behavioral data demonstrated moderate ability to model short-term symptom changes in this predominantly nonclinical sample. However, a substantial proportion of the predictive performance was attributable to baseline symptom severity, underscoring that passive smartphone data may provide modest supplementary information rather than robust stand-alone predictive value. Nevertheless, clustering analyses indicated that passive data may still assist in identifying behaviorally distinct subtypes associated with different depressive symptom trajectories. These findings reflect a practical contribution to digital phenotyping research by elucidating both the potential and constraints of passive smartphone data for short-term symptom monitoring in small general population samples.
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Background: Psychological distress is common among patients with cancer, and it negatively impacts treatment adherence and quality of life. Radiotherapy, with its unique procedures, such as daily sessions and physical immobilization, may induce distress distinct from general cancer anxiety. However, existing screening tools cannot differentiate these distress sources. This study leverages online patient narratives and natural language processing to distinguish radiotherapy-specific distress from general cancer distress. Objective: This study aimed to systematically identify, differentiate, and compare the composition, structure, and emotional characteristics of general cancer distress and radiotherapy-specific distress through the analysis of large-scale online patient narratives. Methods: Using a retrospective observational design, we screened 52,831 relevant posts published between 2015 and 2025 on the online health community Reddit, ultimately including 9860 first-person patient narratives meeting the inclusion criteria. Content-based extraction revealed that the most frequently represented cancer types were head and neck (1576/6625, 23.8%), breast (1418/6625, 21.4%), and prostate (1000/6625, 15.1%), with the majority of posts written during active treatment (3587/5748, 62.4% of phase-identifiable posts). We used latent Dirichlet allocation thematic modeling for topic identification, supplemented by manual qualitative coding for thematic classification. Structural relationships between topics were analyzed via correlation heatmaps, while emotional polarity and discrete sentiments for both distress categories were quantitatively compared using VADER (Valence Aware Dictionary and Sentiment Reasoner) and RoBERTa models. Results: Radiotherapy-specific distress formed a thematically distinct and substantial domain, accounting for 50.4% (4969/9860) of all narratives, comparable to the proportion of 49.0% (4831/9860) attributed to general cancer distress. The remaining 0.6% (60/9860) was unclassifiable into either category. Thematic correlation analysis revealed that both categories exhibited high internal cohesion but weak intercategory associations, a pattern consistent with thematic differentiability between the 2 domains rather than a single undifferentiated construct. Sentiment analysis further revealed that radiotherapy-specific distress carried significantly stronger negative emotional intensity (Mann-Whitney test, <.001; rank-biserial correlation=0.34), with core emotions dominated by “fear” (2773/4969, 55.8%) and “anger/frustration” (1262/4969, 25.4%), whereas general cancer distress was more frequently expressed as “anxiety” (2183/4831, 45.2%) and “sadness” (1599/4831, 33.1%; ² test, <.001; Cramér V=0.22). Conclusions: This study provides exploratory evidence that radiotherapy-specific distress is thematically and emotionally differentiable from general cancer distress, suggesting it may constitute a distinct domain warranting targeted assessment and intervention strategies. Developing targeted assessment and care strategies addressing radiotherapy-specific challenges is essential for achieving truly patient-centered, individualized psychosocial support in oncology.
Digital cognitive interventions (DCIs) have emerged as scalable approaches for treating cognitive dysfunction across psychiatric, neurological, and aging populations. Despite growing evidence of efficacy, little is known about which intervention components drive therapeutic effects or through which neurocognitive mechanisms they operate. As a result, null findings are often difficult to interpret, making it unclear whether interventions failed to engage their intended targets, or whether the targets themselves are not causally related to meaningful outcomes. This limits intervention refinement, comparative evaluation, and precision personalization. Here, we argue that DCI research should shift from broad efficacy testing toward mechanistic trials designed to identify active ingredients—the intervention components responsible for engaging prespecified neurocognitive targets and producing clinically meaningful benefits. We propose adapting dismantling design methodology from psychotherapy research in order to integrate Research Domain Criteria constructs, mechanistic neuroscience, and high-resolution digital behavioral data to identify factors driving cognitive and functional outcomes. This approach aligns with the National Institute of Mental Health experimental therapeutics framework by explicitly linking target specification and target engagement with downstream clinical and functional outcomes. Mechanistic dismantling trials can determine whether specific DCI features, including adaptive difficulty, reward schedules, feedback contingencies, task variability, cognitive targets, and human support, are necessary, sufficient, or synergistic for engaging neural circuitry and producing durable and clinically meaningful transfer. Beyond optimizing intervention design, such studies may transform null or negative trials into mechanistically interpretable findings, while clarifying disease mechanisms and supporting the development of personalized, optimized, and usable DCIs.
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<strong>Background:</strong> Patients with facial paralysis require detailed clinical assessment and long-term follow-up to monitor facial function. The current standard of care for evaluating facial symmetry and movement uses validated clinician scoring tools such as the House-Brackmann facial paralysis score or the Electronic Clinician-Graded Facial Function Scale (eFACE). Existing tools are difficult to use in normal clinic workflows and do not provide real-time facial movement tracking, representing an unmet need. Therefore, we developed FaceADE, a novel iOS app leveraging native 3D image acquisition capabilities on the iPhone to rapidly quantify facial movement in patients with facial paralysis. <strong>Objective:</strong> This study aimed to benchmark FaceADE against 2D image analysis and establish the feasibility of measuring oral commissure movement in patients with facial paralysis and healthy controls. <strong>Methods:</strong> Patients were enrolled in a tertiary care clinic focused on facial paralysis treatment. Patients underwent image capture using the FaceADE app assisted by study team personnel. Measurements of lip commissure position and movement were obtained from 20 patients with facial paralysis and 10 healthy volunteers without facial paralysis. Measurements of lip movement and symmetry gathered from FaceADE were benchmarked against 2D measurements using open-source image analysis software (ImageJ). <strong>Results:</strong> FaceADE measurements of lip commissure position and movement showed strong agreement with 2D measurements in both healthy volunteer and facial paralysis cohorts. The intraclass correlation coefficient was 0.96 (95% CI 0.90-0.98; <i>P</i><.001) in the healthy volunteer cohort and 0.82 (95% CI 0.72-0.88; <i>P</i><.001) in the facial paralysis cohort. Bland-Altman analysis found strong agreement between the 2 methods for these measurements. The 95% CI contained 97.5% (39/40) of data points in the healthy cohort and 91.2% (73/80) of data points in the facial paralysis cohort. <strong>Conclusions:</strong> Our proposed mobile method of measuring clinically important lip commissure position and movement is feasible for use at the time of care delivery. In the future, this technology may be useful for a quantitative assessment of facial paralysis severity.
“I have this idea of a communal brain.” David Baker, PhD, told me as I sat in his office at the University of Washington (UW) surrounded by colorful and complex figurines of protein structures. It was the one-year anniversary of his Nobel Prize in Chemistry win.
Just outside his doors, a lab of more than 100 researchers was united by the shared ambition to design proteins from scratch (or de novo) for powerful applications across pharmaceuticals, vaccines, biosensors, and more. This “communal brain” housed at the UW Institute for Protein Design (IPD), where Baker led as director, was hard at work developing deep learning methods that could achieve atomic precision.
A small protein composed of 100 amino acids had an astronomical 20¹⁰⁰ possible sequences. Yet, only a vanishingly tiny fraction could fold into stable, functional structures.Misplacing a residue by an angstromcould mean the difference between a drug binding tightly to its target or complete failure.
For the antibody drug market worth hundreds of billions of dollars, Nathaniel Bennett, PhD, former postdoctoral researcher in the Baker lab, says AI-guided antibody design that bypasses the need for time-consuming experimental screens has long been a “holy grail” for a breadth of indications, including cancer and autoimmune disease.
Last November, Bennett and colleagues published a Nature paper demonstrating that full length de novo antibodies could bind user-specified epitopes. AI models could now construct antibody loops, the key region involved in binding that has been historically challenging to design due to its flexible nature.
Despite this technological leap, AI-designed proteins that were manufacturable, remained stable in the body, and avoided unwanted side effects, were still a step away. The gap fueled an industry debate over whethergenerating de novo medicines was even possible.
When I asked Baker to separate the hype from reality, he didn’t hesitate.
“The reality is that we can now design proteins on a computer,” Baker explained in our video interview.“The hype is that for therapeutics, there’s a lot more than the basic activity of a protein binding or catalyzing a reaction. Whether de novo proteins will revolutionize medicine will require improving our understanding of the biology.”
Nobel guests
Bennett is continuing molecular design research as a co-founder at Xaira Therapeutics. The AI-focused biotech launched in 2024 with over $1 billion in total funding and a star-studded leadership team, including Baker, as a scientific advisor, and Marc Tessier-Lavigne, PhD, former president of Stanford and CSO of Genentech, as CEO. Carolyn Bertozzi, PhD, Nobel laureate in chemistry, Scott Gottlieb, MD, former FDA head, and Alex Gorsky, former CEO of Johnson & Johnson,are among the board of directors.
“Science all becomes obsolete quickly because the field’s moving!” Baker told me. “The people that you mentor are more important than any science you do. They all go on and do great things.”
2024 Nobel Week was a testament to Baker’s scientific reach. Nearly 200 current and former members of his lab gathered in the Grand Hôtel in Stockholm to celebrate the newly named laureate, who was among a cohort of renowned AI experts who swept the awards ceremony.
Baker shared the Nobel Prize in Chemistry with Google DeepMind duo, CEO Demis Hassabis, PhD, and then-senior research scientist, John Jumper, PhD, whose AI model, AlphaFold, solved the protein structure prediction problem and has become one of the most widely adopted computational tools for drug discovery.
Meanwhile, the Nobel Prize in Physics was jointly awarded to Geoffrey Hinton, PhD, professor emeritus at University of Toronto, and John Hopfield, PhD, professor emeritus at Princeton University, for foundational discoveries that enabled machine learning with neural networks.
Together, the prizes represented a pivotal moment. AI was no longer confined to computer science but had become a transformative force across disciplines, earning recognition as a breakthrough deemed to confer the “greatest benefit to humankind.”
Back at the IPD, Baker’s research group spanned multiple floors. Yet, he knew everyone’s name, where they sat, and moved easily between conversations, bringing together researchers whose expertise might unlock a new direction. In the weeks after receiving the historic Nobel call, Baker chose to remain fully present for his team, implementing a strict “no travel rule,” despite the avalanche of invitations and media attention that accompanied the prize.
“David’s really good at forcing you to break the ice with people,” said Seth Woodbury, a graduate student who is designing metallohydrolases, enzymes that cleave some of the strongest bonds in biology for sustainability applications, including degrading pollutants. “Once you talk to your colleagues at happy hour, it’s not so scary to go ask them a question.”
Woody Ahern, graduate student and co-author of the metallohydrolase Nature paper, adds that Baker has a “very reasonable disdain for hierarchy.”
“Anyone can speak up in meetings. Anyone can question the work. It breeds this culture of staying focused on what matters in an interdisciplinary way,” said Ahern.
When Ria Sonigra was applying to graduate schools in the U.S., every option felt equally far from her home in India. She recalled sending Baker a cold email with questions about the lab. He quickly replied and offered to connect her with another international student who could help her navigate the application process. Today, Sonigra is an IPD graduate student, designing programmable nanopores for molecular sensing and sequencing.
“People outside the lab may think that David can’t pay attention to everyone, which is not true,” Sonigra said. “He knows your project and what he expects of you before the next meeting, even if he has a hundred trainees.”
At one point, Baker waved me over with a smile. “You’re missing chocolate hour!” he said, inviting me to one of many small weekly rituals that embodied the collaborative culture he had built.
“My group was not the first to do protein design,” he said humbly.
The field’s early innings trace back to 1988, when William DeGrado, PhD, demonstrated that sequences not found in nature could achieve stable 3D folds.The work challenged the long-held belief that functional proteins could only arise through evolution.
Steps toward computational design came a decade later, when for the first time, an in silico predicted protein was experimentally validated to adopt a target structure. The work was published in a Science study led by Steve Mayo, PhD.
Baker, alongside then-postdoctoral researcher, Brian Kuhlman, PhD, went a step further in 2003, expanding the design scope to include flexible backbones that represented entirely new folds, making it possible to not only modify natural proteins, but to create new ones from scratch.
“The prize was given because protein design has so much promise now, and that reflects the work of the whole community,” Baker continued.
Today, Degrado, Mayo, and Kuhlman are continuing to advance structural biology as prominent faculty members across University of California, San Francisco (UCSF), California Institute for Technology, and University of North Carolina (UNC) Chapel Hill, respectively.
Top7 was the first protein created on a computer with a custom amino acid sequence that folds into a never-before-seen structure. When viewed at an angle, the transparent partition allows the two forms to become superimposed, illustrating the beauty of uniting sequence and structure. [UW Institute for Protein Design]
Decades before OpenAI co-founder, Andrej Karpathy, coined the term “vibe coding,” Baker’s team was writing a program in FORTRAN. NamedRosetta, the molecular modeling suite simulated proteins atom-by-atom based on biophysical properties, from hydrogen bonds to backbone torsion angles.By calculating free energy, Rosetta could estimate which sequences were most likely to achieve a desired structure: the lower the energy, the more stable the predicted fold.
Yet, a protein’s energy landscape is rugged, with countless local minima among an astronomical number of conformations. Success was rare. Researchers were searching for a single grain of sand across the desert.
Still, “Rosetta was impressive,” said Sierin Lim, PhD, an associate professor at Nanyang Technological University, who is among a group of researchers engineering self-assembling nanoscale containers, known as protein cages, for applications across drug discovery, imaging, and materials science. She recalled watching molecules move on her screen in Singapore in the early 2000s. At the time, Rosetta was the only program that could model proteins.
Over the next twenty years, Baker adamantly pushed Rosetta to be openly available, inviting collaborators to not only use the software, but to improve it.
PyRosetta, a user-friendly Python-based implementation developed by Johns Hopkins University researchers led by Jeffrey Gray, PhD, broadened Rosetta’s access for structural biologists without a strong computational background. Meanwhile, progress in generating high affinity and selective ligand binders and epitope scaffolds for vaccine development were bringing computational proteins closer to real-world medicines.
What started as a single lab project grew into the Rosetta Commons, an international collaboration spanning more than 100 laboratories.
“It was a great move making Rosetta open, seeing what it can do now,” Lim said.
Researchers from Google introduced the transformer, a neural network architecture that enabled machines to analyze entire sequences at once. By using a “self-attention” mechanism, AI models could now uncover patterns across massive datasets at unprecedented scale. Soon, large language models (LLMs) trained on internet-scale text could not only understand, but converse in eloquent dialogue with humans.
The generative AI era had begun.
While the rest of the world was captivated by chatbots, structural biologists were sitting on a treasure trove of biological data pristine for machine learning.
For over fifty years, researchers had painstakingly deposited hundreds of thousands of experimentally determined structures in the Protein Data Bank (PDB) for public use. This molecular atlas now offered AI a window into the rules of biology.
In 2020, Baker received a phone call from one of the organizers of the Critical Assessment of protein Structure Prediction (CASP) competition, the biannual experiment that assesses the field’s latest state-of-the-art models.
“The first thing he said was, ’David somebody has done amazingly well this year, and it isn’t you!’” Baker recalled during his Nobel banquet speech. “That was how I first learned about the work of Demis and John.”
Instead of relying on human-defined biophysical rules, AlphaFold quickly learned decades of biochemistry from the PDB, uncovering the hidden instructions governing an amino acid sequence to fold into its 3D shape. At CASP14, the model remarkably predicted structures that were indistinguishable from real-world proteins. Months of laboratory work turned into a computational task completed in minutes.
Hassabis was quick to translate the breakthrough into medicine, taking the helm of DeepMind’s drug discovery spinout, Isomorphic Labs, as CEO a year later.
Today, the company’s IsoDD (Isomorphic Labs Drug Design Engine) platform, expands the druggable landscape by probing previously inaccessible biology, including predicting induced-fit interactions, where proteins change shape upon ligand binding, and identifying hidden binding pockets for drug targeting.
Isomorphic was betting, not on single therapeutic assets, but on a general discovery engine applicable across any disease area. That vision has since secured major pharma partnerships with Novartis, Eli Lilly, and Johnson & Johnson.
“I’ve always believed the No.1 application of AI should be to improve human health,” wrote Hassabis on LinkedIn when announcing Isomorphic’s whopping $2.1 billion funding raise in May.
Diffusion evolution
Concurrently, Baker’s team began applying deep learning to de novo design, drawing inspiration from AI’s emerging ability to generate realistic images. These diffusion models could operate on atomic coordinates and create entirely new protein backbones. Designs were conditioned for desired structural and functional constraints, opening the door to programmable biology.
When Baker’s team presented de novo design model, RFdiffusion (RoseTTAFold diffusion), in Nature in 2023, Mohammed AlQuraishi, PhD, assistant professor of systems biology at Columbia University, described the advanceas “a really big deal.”
‘‘Prior to the ‘diffusion evolution’, the success rates were probably on the order of 1 to 10,000, if you’re lucky,’’ AlQuraishi told me shortly after RFdiffusion’s publication. ‘‘With diffusion models, the success rates are closer to the single percentages when you get into the laboratory. It’s a huge magnitude improvement of what it used to be.”
Donald Hilvert, PhD, professor emeritus at ETH Zurich, met Baker twenty years ago while working on enzyme design with Defense Advanced Research Projects Agency (DARPA). Traditional Rosetta methods would carve out binding pockets in existing proteins and install a new catalytic apparatus.
“But the activities were not very good,” Hilvert recalled. Designing catalysis, where success depended on precisely positioning chemical groups to stabilize fleeting transition states, proved far more difficult than engineering a stable protein fold.Rosetta struggled to achieve that level of accuracy, prompting much of the field, including Baker, to turn attention elsewhere.
“Two years ago, David called me and said, ‘Why don’t you come and visit? All these new AI-driven techniques are really changing the game!’” Hilvert told me.
Hilvert has spent the past two summers at the IPD, collaborating with Woodbury, Ahern, and IPD postdoctoral researcher, Donghyo Kim, PhD, to design metallohydrolases using RFdiffusion. He “hardly knew how to turn on a computer,” yet was reading Python scripts and generating his first computational designs within weeks. To his amazement, experiments quickly yielded five or six promising hits.
“There is this common purpose of people helping one another,” Hilvert said. “David sets the tone from the top.”
Application generalist
As I walked through the halls of the IPD, I saw the extraordinary reach of protein design applications firsthand. Desks were intermingled across fields. The proximity was deliberate for ideas to travel as far as possible.
Florence Hardy, PhD, is a postdoctoral researcher tackling a new enzyme design project for global health applications, including streamlining the manufacturing process for therapeutics.
“I always say that I can only think in a ten angstrom sphere at a time,” she chuckled. “That’s just as big as the active site.”
“Most medicines focus on inhibitors,” Xinru Wang, PhD, explained when describing her postdoctoral research developing insulin agonists, or binders that lead to activation, to address metabolic disease. In contrast to blocking activity, “turning on” a signaling complex required precise structural tuning that was a natural fit for the IPD’s expertise.
Last November, Wang and colleagues published a study in Molecular Cell, demonstrating that de novo designed insulin receptor (IR) agonists could extend glucose-lowering effects. The findings offered a therapeutic alternative to escalating insulin doses, which is a known contributor to resistance. Notably, these engineered agonists avoided triggering cancer proliferation that is often associated with excessive insulin activation. Wang is currently an assistant professor at Northeastern University.
Tabitha Tcheau designs DNA binding proteins inducible with small molecules that can recognize novel pathogens and trigger the plant immune system. The highlight of her project, she says, is the ability to span interdisciplinary subgroups, from conformational dynamics, small molecules, and nucleic acids.
“One thing that blew me away here is that people are extremely supportive,” Tcheau told me. “Everyone you ask is super eager to help.”
Enisha Sehgal is among a team of researchers designing sequence specific DNA binding proteins that can power programmable transcription factors, targeted gene regulation, and new genome engineering tools.
“Being in this lab allows you to be a specialist in protein design, but a generalist in all the applications,” Sehgal said. “You get answers faster. You can iterate faster. Science moves faster.”
Visiting researcher and machine learning scientist, Kieran Didi, reiterates how the IPD’s interdisciplinary team enables rapid experimental validation of models. “I’m not going to spend two months in this fantasy world of computational benchmarks,” he said. “In the next week, I know if the model is actually working. Someone will quickly put it to the reality test.”
Postdoctoral researcher and chemist, Declan Evans, PhD, concurs and sees himself as the Alpha tester.
“I can go straight to the developer and say, ‘this is not how computational chemists would use this software,’” Evans said. “You can see changes being made in real time.”
Back in Baker’s office, he told me about his regular weekend escape to the mountains, one of the benefits of living in Seattle. Skiing and hiking were activities he valued highly. When asked to contribute an item to the Nobel Prize Museum, Baker chose a broken ski pole as a symbol that progress often comes through overcoming setbacks.
“But I don’t think people get ideas on top of mountains,” Baker tempered. “If you’re going to be a [principal investigator], you have to really like mentoring. For me, it’s super fun!”
Baker’s most enduring creation may not be any single protein, but rather the network he built—the diverse, inviting, and interconnected communal brain.
Women who undergo medically assisted reproduction (MAR) may have a slightly higher risk of developing certain hormone-related cancers, but a large Australian study suggests much of that increase is likely explained by underlying infertility, greater medical surveillance, and other patient characteristics rather than the fertility treatments themselves.
The findings, published in JAMA Network Open, analyzed data from nearly 1.75 million Australian women and represent one of the largest investigations to date of cancer risk following fertility treatment. Using an emulated target trial design—a statistical approach intended to better approximate the conditions of a randomized clinical trial—the researchers examined whether three common forms of MAR were associated with later cancer diagnoses.
The study included 1,748,927 women aged 18 to 55 years between 1991 and 2018, including 396,661 who had received some form of MAR. Treatments evaluated included assisted reproductive technology (ART), intrauterine insemination (IUI) or ovarian stimulation, and ovulation induction with clomiphene citrate.
Researchers found modest increases in the relative risk of several hormone-related cancers—including breast, ovarian, uterine, thyroid, colorectal cancers, and melanoma—after some fertility treatments. However, the absolute increase in risk was small.
For any individual invasive cancer, the investigators estimated that treated women experienced fewer than 20 additional cancers per 100,000 women per year compared with women who had not undergone MAR.
The authors emphasized that the observed associations should not be interpreted as evidence that fertility treatment causes cancer. “Although we observed increased relative risk for most hormone-related cancers following MAR, this corresponded to only small increases in estimated absolute excess risk,” the authors wrote.
To better distinguish treatment effects from other influences, the investigators incorporated several bias analyses rarely included in previous studies. They calculated E-values to estimate the impact of unmeasured confounding and analyzed cancers not believed to be hormonally driven—including pancreatic, lung, and hematologic cancers—as negative controls.
Those analyses suggested that underlying infertility-related conditions, such as endometriosis and polycystic ovary syndrome, as well as factors including obesity, anovulation, and demographic differences, could account for much of the increased risk observed for ovarian, uterine, and thyroid cancers.
The researchers also found that cancer diagnoses tended to cluster during the first few years after fertility treatment. According to the investigators, “Several emulated trials suggested a greater likelihood of incident cancer shortly after first treatment.” They noted that this pattern could reflect either accelerated growth of preexisting cancers or increased medical monitoring during fertility treatment and pregnancy.
After examining the data, the authors concluded that enhanced surveillance was the more likely explanation. “We believe detection bias is the more likely explanation for these results. This finding is the first empirical indication using a negative control that medical surveillance may be responsible for an increased risk of cancer following MAR,” they wrote.
Women pursuing fertility treatment often undergo repeated medical evaluations and may also be more likely to participate in cancer screening programs, increasing the likelihood that existing cancers are detected earlier than they otherwise would be.
Overall, the researchers concluded that while associations between MAR and hormone-related cancers were observed, the evidence does not support a simple causal relationship.
The investigators said the findings should reassure patients while also encouraging careful counseling and follow-up. They recommend that clinicians discuss the possibility of a small increase in cancer risk but explain that the excess risk “may be partially or fully due to the health and sociodemographic profile of women who receive MAR and increased surveillance during treatment.”
The authors added that routine surveillance after fertility treatment remains appropriate, but they cautioned that observed increases in cancer diagnoses should be interpreted within the broader context of infertility-related health conditions and differences in healthcare utilization rather than being attributed solely to fertility medications themselves.
Nebius, an AI cloud company, sponsored its second “AI Discovery Awards” event and dinner earlier this month in London, where the winning companies were announced. The event highlighted leading startups in biopharma, genomics, medical devices, and digital health that are using AI to deliver advances in healthcare and life sciences.
During the evening awards ceremony, artificial intelligence demonstrated that it is rapidly reshaping biomedical research. However, practitioners agree that AI’s success depends on more than advanced algorithms. [Nebius]
At a Nebius-hosted morning discussion before the awards dinner, several researchers highlighted the importance of powerful computing infrastructure, high-quality biological data, and laboratory validation.
Examples included AI models that predict osteoarthritis years before symptoms and an Alzheimer’s platform, which achieved 97% diagnostic accuracy when paired with protein biomarkers. A Stanford Medicine scientist described CRISPR-GPT, an AI assistant that helps design and troubleshoot gene editing.
The investigators also spotlighted AI-powered lab automation, multimodal datasets, and AlphaFold’s dramatic acceleration of protein structure prediction. Across every application, participants emphasized that collaboration among academia, healthcare, industry, and governments will be essential to advance preventive, personalized medicine and to translate AI discoveries into clinical practice.
Ilya Burkov, PhD, who has a background in clinical medicine, is now global head of healthcare and life science at Nebius. Burkov began his research career focusing on osteoarthritis, osteoporosis, hip and knee replacements, and trying to figure out how such diseases develop and progress.
“My goal was to work backward from the end stage of disease and determine whether we could predict who was at risk years before serious joint damage occurred,” he explained. “If we could identify those patients early enough, perhaps we could delay disease progression.”
Machine learning
Speaking with colleagues in a hospital, he was asked: “Have you looked at it from any machine learning perspective?” Burkov had no formal background in artificial intelligence, but he was intrigued by the idea of using emerging AI models and advanced algorithms to analyze long-term medical imaging data.
Ilya Burkov, PhD [Nebius]
The concept was simple but powerful: if AI could identify patterns shared by patients who later developed osteoarthritis or osteoporosis, it might be able to detect subtle biomarkers long before the disease became clinically apparent.
“That idea became the focus of my PhD research. AI models were not a thing ten years ago when I was in the hospital. There were transformer models and algorithmic-based approaches.
“I developed techniques capable of predicting the early onset of osteoarthritis and osteoporosis with an accuracy of roughly 80% to 90%,” he pointed out. “The models identified imaging features that consistently appeared years before patients required joint replacement surgery.
“This made it possible to examine scans from otherwise healthy individuals and estimate their future risk. In some cases, we could tell patients that, without changes to certain lifestyle factors, they had a high probability of requiring a hip replacement within the next 10 to 15 years.”
For Burkov, that was transformative. AI made it possible to move beyond treating individual patients and instead create tools that could benefit entire healthcare systems. Rather than applying clinical expertise one patient at a time, scalable technologies could be created capable of helping clinicians identify high-risk patients earlier and intervening before irreversible damage occurred.
That realization ultimately convinced him to transition from clinical medicine into industry, where he saw the opportunity to build technologies that could have a much broader impact. Whether it’s a small academic lab with only a handful of researchers or a global pharmaceutical company operating at massive scale, every organization faces different computational challenges.
“At Nebius, our role is to provide the computing infrastructure and technology that enables researchers to train increasingly sophisticated AI models and accelerate scientific discovery to advance biomedical research and improve patient care,” he said.
Alzheimer’s disease
Artificial intelligence is rapidly reshaping drug discovery, but many researchers believe the greatest challenge is not designing drugs—it’s knowing what biological targets to pursue.
Prima Mente, a previous AI Discovery Award winner, is tackling that problem by building foundation AI models designed to uncover the molecular mechanisms behind Alzheimer’s disease and other neurodegenerative disorders. By combining blood-based biomarkers, multimodal biological data, and transformer-based AI, the London startup hopes to identify the molecular drivers of neurodegenerative disease—and ultimately accelerate the development of new therapies.
“If we can diagnose disease earlier, better stratify patients, and understand what’s actually driving Alzheimer’s, we can help create better treatments,” said co-founder Hannah Madan, PhD.
Based in London’s King’s Cross innovation district, Prima Mente has grown to approximately 35 employees across London, San Francisco, and the United Arab Emirates. Madan, whose academic background includes a master’s degree in pharmacology and a PhD investigating the relationship between bowel cancer and diabetes, has spent most of her career building biotechnology startups. Prima Mente is the fifth company she has helped launch.
The company’s mission addresses one of healthcare’s most pressing challenges. Dementia is the leading cause of death in the U.K. and the sixth highest in the U.S. Alzheimer’s disease remains the most common form of dementia worldwide.
Looking beyond traditional biomarkers
Prima Mente’s scientific strategy draws inspiration from advances in cancer diagnostics, particularly liquid biopsy technologies that detect circulating tumor DNA in blood samples. The company wondered whether a similar approach could work for neurodegenerative disease.
“When we started three years ago, many people thought we were a little crazy,” noted Madan. “The prevailing view was that very little DNA from dying brain cells entered the bloodstream.”
Hannah Madan, PhD [Nebius]
The team has since demonstrated that cell-free DNA originating from neurons, microglia, and astrocytes can be detected in blood. More importantly, those DNA fragments retain epigenetic information that may reveal the biological state of brain cells before they died.
Rather than focusing solely on DNA sequences, Prima Mente analyzes methylation patterns carried on cell-free DNA. Because methylation reflects how genes are regulated within specific cell types, these signals can provide insight into disease progression and cellular dysfunction.
“When cells die, they release fragmented DNA into the bloodstream,” Madan explained. “Those fragments preserve methylation signatures that tell us what state those brain cells were in.”
The biological strategy is paired with an equally ambitious computational one. Prima Mente believes that transformer architectures—the same AI technology underlying large language models such as ChatGPT—can learn the language of biology.
“If ChatGPT can understand human language, our hypothesis is that similar models can understand biological languages,” noted Madan.
Instead of converting sequencing data into simplified numerical counts, the company trains models directly on raw biological sequences, including DNA, methylation signals, RNA transcripts, and proteomic data. By preserving more of the underlying biological information, Prima Mente believes its models could uncover relationships that conventional bioinformatics pipelines often overlook.
The company’s first foundation model, known as Pleiades 1, demonstrated the potential of that approach. Initially trained to identify Alzheimer’s disease from blood-derived molecular data, the model successfully diagnosed a subset of patients. After protein biomarkers were incorporated, diagnostic accuracy increased to approximately 97% within the study dataset—exceeding the performance of current clinical standards, according to Madan.
AI tokens are the fundamental units of data processed by AI models during training and inference. They represent smaller components of text, audio, images, or other modalities, enabling models to understand, predict, and generate outputs effectively. Pleiades 1 was trained on 1.9 trillion tokens. Its successor, Pleiades 2, is being trained on 80 trillion tokens spanning five biological data modalities, with the long-term goal of building a 100-billion-parameter foundation model.
Prima Mente partnered with AI infrastructure provider Nebius, which supplied a dedicated 32-node computing cluster powered by NVIDIA GPUs. The additional computing capacity enabled the company to scale from a 1-billion-parameter model to a 10-billion-parameter model within weeks while increasing training throughput from roughly 8,000 tokens per second per device to more than 1.1 million tokens per second across a 16-node cluster.
Beyond model development, Prima Mente is collaborating with the U.K.’s National Health Service (NHS) through the Sandbox Study, which collects blood samples from patients with suspected neurological disease. The real-world data help researchers develop AI models aimed at detecting Alzheimer’s earlier, potentially enabling treatment before irreversible brain damage occurs.
Dementia is the leading cause of death in the U.K. and the sixth highest in the U.S. Alzheimer’s disease remains the most common form of dementia worldwide. [Cecille Arcurs/Getty Images]
Unlike AI companies that rely primarily on public datasets, Madan said Prima Mente is generating much of its own training data. The company collaborates with 20 NHS memory clinics throughout the U.K., collecting blood samples, speech recordings, clinical notes, and imaging data from patients at the earliest stages of cognitive decline. It also participates in the U.K.’s Sovereign AI initiative.
Lab validation is integrated into the company’s development process. Candidate discoveries generated by AI models are tested using stem cell systems, brain tissue, and additional blood-based experiments, creating a continuous feedback loop between computational prediction and experimental validation.
That combination of proprietary data generation, lab experimentation, and AI model development represents what the company views as a significant competitive advantage.
While AI has attracted enormous attention for accelerating drug discovery, Madan argues that identifying the right biological target remains the industry’s greatest bottleneck. She compares today’s AI revolution to the impact AlphaFold had on protein structure prediction. As computational tools become increasingly capable, designing drug candidates may become faster, cheaper, and more routine.
“But if you don’t know what biology actually matters,” she said, “there’s little value in having better tools to build drugs.”
For Prima Mente, Madan says the opportunity lies upstream of drug development—discovering the cellular pathways, biomarkers, and molecular mechanisms that should become tomorrow’s therapeutic targets.
That strategy recently received external validation when the company won the AI Insights Prize for Alzheimer’s from the Gates Foundation, receiving $1 million to expand research into microglial biology. The funding will support AI models designed to identify gene perturbations in specific brain cell types that could serve as the basis for future Alzheimer’s therapies.
As foundation models continue to expand beyond language into biology, Madan is betting that the next major AI breakthrough in medicine will not simply generate better drugs—it will reveal entirely new biology that makes those drugs possible.
CRISPR-GPT
CRISPR-GPT is a large language model developed by Stanford Medicine to automate key steps in CRISPR gene-editing research. Acting as an AI agent, it interprets scientific literature, designs guide RNAs, suggests experimental parameters, and integrates with lab automation systems to execute and refine experiments. By reducing manual planning and accelerating iterative testing, the system enables researchers to complete complex gene-editing workflows more efficiently and consistently, noted Stanford researchers.
CRISPR-GPT is also credited with lowering the barrier for scientists with limited CRISPR expertise, improving accessibility. The platform represents an emerging class of AI tools that can “reason” through complex scientific tasks, recommend next steps, and accelerate discovery. Potential applications include developing gene therapies, improving cancer research, engineering cell therapies, and expanding access to genome-editing technologies.
The goal, according to Le Cong, PhD, assistant professor of pathology and genetics is to help scientists produce life-saving drugs faster. “The hope is that CRISPR-GPT will help us develop new drugs in months instead of years,” he said.
Le Cong, PhD [Stanford Medicine]
Cong and team developed CRISPR-GPT using Nebius AI Cloud as its core infrastructure. The group leveraged Nebius’ GPU clusters to train their specialized CRISPR-Llama3 model, rapidly iterate on architectures, and scale from prototyping to full model training.
CHAT-GPT could also expand the pool of scientists who can effectively use gene editing technology—no experience required, pointed out Cong. “Trial and error is often the central theme of training in science, but what if it could just be trial and done?” he added. Cong is the senior author of a study “CRISPR-GPT for agentic automation of gene-editing experiments,” published July 2025, in Nature Biomedical Engineering.
AI Discovery Awards
At the AI Discovery Awards dinner in the evening, the sponsors announced that the 2026 program added medical devices and medical imaging to the existing biopharma, genomics, and digital health tracks to reflect the growing role of AI in connected medical equipment and diagnostic imaging.
“Our winners—and indeed all of the 647 submissions we reviewed—reflect how rapidly AI is changing the pace of healthcare research,” said Ilya Burkov during a short presentation. “Across all categories, startups are compressing timelines that once took years into months or even weeks, and bringing capabilities to clinical and laboratory settings that simply did not exist before.
Margaret Hua, founding chief of staff at Phylo, accepts $100,000 in GPU cloud credits for first prize in the biopharma category. The company is building AI research assistants that can independently help biomedical scientists think through problems, design experiments, analyze data, and suggest what to do next, with the aim of speeding up scientific and biomedical discovery. [Nebius]
“The AI Discovery Awards exist to accelerate that momentum, and to connect the most promising teams with the compute resources, investor networks, and mentorship they need to move from promising research to bringing products to market.”
Alongside the awards program, Nebius previewed its Nebius Scientific AI and Healthcare Platform, which is an AI infrastructure built to meet the specialist needs of healthcare and life sciences organizations, explained a Nebius official.
The 2026 AI Discovery Awards were open to companies from pre-seed through to Series D that put AI and machine learning at the core of their product. Category winners were selected from 647 applications from around the world by an independent panel of 28 judges representing leading pharmaceutical companies, academic institutions, and venture capital firms. Submissions were evaluated based on the use of AI within the product, use of compute, technical innovation, functionality and advantages, performance and efficiency, global impact, market potential, and business sustainability.
A full list of shortlisted companies, as well as qualification criteria and a jury list, can be found on Nebius’s website.
The results of a preclinical study by researchers at the University of Texas MD Anderson Cancer Center have found that an investigational epigenetic therapy called NTX-301 remained effective in treatment-resistant acute myeloid leukemia (AML) by activating the Hippo pathway, a tumor-suppressing pathway linked to cancer growth and drug resistance.
In preclinical models, the hypomethylating agent (HMA) NTX-301 was more effective than standard hypomethylating agent therapy and retained anti-leukemia activity in treatment-resistant and TP53-mutant AML. The team also found that the therapy activated the Hippo pathway through targeted epigenetic changes, revealing a previously unrecognized mechanism that may contribute to its anti-leukemia effects.
“Leukemia cells are remarkably adaptable and often find new pathways to survive after treatment,” said Michael Andreeff, MD, PhD, professor of medicine at the University of Texas MD Anderson Cancer Center and research co-lead. “These findings suggest NTX-301 may disrupt several of those survival mechanisms simultaneously while reactivating pathways that normally restrain cell growth. That dual effect could help explain why NTX-301 remained active in some of the most therapy-resistant forms of AML.”
First-generation hypomethylating agents, including 5-azacytidine (5-AZA) and decitabine (DAC), are used as standard clinical care for patients with AML and myeloid dysplastic syndromes (MDS), the authors wrote. Combining HMAs with the BCL-2 inhibitor venetoclax has further improved outcomes for patients.
“Hypomethylating agent (HMA) and the BCL-2 inhibitor venetoclax (VEN) combinations have evolved into frontline therapies for patients with acute myeloid leukemia (AML), yielding high response rates,” they stated. However, while such combination therapy works well initially, resistance and relapse remain common.
The challenge is particularly significant in AML with mutations in the TP53 gene, which normally helps cells respond to damage and prevent uncontrolled growth. When that gene is mutated, leukemia cells can become resistant to therapy and more difficult to eliminate. “… most patients ultimately relapse, particularly those with TP53 mutations,” the researchers continued.
Efforts have been made to develop improved and more effective HMAs, they noted, and NTX-301 is such a next-generation HMA. But as they pointed out, “… previous reports of NTX-301 preclinical studies in leukemia were conducted primarily in cell lines and xenograft models … its activities in therapy-resistant settings have not been investigated.” And while a Phase I study (NCT04167917) of the oral agent NTX-301in patients with AML and MDS has been completed, the team noted in their paper that the study has not yet been reported.
For their newly reported preclinical study, the researchers evaluated NTX-301 across multiple preclinical models of treatment-resistant AML, including patient-derived xenograft (PDX) models of AML with acquired resistance. Their results showed that NTX-301 therapy consistently reduced leukemia cell survival more effectively than azacitidine (AZA), a commonly used hypomethylating agent.
Importantly, NTX-301 remained active in leukemia cells that had already developed resistance to both hypomethylating therapy and venetoclax, and demonstrated anti-leukemia activity in TP53-mutant AML models. When combined with venetoclax in resistant leukemia samples, NTX-301 produced stronger anti-leukemia effects than either treatment alone. The combination was effective not only against leukemia blasts but also against leukemia stem and progenitor cells, which are believed to contribute to disease persistence and relapse.
In summary, they wrote, “Therapeutically, NTX-301 is more potent than 5-AZA in AML cells with various genetic backgrounds, is active in AML cells with acquired resistance to HMA or VEN, overexpressing VEN-resistant factors MCL-1 or BCL-2A1, and in isogenic AML cells with TP53 deletions/mutations in vitro and in vivo in xenograft models, exhibits activities against AML blasts and stem/progenitor cells from patients resistant to/relapsed from VEN-based therapies and with TP53 mutations in vitro and in vivo VEN/DAC-resistant PDX models, and enhances VEN activity.”
To understand why NTX-301 appeared more effective than existing drugs, researchers analyzed changes in DNA methylation, a process that can switch genes on or off without altering the underlying genetic code. Unlike current hypomethylating therapies, which broadly affect DNA methylation, NTX-301 focused on a more selective set of genes and pathways, including the Hippo pathway, which functions as a natural cell growth regulator.
NTX-301 increased activity of key Hippo pathway genes while reducing activity of YAP, a protein frequently linked to cancer cell survival, treatment resistance, and stemness. These findings suggest Hippo pathway reactivation may be an important reason the therapy remained effective in resistant leukemia models and could represent a new strategy for overcoming treatment resistance in AML. “Collectively, our data suggest that NTX-301 exhibits more potent anti-leukemia activities compared to current HMAs and synergizes with VEN in VEN-resistant and TP53-mutant AML and AML stem/progenitor cells,” the team concluded.
Additional studies are needed to determine whether these results translate to patients and to identify which populations may benefit most. The findings suggest that patients with relapsed AML, venetoclax-resistant disease, and TP53 mutations may be important groups for future clinical evaluation. “Taken together, the numerous NTX-301 targets identified here, its novel mechanism of action, and its superior activity against VEN-resistant and TP53-mutant AML compared to 5-AZA, warrant the future clinical development,” the investigators noted. “Given the strong preclinical data in TP53-mutant AML and the unmet clinical need, this should be a primary target group in the next clinical trial.”
Carter said, “An encouraging aspect of this study is that it identified both a potential therapeutic opportunity and a biological explanation for why it may be effective. The results provide a rationale for continued clinical development and suggest that targeting Hippo signaling may help address treatment resistance in AML.”
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![During the evening awards ceremony, event, artificial intelligence demonstrated that it is rapidly reshaping biomedical research. However, practitioners agree that AI’s success depends on more than advanced algorithms. [Nebius]](https://www.genengnews.com/wp-content/uploads/2026/07/Nebius_Highlights-28.jpg)
![Margaret Hua, founding chief of staff at Phylo, accepts $100,000 in GPU credits for first prize in the biopharma category. The company is building AI research assistants that can independently help biomedical scientists think through problems, design experiments, analyze data, and suggest what to do next, with the aim of speeding up scientific and biomedical discovery. [Nebius]](https://www.genengnews.com/wp-content/uploads/2026/07/Nebius_Highlights-33-300x200.jpg)