Biotech

“The White House Office of Science and Technology Policy (OSTP) will convene an interagency group to monitor advancements at the intersection of biological sciences and artificial intelligence, including in silico life sciences research,” the guidance says.

Landmark project that solved protein folding gives way to a wider race to build AI systems for scientific discovery

Agriculture is increasingly characterized by a data paradox, while the sector generates massive volumes of genomic, climatic, remote sensing, and field data. Translating this information into actionable, farm-level insights remains a critical bottleneck. Traditional advisory mechanisms cannot operate at the spatial scales or provide the context-specificity needed for climate adaptation and food security. The work presents GenAI as a transformative interface that makes advanced agricultural scien

IntroductionIn recent years, high-dimensional clinical and genomic data have gained significant importance for prognosis and personalized medicine in breast cancer. But the use of quantum machine learning (QML) on such data is limited by the availability of few qubits, the computation time of quantum simulation, and dimensionality reduction. This work systematically compares several QML architectures for breast cancer classification in the presence of realistic and simulator constraints.MethodsT

A new field report shows how scientists use AI coding agents to modernize scientific computing, accelerating software development and discovery in genomics and beyond.

Scientist have developed a host of AI ‘protein language models’ that can create proteins from scratch. But Raygun can modify existing proteins, using some of the same steps as natural evolution: ...

Anthropic CEO Dario Amodei is once again warning about the risks of open AI models while insisting he has never called for a ban. He argues that authoritarian states like China could overtake the US and that open models could be misused for biological or cyberattacks. Critics say he's mostly trying to protect his own business from cheaper competition. The article Anthropic CEO Amodei doubles down on open-weight risk stance while insisting he never called for a ban appeared first on The Decoder .

To make sense of some characteristics that species exhibit from birth, you have to turn to an old discredited theory known as Lamarckism.

Multimodal drug discovery enables drug representation learning beyond chemical structure by incorporating cellular responses such as gene expression and cell morphology. However, direct fusion and instance-level contrastive alignment may mix mechanism-related signals with modality-specific noise and incorrectly separate structurally dissimilar but biologically related compounds. This limitation can obscure transferable mechanism patterns required for predicting the properties of unseen compounds

arXiv:2607.22607v1 Announce Type: new Abstract: The prospective clinical evaluation of artificial intelligence in medicine has expanded rapidly, but the global AI clinical trial landscape remains incompletely characterized. We systematically identified AI-related trials registered in ClinicalTrials.gov using a broad keyword search followed by an LLM-based classifier. Each trial was classified across seven dimensions: clinical function, data modality, specialty, AI integration and autonomy, workf

arXiv:2607.23566v1 Announce Type: cross Abstract: Traditional human marking in upper-secondary STEM education creates a structural bottleneck that restricts the frequency of formative mock examinations. This quasi-experimental, mixed-methods longitudinal study (N = 142) investigates the efficacy of deploying a fully automated, handwritten assessment marking platform to remove this bottleneck. Students preparing for STEM A-levels (Mathematics, Further Mathematics, Biology, Chemistry, Physics) wer

IntroductionBreast cancer remains one of the leading causes of cancer-related mortality rate worldwide, and the identification of effective drug combinations is an essential requirement in pharmaceutical research. The integration of Artificial Intelligence (AI) in processing large volumes of chemical and biological data combines molecular representation, predictive modeling and structured support within a single accessible tool, which accelerates early-stage candidate identification for breast c

Imagine a healthcare system made up of multiple AI agents: one that manages symptom assessment, another scheduling, a third insurance, and a fourth pharmacy. Each is an expert in its domain. But they all have their own distinct knowledge and objectives. Today they can exchange data, but they are not yet able to actually coordinate…

Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage. Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law. Today, bringing a new drug to market takes an average of 10-15 years and costs…

The offensive potential is no longer theoretical. We need to develop systems to strengthen public health as quickly as AI is accelerating biological design As artificial intelligence rapidly transforms the biological sciences, it is pushing the future of biology in two opposing directions. AI can help bad actors generate recipes for biological weapons with just a few keystrokes and computational prompts. At the same time, AI can track disease outbreaks and deliver critical public health informat

Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology. Unlike bulk assays, scRNA-seq captures heterogeneous cellular states and rare subpopulations, but this same heterogeneity makes target discovery highly sensitive to analytical choices throughout the pipeline, including preprocessing, cell population selection, differential expression analysis, and downstream biological interpretation. As a result, existi

In summer 2025, OpenAI internally flagged GPT-5 as high-risk because it helped users create biological hazards, but downgraded the model's risk rating that fall. According to the Wall Street Journal, some users got step-by-step instructions for making poisons and biological weapons. Hundreds asked for that kind of information. The article Hundreds asked ChatGPT for poison and bioweapon recipes and some got step-by-step high school level guides appeared first on The Decoder .

The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via joint sequence-structure modeling or hallucination. However, existing approaches are predominantly implemented under a single-target, single-state assumption, limiting their ability to model multi-target or multi-state interactions required for advanced function-oriented protein design. Here, we introduce Chamaileon, which

Google's AlphaFold can help ID what parts of a gene editing protein enable mistakes.

Quantum state preparation is a key component of many quantum algorithms. Performing this step efficiently is essential for realizing practical quantum advantage in quantum chemistry applications. Iterative algorithms like ADAPT-VQE can produce shallow ground-state preparation circuits, but become computationally prohibitive for the larger molecules relevant to materials science and pharmaceutical development. Here, we introduce ADAPT-GQE, a generative AI framework that learns to synthesize groun

You will get data, but perhaps not knowledge

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Multiple Sequence Alignments (MSAs) provide protein language models with explicit evolutionary context, but their large depth makes subsampling unavoidable under limited token budgets. Existing strategies, including random selection, identity-based filtering, and diversity-driven sampling, are effective heuristics, yet provide limited control over the evolutionary signals retained in the subset. In this work, we recast MSA subsampling as an explicit optimization problem, where key evolutionary m

Molecular glue degraders have emerged as a promising strategy for targeted protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target protein. Despite their therapeutic potential, computational design of molecular glues remains largely unexplored. Unlike conventional structure-based drug design, molecular glue design is governed by the unknown protein-protein interface and requires the simultaneous modeling of ligand generation, protein-protein docking,

In a time of belt-tightening and America’s protein obsession, pork should be on a winning streak. It’s cheap compared with beef, which is climbing to record high prices, and it’s the type of clean ...

The group met on July 23 and 24 to vote on whether compounding pharmacies could legally dispense certain peptides.

An investigation by Science and Retraction Watch has uncovered details about a clinical trial that resulted in the death of its sole patient: a 6-year-old girl with a rare genetic mutation affecting her cognitive development. Her death has never been reported publicly, even though the medical team published its preclinical work in Nature earlier this … Continue reading Exclusive: A couple paid more than $800,000 for a gene-editing therapy for their daughter. She died, and it wasn’t made public

Scientists have exploited a peculiar CRISPR enzyme so that it fights cancer by shredding the DNA in cancer cells, causing them to self-destruct. The enzyme can be programmed to recognize a specific ...

Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to…

This survey explores how machine learning and artificial intelligence (AI) can be integrated with mechanistic models to create more accurate, dynamic, predictive, and personalized representations of biological systems, commonly referred to as digital twins (DTs). Mechanistic models, such as pathway-based Boolean or differential equation frameworks, provide interpretable insights into biological processes; however, calibrating these models to represent individual variability across large, heterog

By identifying the genes and molecules crucial to resistance in American chestnuts and other species, researchers hope to use genomic techniques to breed trees that can flourish in the face of blight.

Antibodies are essential proteins that play a central role in immune recognition by binding specific antigen molecules. Although recent protein language models have enabled progress in single-chain protein modeling and generation, they often fall short in antigen-specific antibody design, where effective modeling requires explicit pairing between antibody and antigen, particularly at the epitope level. To address these limitations, we introduce AAMFM, an Antigen-specific Antibody Multimodal Foun

Inside the lab of a biologist who is seeking to understand intelligence by focusing on problem-solving, not neurons - by Aeon Video Watch on Aeon

House Republicans Move to Codify the Definition of Biological Sex jessica.blake@… Wed, 07/22/2026 - 03:00 AM Democrats argue the bill will actually strip LGBTQ+ students of civil rights protections, going beyond what the Supreme Court intended. Byline(s) Jessica Blake

Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services. Intelligent services in this context refer to privacy-sensitive distributed decision systems, such as fraud detection and genomic classification, where reliable and fair client-level learning is as important as the accuracy of the aggregate model. However, heterogeneous client data and noisy quantum optimization often caus

Global biotech firms are increasingly being drawn to Hong Kong for its growing investor base and proximity to Chinese pharmaceutical partners.

Xaira Therapeutics is all in on data generation for model building! We talk with Bo Wang and Ci Chu about how and why.

The Variational Quantum Eigensolver (VQE) is a leading algorithm for estimating molecular ground-state energies on near-term quantum hardware, with applications spanning quantum chemistry, materials science, and drug discovery. As VQE workloads are increasingly deployed through cloud-based ``VQE-as-a-service'' pipelines, they become exposed to adversaries such as compromised service components, malicious co-tenants, or insiders in the transpilation stack, any of which can corrupt results before

As pathogen genomic surveillance scales, the bottleneck is shifting from data generation to analysis. We present BioSecBench-Surveillance, a verifiable benchmark of 100 evaluations testing whether AI agents can infer the right analysis pipeline from raw sequencing data and surveillance context. Each evaluation gives an agent only the data and context a human analyst would have, then grades its structured answer deterministically. The tasks span seven categories, from taxonomic classification to