Biotech

The fund-raising values the company at $3.8 billion and underscores investor interest in using artificial intelligence to tackle problems like drug discovery.

Settlement will drive down patients’ out-of-pocket costs, increase transparency and ensure community pharmacies are treated fairly The Federal Trade Commission secured a settlement agreement with one of the nation’s largest pharmacy benefit managers (PBMs) and its affiliated entities, marking yet another important victory in the Commission’s fight to lower healthcare costs for Americans. View Press Release

This paper proposes a theoretical framework for understanding intelligence as a process of atomic compression and compositional reuse. We argue that cognitive, biological, computational, and organizational systems achieve scalable intelligence by decomposing complex phenomena into reusable atomic units that can be recombined into higher-order structures. Drawing on evidence from cognitive science, information theory, evolutionary biology, software engineering, medicine, legal reasoning, educatio

What GAO Found Certain types of waste are particularly pervasive or difficult to clean up with existing technologies. Microbes (e.g., bacteria, fungi) can be engineered to break down pollutants more effectively. For example, researchers have inserted genes into bacteria that allow the bacteria to break down contaminants, such as phenol or hydrocarbons, in water and soil. At present, there are no examples of commercially available engineered microbes for waste cleanup. Example areas where enginee

The modern soybean is one of the world’s miracle technologies, capable of cheaply, efficiently, and healthfully supplying protein and other nutrients to billions of people. But, unfortunately, we’ve mostly chosen to squander it on the most destructive purposes possible. Soy is America’s second most widely cultivated crop, occupying a land area equivalent to more than […]

As frontier language models advance, policymakers and model developers need methods for assessing whether model access materially increases a non-expert actor's ability to plan high-consequence Chemical, Biological, Radiological, or Nuclear (CBRN) misuse relative to public tools alone. Existing CBRN evaluations differ in non-expert definitions, threat scope, baselines, scoring rubrics, and decision rules, making results difficult to compare across studies. We introduce a Threshold Exceedance Cri

Background: Osteogenesis imperfecta (OI) is a rare genetic disorder characterized by bone fragility and recurrent fractures. Emerging biologics demonstrate promise by targeting bone-remodeling pathways, yet evidence for their efficacy and safety remains fragmented and heterogeneous, and no prior systematic review in OI has incorporated artificial intelligence (AI) to synthesize it. Objective: This study aims to systematically evaluate the efficacy and safety of novel biologics in patients with O

We present RAG-GNN, an end-to-end trainable framework that augments a graph neural network (GNN) encoder for protein interaction networks with a jointly optimized dense retrieval module over a TF-IDF-indexed document corpus, a gated fusion mechanism, and contrastive alignment between node and document representations. The study is positioned as a controlled methodological investigation of whether retrieval augmentation provides measurable benefit beyond a matched GNN-only ablation, rather than a

A striking feature of the human visual system is that it ingests visual information through a series of local foveated glimpses, rather than a single global computation. This makes human vision distinctly different from most popular computer vision models in use today, which input images globally and in a single shot. A natural question therefore is whether local, sequential vision models may provide any fundamental computational benefits in addition to being biologically more plausible than glo

Scientific ideas rarely start from a blank page. They inherit mechanisms, repair known limitations, and recombine pieces of earlier work, much like biological genomes. Current benchmarks still say little about whether AI systems can follow this inheritance structure. We present IdeaGene-Bench (IG-Bench), a benchmark for scientific lineage reasoning and lineage-grounded idea generation. IG-Bench is organized around the IdeaGene framework: each paper or proposal is represented as a set of minimal,

Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influence of disease context on target behavior and therapeutic outcomes. To address this gap, we introduce DrugGen-2, a novel generative model that designs small molecules conditioned on both disease ontology and target protein sequences. DrugGen-2 was developed by fine-tuning a pre-trained GPT-2 model on a curated dataset

The idea of ‘biological agency’ — that life devises its own goals and behaves accordingly — complicates our understanding of what it means to be alive. But does it serve a scientific purpose? The post Is Life Just Different? first appeared on Quanta Magazine

The goal of creating synthetic cells is not to replace nature, but to learn more deeply about biology and reengineer it to help society.

Recent cases of forced explantation of neurotechnologies seem to be grounded on a pre-theoretical naturalist conception of the body as an entity that cannot have a non-biological object as a proper part. However, this conception has been challenged by functional approaches, according to which if an artifact robustly contributes to the function of a body, it is part of it and should be legally treated as such. Bublitz ( 2022 ) argues that a series of problems would result from revising the law to

A cancer journal has retracted a paper by a former acting director of an institute in India, bringing her retraction total to nine. Chitra Mandal, a former senior researcher at the Centre for Scientific and Industrial Research’s Indian Institute of Chemical Biology (CSIR-IIC) at Kolkata, served as acting director in 2014-15. She also headed the … Continue reading Former acting director of national research lab in India adds another retraction

One morning in 2019, Adebayo Alonge was in a Cape Town hotel room, preparing to demonstrate his startup’s AI answer to a serious problem in African health care: counterfeit medication, which kills thousands of people across the continent every year. The RxScanner is a handheld spectrometer that scans a pill with infrared light, then sends the item’s molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile—or

Constance Viehbeck is an ESRC-funded PhD candidate in the Department of Health Policy at LSE, researching how pharmaceutical research and regulation shape health equity. Her work combines quantitative ...

Deep oceans contain microbes with yet-to-be-discovered properties that could drive future innovations in biotechnology.

The Journal of Medical Ethics has retracted a paper on the use of AI in the pharmaceutical industry for containing references that don’t exist. The article’s sole author: a high school student. The paper, which argues biased algorithms can exacerbate inequities in health care, was published in September. The author, Irfan Biswas, listed his affiliation … Continue reading Ethics journal retracts paper by high school student for AI, peer review manipulation

Call AI in biotech hype at your peril. There are real reasons that pharmaceutical companies are embracing this technology right now.

As AI agents are incorporated into life science workflows, the capabilities that speed discovery might also enable misuse. We present BioSecBench-Refusal, a benchmark for risk identification and refusal behavior for biological research tasks. The benchmark pairs 61 Routine tasks, legitimate analyses adapted from the published literature, with 46 Red-Team tasks, fictional scenarios that resemble real research but conceal a biosecurity hazard. Across 16 model-harness configurations, refusal rates

Hierarchical structure is common in image data, where fine-grained clusters often merge into larger, coarser semantic groups. In biological cell images, current self-supervised learning models often suppress this hierarchy, as coarse factors such as imaging modality can obscure finer morphological attributes in the latent space. We propose a hierarchy-aware self-supervised training framework to address this problem. Our method combines two components: a distillation framework with a segmentation

The transition of Large Language Models (LLMs) from passive generators to autonomous agents has introduced significant challenges in reliability, security, and state management. Current agentic architectures are often constructed ad-hoc, prone to hallucination cascades, infinite loops, and prompt injection attacks. This paper argues that many of these failure modes can be analyzed using control motifs long studied in systems biology, provided the comparison is made at the level of typed interfac

Using molecular large language models (LLMs) as a unified framework for understanding molecular structures and functions is emerging as a new trend in tasks such as molecular design and drug discovery. However, these models struggle to fully capture the visual representation of molecular structures, limiting their potential. While existing molecular vision-language models (VLMs) show promise, they still face challenges in structural alignment and lack the necessary topological modeling for accur

Last month, mathematics researchers released the Leiden Declaration, a treatise on how to handle the challenges that artificial intelligence poses to their field. C. Brandon Ogbunu argues that biologists should learn from the mathematics community and work on their own version.

A biology journal that paid peer reviewers found that the approach cut the time to a first editorial decision by 85% and maintained high-quality reviews.

In 2025, an Australian couple asked to have their remaining embryos moved to another clinic, only to discover that the child they had birthed 2 years earlier had not come from their own embryos, but an embryo belonging to a different couple. These situations can lead to disputes about who is recognised as ‘the parents’ in the biological or social sense, as well as who has moral or legal claims to parental rights and responsibilities. In terms of specific legal disputes over custody o

The pace of scientific advancements in medicine, driven by artificial intelligence as much as by novel biotechnologies, demands an ever-faster update of professional knowledge from physicians and collaboration in interdisciplinary teams. At the same time, the increased heterogeneity of patients’ lifeworlds in socially and culturally diverse societies requires healthcare professionals to consider diverging personal and cultural perspectives in their treatment recommendations. Both developme

Why the Llama lead left Meta for drug discovery, PEARL's zero-shot OpenBind win, and what becomes possible when co-folding finally crosses the accuracy threshold.

Postural balance is essential for both humans and robots, as failures increase fall risk and limit robotic performance in real-world settings. Although humans and robots share fundamental balancing mechanics, biological complexity limits the isolation of individual muscle functions and direct principle transfer to robots. In this study, we use EPA-Walker, a bio-inspired robot actuated by electric motors and pneumatic artificial muscles (PAMs), as a physical platform to investigate perturbed stan

Understanding how complex cognitive functions are organized within artificial systems is central to interpreting large language models (LLMs) and relating them to biological cognition. Yet although LLMs exhibit broad cognitive-like behaviours, it remains unclear whether their internal representations form reproducible functional systems that explain behaviour, failure and links to human cognition. Here we present NeuroCogMap, a cognitive neuroscience-inspired framework that organizes internal fe

At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access…

Anthropic released Claude Science, an application that optimizes its large language model for scientists and, especially, those doing research at pharma companies.

Autonomous wet-lab experimentation requires more than plausible protocol text: biological intent, quantitative procedures, device constraints and experimental feedback must remain aligned from protocol and SOP design to code and physical execution. We developed ProtoPilot, a self-evolving multi-agent system, together with an expert-grounded benchmark and evaluation framework for testing this conversion as an experimental automation problem. The framework spans 294 synthetic-biology and molecular

Artificial intelligence is transforming our capability to solve biological challenges. In dimensionality bottleneck regimes exacerbated by high-dimensional biological data, neural networks force distinct concepts into the lower dimensions known as superposition. Although this superposition is widely known to hinder interpretability, its impact on corrupting the geometry of latent spaces remains critically overlooked. Here, we utilized sparse autoencoders (SAEs) trained on over 100,000 multiplexe

Introducing GeneBench-Pro, a new benchmark testing AI performance in genomics, biology, and scientific research using complex, real-world datasets.

The genomic generation is on its way

Edison Scientific and investment firm Population Health Partners are teaming up to leverage AI agents in drug discovery and development.

While AI holds the potential to revolutionize space life sciences, realizing this promise is contingent upon the systematic restructuring of heterogeneous spaceflight biological data into machine-actionable, AI-ready forms. Even though open access principles support human reuse and scientific reproducibility, this does not necessarily enable AI systems to access and analyze such a diverse set of scientific datasets. In addition, the growing array of AI approaches places distinct demands on data

Protein language models are standard priors for biological sequence generation, but steering them toward explicit distributional design targets remains largely unexplored. We study a constrained protein generation problem in which sequences must match a desired amino-acid (AA) composition profile while preserving plausible sequence statistics and diversity. The motivating application is synthetic feed protein design, where the AA composition of dietary proteins directly determines their nutritio