Pharma Biotech

Barcelona’s Mafer AI raises €2 million to build an AI operating system for R&D teams in formulation industries

Mafer AI, a Barcelona-based startup building an AI operating system for R&D teams in formulation industries, has closed a €2 million pre-Seed funding round backed by Kfund, 4Founders Capital, Masia and Lavanda Ventures, the startup investment arm of the Puig family.  It has also secured backing from leading business angels, including Adrián Mato (Andreessen Horowitz […]

The Biosecurity Blind Spot: Systematic Dual-use Detection in Open Science Infrastructure

arXiv:2605.28843v1 Announce Type: cross Abstract: AI is transforming life sciences research at unprecedented speed, accelerating discovery across protein structure prediction, genome modeling, and drug development (Jumper et al., 2021; Mak et al., 2024). Yet this rapid advancement, coupled with the open science movement, introduces significant dual-use research concerns that have received limited empirical scrutiny. Here we present the first systematic analysis of dual-use research of concern (DURC) content on open preprint servers. We screened ~52,000 bioRxiv preprints (2024-2025) using a hybrid pipeline of lexical filtering and large language model (LLM) evaluation, scoring metadata across nine DURC, three PEPP, and five governance categories aligned with U.S. and Australia Group oversight frameworks. Our analysis reveals that dual-use-adjacent knowledge is routinely present in openly accessible titles and abstracts, often exceeding established risk thresholds even in studies with legitimate public health objectives. While this mapping captures surface-level information diffusion, it does not measure operational capability, downstream misuse potential, or the substantial technical and biosafety barriers that constrain harmful application. We argue that institutional review processes, funding requirements, and preprint platform policies must evolve to incorporate proactive, metadata-level monitoring without compromising scientific transparency. Ultimately, harmonizing controlled-access mechanisms for high-risk methodologies with open summaries of scientific contributions offers a pragmatic framework for governing AI-accelerated biology at scale.

Zuckerberg's lab just had a big AI biology moment

Meta's AI research lab has achieved a significant breakthrough in the field of biology, specifically regarding protein modeling.

Merck and Mastercard are seeing real agentic AI results. Both say the plumbing came first.

Merck is using AI agents to cut drug discovery cycles by a third and ship compliant marketing materials up to 80% faster — but VP of Digital Platforms Sean Finnerty says the only reason it's working is because they built the infrastructure first. And the pharmaceutical manufacturer is seeing promising early results: AI is generating marketing drafts that are “99% right” when it comes to compliance

Zuckerberg's philanthropic venture unveils AI world model for drug discovery

# Zuckerberg's philanthropic venture unveils AI world model for drug discovery Published: 2026-05-27T12:03:51.230000+00:00 Source: reuters.com (reuters.com) Language: en ## Story Meta Platforms CEO Mark Zuckerberg arrives outside court to take the stand at trial in a key test case accusing Meta and Google's YouTube of harming kids' mental health through addictive... Purchase Licensing Rights , opens new tab May 27 (Reuters) - Biohub, a philanthropic venture of Meta CEO Mark Zuckerberg and his wife, Priscilla Chan, launched on Wednesday a world model of protein biology ​to accelerate drug discovery. Proteins are the body's essential molecular machinery, performing diverse ‌roles from building structures to generating energy. But designing new proteins that are stable and effective in the body has remained a scientific challenge. Biohub said its world model is built on ​the fourth ge

Insilico Taps US Partner to Build AI Model for Human Longevity

Hong Kong AI drug discovery firm Insilico Medicine Cayman TopCo is stepping up the quest to harness artificial intelligence to extend human life.

How Digital Strategies Transform Pharma Supply Chains in 2026 | Pharmaceutical Commerce

The same logic applies to AI: deploy narrow, high-value agents inside an orchestration layer with human-in-the-loop guardrails before pursuing broader autonomy. The third theme is the institutional capability that makes digital investments durable: building a supply chain that is simultaneously sustainable, resilient, and agile. Resilience in 2025 is being redefined by geopolitics...

From Replacement to Orchestration: A Socio-Technical Architecture for Agentic AI in Corporate R&D

arXiv:2605.24580v1 Announce Type: new Abstract: Purpose: Corporate R&D faces a persistent productivity paradox: rising investment and expanding scientific knowledge have not translated into proportional innovation output. In pharmaceuticals this is captured as Eroom's Law; analogous patterns appear across engineering, materials science, and healthcare. The core cause is not insufficient tools but cognitive saturation: researchers spend an increasing share of their effort on coordination, documentation, and data governance -- hidden work that displaces high-value hypothesis formation, interpretation, and strategic synthesis. Design/Methodology/Approach: The paper uses a Design Science Research (DSR) methodology. The artifact is the HARMONY operating model. Evidence is triangulated from four semi-structured expert interviews with senior R&D leaders across industrial, healthcare, and academic settings; a foresight scenario analysis projecting four plausible 2040 R&D futures; and pattern matching with documented agentic R&D deployments. Two non-negotiable design requirements guide the architecture: cognitive-load redistribution (DR1) and bounded autonomy with alignment (DR2). Findings: We propose HARMONY -- Hybrid Agentic Research Model for Organisational New Yield -- a four-pillar socio-technical architecture comprising ResOps (Industrialized Execution), the Control Tower (Strategic Visibility and Drift Detection), the Ethics Fabric (Bounded Autonomy by Design), and the Talent Studio (Sciencepreneur Capability). The model introduces the Sciencepreneur as the central human archetype in agentic R&D, and Orchestration Leverage as a candidate productivity metric suited to human-agent hybrid systems.

5 Key Regulatory Shifts From Makary's Era at the FDA | AJMC

From rewriting drug approval standards to embedding AI in review workflows, former FDA commissioner Marty Makary, MD reshaped how evidence, speed, and access are balanced in US drug regulation.

AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery

arXiv:2605.23204v1 Announce Type: new Abstract: Scientific research is being reshaped by AI systems that move beyond isolated assistance toward longer-horizon workflows spanning literature grounding, hypothesis generation, experimentation, validation, reporting, and revision. This shift marks a transition from task-level AI for science to workflow-level research automation. Yet current systems remain fragmented, differing in autonomy, domain scope, execution environment, validation mechanism, and human oversight, while still struggling with evidence preservation, reproducibility, weak-direction rejection, provenance tracking, cross-domain robustness, and accountable scientific closure. This survey examines these developments through AutoResearch, defined as the developmental spectrum of AI-powered scientific workflow automation. Within it, Vibe Research denotes the human-steered region of prompt-based assistance and human-verified execution, whereas emerging AI-led systems coordinate larger portions of the discovery loop without achieving robust autonomy. We analyze how research systems redistribute control, evidence, execution, validation, and accountability across workflows and organize the field around five workflow conditions: literature and research grounding; hypothesis formation and planning; experimentation and tool use; feedback, validation, and review; and reporting and knowledge communication. We further synthesize AI scientist systems, mixed-initiative co-research frameworks, benchmarks, domain deployments, and open-source infrastructures. Finally, we propose five evaluation dimensions--novelty, validity, impact, reliability, and provenance--and show that AutoResearch autonomy is domain-conditioned, being more credible in structured, executable, and rapidly verifiable settings but limited in embodied, delayed, heterogeneous, ethical, or institutionally accountable contexts.

Anthropic policy chief Jack Clark says, "AI will help make a Nobel prize-winning discovery within a year" | - The Times of India

AI could begin contributing to discoveries worthy of major scientific prizes in the coming months, potentially leading to a Nobel Prize nomination, according to Jack Clark, co-founder and head of policy at AI firm Anthropic.

Final frontier for meds? UK startup sends drug-making into space

BioOrbit hopes drug-crystallisation technology will lead to self-injected cancer treatment that could save millions Onboard a SpaceX flight last week was a remarkable piece of cargo – a hi-tech box destined for the International Space Station to grow ultra-pure protein crystals, with the aim of producing self-injected cancer drugs. A British startup, BioOrbit, has developed the drug-crystallisation technology at its labs in London and launched Box-E, a compact unit the size of a microwave, on the 15 May rocket from Kennedy Space Center in Florida. Continue reading...

AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows

arXiv:2605.20425v1 Announce Type: new Abstract: Designing multi-agent workflows is especially difficult in open-ended scientific settings where tasks lack curated training sets, reliable scalar evaluation metrics, and standardized interfaces between existing tools and agents. We propose AgentCo-op, a retrieval-based synthesis framework that composes reusable skills, tools, and external agents into executable workflows through typed artifact handoffs, then applies bounded self-guided local repair to implicated components when execution evidence indicates failure. In two open-world genomics case studies, AgentCo-op composes independently developed scientific agents and external tool repositories into auditable workflows without redesigning them or running global topology search. It coordinates specialized agents for spatial transcriptomics and gene-set interpretation to enable collaborative discovery from spatial transcriptomics data, and builds a parallel workflow for cross-modality marker analysis on single-cell multiome data. AgentCo-op can also import a searched workflow as a structural prior and improve it by grounding nodes with retrieved components and applying local repair, showing that synthesis and search are complementary. On six coding, math, and question-answering benchmarks, AgentCo-op achieves the best result on four benchmarks and the best average score under a unified backbone setting, while consistently reducing per-task cost relative to multi-agent baselines. Together, these results suggest that retrieval-based synthesis can extend automated agentic workflow design beyond benchmark-optimized agent graphs to open-world workflows built from existing agents, tools, and typed artifacts.

AI may speed up search for drugs to treat brain conditions

Researchers hope the work will help identify affordable, effective drugs to treat conditions like MND.

Manchester Imperagen raises €5.8 million Seed to scale AI and quantum-powered enzyme engineering

Imperagen, a Manchester-based BioTech company using AI and quantum physics to engineer better enzymes faster, has closed €5.7 million (£5 million) in Seed funding to accelerate R&D, expand its wet lab capabilities, and build out its go-to-market function over the next 18 months. The round was led by PXN Ventures with participation from Imperagen’s existing […]

From Prompts to Protocols: An AI Agent for Laboratory Automation

arXiv:2605.16552v1 Announce Type: new Abstract: Automating science laboratories enables faster, safer, more accurate, and more reproducible execution of protocols, accelerating the discovery and testing of new materials, drugs, and more. However, setting up and running autonomous labs requires coordinating numerous instruments and robots, forcing scientists to write code, manage configuration files, and navigate complex software infrastructure. We present an AI agent architecture that integrates large language models with laboratory orchestration, enabling scientists to interactively create and monitor automated lab protocols using natural language. Integrated into the Experiment Orchestration System (EOS), the AI agent operates under an agentic loop with automated validation and error correction, and supports the complete experimental lifecycle: creating protocols, running and monitoring both protocols and closed-loop optimization campaigns, and analyzing results. A visual graph editor renders protocols as interactive node-based diagrams synchronized with the AI agent's protocol representation, enabling seamless alternation between AI-assisted and manual protocol construction. Evaluated on three simulated automated labs spanning chemistry, biology, and materials science, the AI agent achieves a 97% first-attempt protocol generation success rate and an order of magnitude reduction in required interface actions.

PRISMat: Policy-Driven, Permutation-Invariant Autoregressive Material Generation

arXiv:2605.16612v1 Announce Type: new Abstract: Rapid identification of candidate materials with target properties has become a key task in materials science. Machine learning has emerged as an alternative to physics-based simulation, offering a faster and cheaper way to filter materials based on their stability and other target properties, reducing the number of candidates that reach the costly synthesis stage. Recently, Large Language Models (LLMs) have been applied to this role, but these models are parameter-heavy and computationally expensive both during training and at inference time, making them unsuitable for high-throughput tasks. This inefficiency stems from both the large over-parameterization of language models and the difficulty of framing material generation as a sequence learning problem. In this paper, we present PRISMat, a cost-effective, permutation-invariant model, which addresses these limitations. We show that PRISMat, despite taking less time for inference, is able to outperform LLMs in generating crystal slabs conditioned on critical materials' surface properties. In targeted material discovery, we achieve mean absolute errors of 0.188 eV/A$^2$ and 2.79 eV for cleavage energy and work function tasks, respectively, reducing the error of the next best model by 4$\times$.

FDA’s AI In Early Phase Clinical Trials RFI: An Opportunity To Help Set The Rules Of The Road - New Technology - United States

A path toward “co-developing” ... refine evaluation approaches, metrics, and governance controls for AI used in trial conduct and early decision-making (including FDA regulatory decisions and sponsor-internal decision points). For companies, the main near-term opportunity is to shape the practical standards that may ultimately govern AI used in recruitment, dose selection, endpoint measurement, and safety ...

How AI is speeding up cancer research | Science And Tech | wfmz.com

Kivo reports AI accelerates cancer research by analyzing large datasets, improving clinical trial matching, and expediting biomarker discovery.

Medical, Legal, and Regulatory (MLR) Review Software Business Report 2026: A $27.1 Billion Market by 2032 from $13.1 Billion in 2025 - Rising Demand for Streamlined Compliance in Pharma and Biotech

The MLR Review Software market presents opportunities for streamlined compliance in pharma and healthcare, driven by regulatory demands and digital transformation. AI, ML, and cloud tech enhance workflow efficiency and global collaboration, catering to a demand for integrated, secure, and scalable ...

AI Protein Design Market 2026-2033 | Market Growth to FY (Q2) - Generate:Biomedicines, Insilico Medicine, Arzeda Corp., Cradle, Profluent, A-Alpha Bio Inc., Schrödinger Inc

Market Growth Size 2026 2033 Global AI Protein Design Market reached US 1 18 Billion in 2024 rising to US 1 5 Billion in 2025 and is expected to reach US 6 98 Billion by 2033 growing at a CAGR ...

GGBound: A Genome-Grounded Agent for Microbial Life-Boundary Prediction

arXiv:2605.14442v1 Announce Type: new Abstract: Characterizing the physiological life boundaries of microbial strains, including viable temperature, pH, salinity, substrate utilization, and morphology, is central to biotechnology and ecology, yet traditionally requires exhaustive in vitro screening. Existing computational approaches either treat physiological traits as isolated supervised targets or repurpose biological foundation models as static encoders, leaving the genotype-to-physiology gap largely unbridged. We formulate microbial life-boundary prediction as a unified genome-to-physiology task and address it with a genome-conditioned, tool-augmented LLM agent. To support this task, we curate a strain-centric benchmark from IJSEM, NCBI, and BacDive covering 1,525 strains and 6,448 instances across viability intervals, environmental optima, substrate utilization, categorical traits, and morphology. Architecturally, the agent injects frozen LucaOne genome embeddings into a Qwen backbone via lightweight token fusion, and reasons over a similarity-based RAG module and a Genome-scale Metabolic Model (GEM) perturbation tool. We optimize the agent through a three-stage pipeline of gene-text alignment, agentic SFT on distilled trajectories, and GRPO with a novel counterfactual gene-grounding reward that reinforces the policy only when the authentic genome embedding causally improves correct-token generation relative to a zero-gene ablation. The resulting 4B-parameter agent matches or surpasses substantially larger frontier LLMs, with ablations confirming that genome-token fusion, dynamic tool use, and the counterfactual reward each yield distinct, significant gains.

PROMETHEUS: Automating Deep Causal Research Integrating Text, Data and Models

arXiv:2605.12835v1 Announce Type: new Abstract: Large language models can extract local causal claims from text, but those claims become more useful when organized as persistent, navigable world models rather than as flat summaries. We introduce PROMETHEUS, a framework that turns retrieved literature, filings, reviews, reports, agent traces, source data, code, simulations, and scientific models into causal atlases: sheaf-like families of local causal predictive-state models over an explicit cover of a research substrate. Each local region contains causal episodes, structured claim tables, predictive tests, support statistics, and provenance; restriction maps compare overlapping regions; gluing diagnostics expose agreement, drift, contradiction, and underdetermination. The resulting Topos World Model is not a single universal graph. It is a research instrument for navigating what a corpus says, where it says it, how strongly it is supported, and where local claims fail to assemble into a coherent global view. Three literature-atlas case studies -- ocean-temperature impacts on marine populations, GLP-1 weight-loss evidence, and resveratrol/red-wine health-benefit claims -- illustrate deep causal research from text with explicit locality, evidence, persistent state, and gluing tension. Four grounded-counterfactual case studies -- a Nature Climate Change microplastics forcing paper, an Indus Valley hydrology paper with VIC-derived figure data and model code, the canonical Sachs protein-signaling study with single-cell perturbation data, and a Nature singing-mouse study with MAPseq projection matrices -- show a stronger mode: when a paper ships source data, simulation outputs, or code, PROMETHEUS can evaluate a counterfactual against that scientific substrate and then rebuild the sheaf world model around the

Alphabet’s Isomorphic Labs raises $2.1bn in Series B funding

The organisation plans to use the investment as a means of accelerating the application of its AI model at scale. Read more: Alphabet’s Isomorphic Labs raises $2.1bn in Series B funding

UK backs Isomorphic Labs to strengthen sovereign AI and drug discovery | Digital Watch Observatory

Frontier innovation in the UK grows as Sovereign AI supports Isomorphic Labs and AI-driven drug development.

Isomorphic Labs secures $2.1B to advance AI drug design

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Google Commits $10M to REPLIQA Initiative Linking Quantum AI and Life Sciences - HPCwire

Google is launching the Research Program at the Intersection of Life Sciences & Quantum AI (REPLIQA), an initiative committing $10 million to five universities to apply advanced quantum science and AI to the life sciences, to improve human outcomes. May 11, 2026 — Understanding human biology ...

Agentic Discovery of Exchange-Correlation Density Functionals

arXiv:2605.05460v1 Announce Type: new Abstract: The development of accurate exchange-correlation (XC) functionals remains a longstanding challenge in density functional theory (DFT). The vast majority of XC functionals have been hand designed by human researchers combining physical insight, exact constraints, and empirical fitting. Recent advances in large language models enable a systematic, automated alternative to this human-driven design loop. This report presents an agentic search system in which an LLM proposes structured functional-form changes guided by evolutionary history. The system attempts to improve functional performance through an iterative plan-execute-summarize loop, where improvements are measurable by optimizing functional parameters against a standard thermochemistry dataset, then evaluating performance on a held-out subset. The strongest discovered functional, SAFS26-a (Seed Agentic Functional Search 2026), improves upon the gold-standard {\omega}B97M-V baseline by ~9%. These results also surface a cautionary lesson for AI-assisted science: models powerful enough to discover genuine improvements are equally capable of exploiting unphysical shortcuts to game the benchmark; domain expertise translated into explicitly enforced constraints remains essential to keeping results scientifically grounded.

AI Investment Surge Accelerates Global Competition as Corporate Spending Reaches $218 Billion, Driven by Pharmaceutical and Automotive Digital Transformation

Report examines corporate investment, private funding, mergers and acquisitions, venture capital, government grants, and regional AI investment patterns....

The Case for ESM3 as a General-Purpose AI Model with Systemic Risk Under the EU AI Act

arXiv:2605.01611v1 Announce Type: new Abstract: Due to ambiguity in the wording of the EU AI Act, we examine the question of to what extent frontier biological foundation models such as ESM3 are subject to obligations for general-purpose AI models with systemic risk under the EU AI Act. In this paper, we map ESM3 to the biorisk chain, and conclude that it would be desirable if the providers of ESM3 and similar biological models were subject to these obligations, which would require them to assess and mitigate dual-use risks from their models. We then perform an analysis, comparing the attributes of ESM3 to the classification criteria in the AI Act and the supporting material. We conclude that at this time, ESM3 does not appear to be meaningfully regulated by the Act. We then propose remedies to correct the situation.

AI Revolutionizes Drug Discovery

AI-driven platforms like AlphaFold 3 and Insilico Medicine's Pharma.AI are revolutionizing drug discovery, expediting target identification and molecule design. With collaborations and internal tool d

AI Revolutionizes Drug Discovery

AI-driven platforms like AlphaFold 3 and Insilico Medicine's Pharma.AI are revolutionizing drug discovery, expediting target identification and molecule design. With collaborations and internal tool development, AI is poised to save the pharmaceutical industry over $50 billion in annual R&D costs.

AI Revolutionizes Drug Discovery, Clinical Research, and Manufacturing, Cutting R&D Costs by $50 Billion Annually

AI-driven platforms like AlphaFold 3 and Insilico Medicine's Pharma.AI are revolutionizing drug discovery, expediting target identification and molecule design.

Council Post: The New Rules Of AI In Pharma: What FDA And EMA's 10 Guiding Principles Mean For Your Business

For pharmaceutical and life sciences companies ready to embrace this moment, the 10 principles aren't a burden. They're a blueprint.​

Madrigal Pharmaceuticals AI Platform

This case study details how Madrigal Pharmaceuticals used LangChain and LangGraph to build a multi-agent AI platform for research and intelligence, incorporating orchestration, guardrails, and state management. While it showcases many cutting-edge generative AI patterns like multi-agent systems and orchestration layers, our analysts noted the lack of measurable business impact and questioned scalability for large enterprises.

Zuckerberg Invests $500M in AI Biology

Zuckerberg's $500M AI biology swing indicates a significant investment in the field, potentially leading to breakthroughs and economic growth.

Swiss BioTech startup ALP Bio raises €1.9 million to advance immune organoid and AI platform

Schlieren-based ALP Bio AG today announced it has raised €1.9 million in pre-Seed financing to accelerate their platform which combines human immune organoid biology with generative AI to help antibody developers identify, understand, and reduce immunogenicity risk earlier in drug development. The round was led by Munich-based VC 42CAP, with participation from Venture Kick and […]

Zuckerberg bets $500M on biology

Biohub, the nonprofit spearheaded by Mark Zuckerberg and Priscilla Chan, is committing $500 million to help create better AI simulations of the human body.

AI Breakthrough Revolutionizes RNA Therapeutics

A new AI framework improves the identification and optimization of IRES elements, showing a 15% increase in predictive accuracy for RNA-based therapeutics.

FDA to pilot real-time clinical drug trials through cloud and AI - Government Executive

The first-of-its-kind pilot could lead to speedier regulatory approval of medical drugs and devices and potentially reduce “20, 30, 40% of overall clini...

Pharma supply chains are performing, but not yet optimised

Released in Vienna, LogiPharma’s 2026 Playbook shows pharma supply chains at an inflection point shaped by AI, risk, and digital collaboration.

Pharma Giants Invest in AI

Pharma giants like Merck, Novo Nordisk, and Sanofi are investing heavily in AI and biotech acquisitions, fueled by savings from significant workforce cuts.

J&J sees AI halving the time to generate drug development leads | Reuters

Johnson & Johnson is using artificial intelligence to slash by half the time it ‌takes to generate new leads for developing drugs, the company's chief information officer said on Monday.

AI Healthcare Transformation: 7 Powerful Breakthroughs?

AI healthcare transformation accelerates as Profluent and Eli Lilly sign a $2.25B genetic medicine deal, advancing next-generation gene editing.

Merck, Novo Nordisk, and Sanofi Lead Pharma's AI Revolution Amid Workforce Cuts and Strategic Acquisitions

Pharma giants like Merck, Novo Nordisk, and Sanofi are investing heavily in AI and biotech acquisitions, fueled by savings from significant workforce cuts.

MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization

arXiv:2604.21937v1 Announce Type: new Abstract: Computational drug discovery, particularly the complex workflows of drug molecule screening and optimization, requires orchestrating dozens of specialized tools in multi-step workflows, yet current AI agents struggle to maintain robust performance and consistently underperform in these high-complexity scenarios. Here we present MolClaw, an autonomous agent that leads drug molecule evaluation, screening, and optimization. It unifies over 30 specialized domain resources through a three-tier hierarchical skill architecture (70 skills in total) that facilitates agent long-term interaction at runtime: tool-level skills standardize atomic operations, workflow-level skills compose them into validated pipelines with quality check and reflection, and a discipline-level skill supplies scientific principles governing planning and verification across all scenarios in the field. Additionally, we introduce MolBench, a benchmark comprising molecular screening, optimization, and end-to-end discovery challenges spanning 8 to 50+ sequential tool calls. MolClaw achieves state-of-the-art performance across all metrics, and ablation studies confirm that gains concentrate on tasks that demand structured workflows while vanishing on those solvable with ad hoc scripting, establishing workflow orchestration competence as the primary capability bottleneck for AI-driven drug discovery.

OpenAI Launches GPT-Rosalind: A Specialized AI Model Aimed at Accelerating Drug Discovery

the company officially introduced GPT-Rosalind — its first frontier reasoning model purpose-built for life sciences research

MSD teams up with Google Cloud on agentic AI transformation

MSD and Google Cloud have formed a multi-year partnership, investing up to $1bn, to advance agentic AI enterprise transformation.

AI is spitting out more potential drugs than ever. This start-up wants to figure out which ones matter. | TechCrunch

10x Science has raised a $4.8 million seed round to help pharmaceutical researchers understand complex molecules.

Revolutionary Protein Interaction Model Boosts Accuracy by 17%

The National University of Singapore unveiled PPLM, a tool for predicting protein-protein interactions with 17% higher accuracy, aiding drug discovery.

Novo Nordisk Partners with OpenAI for Drug Discovery | BioPharm International

Novo Nordisk intends to apply advanced AI to analyze complex data, improve target identification, and accelerate development of therapies for chronic diseases

Introducing GPT-Rosalind for life sciences research

GPT-Rosalind is OpenAI’s life sciences model designed to help researchers navigate literature, identify promising compounds, and support faster experimental design.

Boehringer Ingelheim launches AI centre for pharma research in London | Reuters

German drugmaker Boehringer Ingelheim is launching a centre for artificial intelligence and machine learning in London, ​it said on Monday, as it seeks to ‌expand its AI capabilities in pharmaceutical research and development.

OpenAI debuts AI model GPT-Rosalind to speed up drug discovery | TechTarget

Biopharma companies, including Moderna and Amgen, are working with OpenAI to integrate its new artificial intelligence model into their workflows to accelerate research and discovery.

Lunit Unveils AI Breakthroughs in Oncology at AACR 2026, Boosting Biomarker Precision and Treatment Strategies

Lunit unveiled six groundbreaking AI-driven studies at AACR 2026, focusing on enhancing cancer treatment decision-making through AI-powered biomarkers and tumor microenvironment analysis.

OpenAI Launches GPT-Rosalind to Revolutionize Life Sciences Research with AI-powered Drug Discovery

OpenAI has introduced GPT-Rosalind, a specialized AI model for life sciences research, targeting advancements in drug discovery, genomics, and protein engineering.

OpenAI launches AI model GPT-Rosalind for life sciences research | Reuters

Open AI , creator of popular chatbot ChatGPT, on Tuesday unveiled GPT-5.4-Cyber, a variant ⁠of ​its latest flagship model fine-tuned specifically ​for defensive cybersecurity work, following rival Anthropic's announcement of frontier AI model Mythos.

OpenAI to rival Google’s AlphaFold with new AI model for life sciences research

The model is the first release in OpenAI’s Life Science model series. Read more: OpenAI to rival Google’s AlphaFold with new AI model for life sciences research

Bringing AI-driven protein-design tools to biologists everywhere

MIT researchers are working to make advanced AI-driven protein design tools more accessible to biologists, aiming to accelerate scientific discovery.

What to know about OpenAI's new model for life sciences research GPT-Rosalind | Euronews

The GPT-Rosalind model is designed to accelerate biological research and drug discovery.

OpenAI Targets Pharma Giants With Purpose-Built AI Model | PYMNTS.com

OpenAI has introduced an AI model, GPT-Rosalind, that is purpose-built for scientific research and drug discovery.

OpenAI Launches Life Sciences AI Model, Competing with Tech Giants in Pharma - El-Balad.com

OpenAI has unveiled its new AI model, GPT-Rosalind, specifically designed for life sciences research. This launch signifies OpenAI’s entry into a competitive market populated by major tech firms focused on pharmaceutical applications. Overview of GPT-Rosalind GPT-Rosalind represents OpenAI’s ...

AI in Drug Discovery Market: Share, Growth and Outlook Driven by AI-Enabled Pharmaceutical Innovation

The AI in Drug Discovery Market size was valued at USD 2 34 Billion in 2025 and is projected to reach USD 17 43 Billion by 2033 growing at a CAGR of 28 5 during the forecast period 2026 2033 ...

AI For Smarter Regulatory Filings And Pharma Factories – blog.aimactgrow.com

How AI Makes Regulatory Filings and Pharma Factories SmarterSynthetic intelligence is reshaping how medicines are developed, manufactured, and accredited, but

OpenAI debuts a life sciences AI model, entering crowd of tech giants selling to pharma

OpenAI became the latest tech giant to launch a life sciences-focused AI offering, aiming to build a biopharma business.

Novartis CEO joins Anthropic board, embedding AI in the heart of biopharma - PharmaLive

In recent months, Anthropic has been building more and more ties with the biopharma industry, including partnerships with Big Pharma companies such as Sanofi, Novo Nordisk and AbbVie.

Novo Nordisk OpenAI AI Partnership | News

Novo Nordisk partners with OpenAI to accelerate AI-driven drug discovery and transform pharma innovation and operations | News

Helical Builds Reproducible AI Platform for Drug Discovery

Helical raises $10M to advance reproducible AI workflows, accelerating drug discovery with scalable, decision-ready platforms.

OpenAI debuts GPT-Rosalind, a new limited access model for life sciences, and broader Codex plugin on Github

The journey from a laboratory hypothesis to a pharmacy shelf is one of the most grueling marathons in modern industry, typically spanning 10 to 15 years and billions of dollars in investment. Progress is often stymied not just by the inherent mysteries of biology, but by the "fragmented and difficult to scale" workflows that force researchers to manually pivot between the actual experimental design equipment, software, and databases. But OpenAI is releasing a new specialized model GPT-Rosalind specifically to speed up this process and make it more efficient, easier, and ideally, more productive. Named after the pioneering chemist Rosalind Franklin, whose work was vital to the discovery of DNA’s structure (and was often overlooked for her male colleagues James Watson and Francis Crick), this new frontier reasoning model is purpose-built to act as a specialized intelligence layer for life sciences research. By shifting AI’s role from a general-purpose assistant to a domain-specific "reasoning" partner, OpenAI is signaling a long-term commitment to biological and chemical discovery. What GPT-Rosalind offers GPT-Rosalind isn't just about faster text generation; it is designed to synthesize evidence, generate biological hypotheses, and plan experiments—tasks that have traditionally required years of expert human synthesis. At its core, GPT-Rosalind is the first in a new series of models optimized for scientific workflows. While previous iterations of GPT excelled at general language tasks, this model is fine-tuned for deeper understanding across genomics, protein engineering, and chemistry. To validate its capabilities, OpenAI tested the model against several industry benchmarks. On BixBench, a metric for real-world bioinformatics and data analysis, GPT-Rosalind achieved leading performance among models with published scores. In more granular testing via LABBench2, the model outperformed GPT-5.4 on six out of eleven tasks, with the most significant gains appearing in CloningQA—a task requiring the end-to-end design of reagents for molecular cloning protocols. The model’s most striking performance signal came from a partnership with Dyno Therapeutics. In an evaluation using unpublished, "uncontaminated" RNA sequences, GPT-Rosalind was tasked with sequence-to-function prediction and generation. When evaluated directly in the Codex environment, the model’s submissions ranked above the 95th percentile of human experts on prediction tasks and reached the 84th percentile for sequence generation. This level of expertise suggests the model can serve as a high-level collaborator capable of identifying "expert-relevant patterns" that generalist models often overlook. The new lab workflow OpenAI is not just releasing a model; it is launching an ecosystem designed to integrate with the tools scientists already use. Central to this is a new Life Sciences research plugin for Codex, available on GitHub. Scientific research is famously siloed. A single project might require a researcher to consult a protein structure database, search through 20 years of clinical literature, and then use a separate tool for sequence manipulation. The new plugin acts as an "orchestration layer," providing a unified starting point for these multi-step questions. Skill Set: The package includes modular skills for biochemistry, human genetics, functional genomics, and clinical evidence. Connectivity: It connects models to over 50 public multi-omics databases and literature sources. Efficiency: This approach targets "long-horizon, tool-heavy scientific workflows," allowing researchers to automate repeatable tasks like protein structure lookups and sequence searches. Limited and gated access Given the potential power of a model capable of redesigning biological structures, OpenAI is eschewing a broad "open-source" or general public release in favor of a Trusted Access program. The model is launching as a research preview specifically for qualified Enterprise customers in the United States. This restricted deployment is built on three core principles: beneficial use, strong governance, and controlled access. Organizations requesting access must undergo a qualification and safety review to ensure they are conducting legitimate research with a clear public benefit. Unlike general-use models, GPT-Rosalind was developed with heightened enterprise-grade security controls. For the end-user, this means: Restricted Access: Usage is limited to approved users within secure, well-managed environments. Governance: Participating organizations must maintain strict misuse-prevention controls and agree to specific life sciences research preview terms. Cost: During the preview phase, the model will not consume existing credits or tokens, allowing researchers to experiment without immediate budgetary constraints (subject to abuse guardrails). Warm reception from initial industry partners The announcement garnered significant buy-in from OpenAI parnters across the pharmaceutical and technology sectors. Sean Bruich, SVP of AI and Data at Amgen, noted that the collaboration allows the company to apply advanced tools in ways that could "accelerate how we deliver medicines to patients".The impact is also being felt in the specialized tech infrastructure that supports labs: NVIDIA: Kimberly Powell, VP of Healthcare and Life Sciences, described the convergence of domain reasoning and accelerated computing as a way to "compress years of traditional R&D into immediate, actionable scientific insights". Moderna: CEO Stéphane Bancel highlighted the model's ability to "reason across complex biological evidence" to help teams translate insights into experimental workflows. The Allen Institute: CTO Andy Hickl emphasized that GPT-Rosalind stands out for making manual steps—like finding and aligning data—more "consistent and repeatable in an agentic workflow". This builds on tangible results OpenAI has already seen in the field, such as its collaboration with Ginkgo Bioworks, where AI models helped achieve a 40% reduction in protein production costs. What's next for Rosalind and OpenAI in life sciences? OpenAI’s mission with GPT-Rosalind is to narrow the gap between a "promising scientific idea" and the actual "evidence, experiments, and decisions" required for medical progress. By partnering with institutions like Los Alamos National Laboratory to explore AI-guided catalyst design and biological structure modification, the company is positioning GPT-Rosalind as more than a tool—it is meant to be a "capable partner in discovery". As the life sciences field becomes increasingly data-dense, the move toward specialized "reasoning" models like Rosalind may become the standard for navigating the "vast search spaces" of biology and chemistry.

OpenAI Takes on Google With New AI Model Aimed at Drug Discovery

OpenAI is rolling out an early version of an artificial intelligence model meant to speed up drug discoveries, joining a field of growing interest for tech companies eager to prove AI can pave the way for more scientific breakthroughs.

Exclusive: OpenAI lobbies for science

OpenAI is lobbying for an expanded role for AI in the life sciences sector.

Evaluating agents for scientific discovery

This piece examines benchmark design for scientific discovery agents, testing how well AI systems handle science-like tasks within simulated environments.

Amazon launches AI research tool to speed early-stage drug discovery

Amazon launches AI research tool to speed early-stage drug discovery | Reuters Exclusive news, data and analytics for financial market professionalsLearn more aboutRefinitiv An Amazon Web Services (AWS) logo is pictured during a trade fair in Hannover Messe, in Hanover, Germany, April 22, 2024. REUTERS/Annegret Hilse Purchase Licensing Rights, opens new tab - Companies Amazon Web Services Inc Follow Follow Follow Show more companies April 14 (Reuters) - Amazon's (AMZN.O), opens new tab cloud unit on Tuesday launched Amazon Bio Discovery, an ar

Wegovy-maker Novo Nordisk partners with OpenAI to speed drug ...

Wegovy-maker Novo Nordisk partners with OpenAI to speed drug development | Reuters Exclusive news, data and analytics for financial market professionalsLearn more aboutRefinitiv Item 1 of 3 Boxes of Ozempic and Wegovy made by Novo Nordisk are seen at a pharmacy in London, Britain March 8, 2024. REUTERS/Hollie Adams/File Photo [1/3]Boxes of Ozempic and Wegovy made by Novo Nordisk are seen at a pharmacy in London, Britain March 8, 2024. REUTERS/Hollie Adams/File Photo Purchase Licensing Rights, opens new tab - Summary - Companies - Novo to use OpenAI tech for drug discovery, manufacturing and operations - O

GLP-1 giant Novo Nordisk partners with OpenAI as pharma industry's AI race accelerates | NYSE:NVO

The Danish drugmaker becomes the latest major pharmaceutical company to embed AI across its entire drug development pipeline, from discovery to commercial...

Novo Nordisk Partners with OpenAI to Accelerate AI-Driven Drug Discovery - The Indian Practitioner

Novo Nordisk has announced a strategic partnership with OpenAI to drive AI-led transformation in healthcare. Through this collaboration, Novo Nordisk aims to accelerate the development of innovative treatment options and […]

LABBench2: An Improved Benchmark for AI Systems Performing Biology Research

arXiv:2604.09554v1 Announce Type: new Abstract: Optimism for accelerating scientific discovery with AI continues to grow. Current applications of AI in scientific research range from training dedicated foundation models on scientific data to agentic autonomous hypothesis generation systems to AI-driven autonomous labs. The need to measure progress of AI systems in scientific domains correspondingly must not only accelerate, but increasingly shift focus to more real-world capabilities. Beyond rote knowledge and even just reasoning to actually measuring the ability to perform meaningful work. Prior work introduced the Language Agent Biology Benchmark LAB-Bench as an initial attempt at measuring these abilities. Here we introduce an evolution of that benchmark, LABBench2, for measuring real-world capabilities of AI systems performing useful scientific tasks. LABBench2 comprises nearly 1,900 tasks and is, for the most part, a continuation of LAB-Bench, measuring similar capabilities but in more realistic contexts. We evaluate performance of current frontier models, and show that while abilities measured by LAB-Bench and LABBench2 have improved substantially, LABBench2 provides a meaningful jump in difficulty (model-specific accuracy differences range from -26% to -46% across subtasks) and underscores continued room for performance improvement. LABBench2 continues the legacy of LAB-Bench as a de facto benchmark for AI scientific research capabilities and we hope that it continues to help advance development of AI tools for these core research functions. To facilitate community use and development, we provide the task dataset at https://huggingface.co/datasets/futurehouse/labbench2 and a public eval harness at https://github.com/EdisonScientific/labbench2.

AI bioterrorism risk on the rise, warns leading scientist | Semafor

AI bioterrorism risk on the rise, warns leading scientist | Semafor Intelligence for the New World Economy --- --- From Semafor Flagship In your inbox, every weekday # AI bioterrorism risk on the rise, warns leading scientist Apr 13, 2026, 8:17am EDT Share Kylie Cooper/Reuters AI can now design and run biological experiments, racing ahead of regulatory systems and raising the risk of bioterrorism, a leading scientist warned. OpenAI’s GPT-5 autonomously operated 36,000 experiments via a robotic lab, cutting the cost of creating a target protein by 40%. AI is making biological engineering more accessible, Stephen Turner wrote in The Conversation, lowering the barriers to use. But research suggests users can get

Are We Allowed to Use This AI Thing?

AlphaFold, the Nobel-winning AI system that solved the 50-year-old protein folding problem, has been used by over 3 million researchers in more than 190 countries, including over a million users in low and middle-income countries.

Co-design for Trustworthy AI: An Interpretable and Explainable Tool for Type 2 Diabetes Prediction Using Genomic Polygenic Risk Scores

The polygenic risk scores (PRS) have emerged as an important methodology for quantifying genetic predisposition to complex traits and clinical disease. Significant progress has been made in applying PRS to conditions such as obesity, cancer, and type 2 diabetes (T2DM). Studies have demonstrated that PRS can effectively identify individuals at high risk, thereby enabling early screening, personalized treatment, and targeted interventions for diseases with a genetic predisposition.

PharmaShots Magazine-April-2026 Edition - PharmaShots

Shots: AI is redefining compliance from reactive to predictive, as this April edition highlights how AI-powered systems are transforming regulatory oversight into a continuous, real-time, and data-driven function, enabling early risk detection, automated audits, and smarter regulatory collaboration ...

Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS

GROMACS is a de-facto standard for classical Molecular Dynamics (MD). The rise of AI-driven interatomic potentials that pursue near-quantum accuracy at MD throughput now poses a significant challenge: embedding neural-network inference into multi-GPU simulations retaining high-performance. In this work, we integrate the MLIP framework DeePMD-kit into GROMACS, enabling domain-decomposed, GPU-accelerated inference across multi-node systems.

MMORF: A Multi-agent Framework for Designing Multi-objective Retrosynthesis Planning Systems

arXiv:2604.05075v1 Announce Type: new Abstract: Multi-objective retrosynthesis planning is a critical chemistry task requiring dynamic balancing of quality, safety, and cost objectives. Language model-based multi-agent systems (MAS) offer a promising approach for this task: leveraging interactions of specialized agents to incorporate multiple objectives into retrosynthesis planning. We present MMORF, a framework for constructing MAS for multi-objective retrosynthesis planning. MMORF features modular agentic components, which can be flexibly combined and configured into different systems, enabling principled evaluation and comparison of different system designs. Using MMORF, we construct two representative MAS: MASIL and RFAS. On a newly curated benchmark consisting of 218 multi-objective retrosynthesis planning tasks, MASIL achieves strong safety and cost metrics on soft-constraint tasks, frequently Pareto-dominating baseline routes, while RFAS achieves a 48.6% success rate on hard-constraint tasks, outperforming state-of-the-art baselines. Together, these results show the effectiveness of MMORF as a foundational framework for exploring MAS for multi-objective retrosynthesis planning. Code and data are available at https://anonymous.4open.science/r/MMORF/.

Advancing Healthcare with Generative AI: From Promise to Practice | BioPharm International

Reliable, domain-specific AI models grounded in validated clinical evidence are emerging as essential to safely scaling generative AI across healthcare applications.

AI Drug Discovery: Revolution or Expensive Illusion? | Ep. 978

This week we’re digging into one of the biggest narratives in biotech over the past cycle: AI -driven drug discovery, and whether it’s actually delivering on its promise or just compressing timelines without improving outcomes. We break down how the original pitch of faster, cheaper, and more successful drug development is now colliding with the reality of clinical biology, where failure rates remain stubbornly high.

Tiago Lopes, PhD – Founder & CEO | Nezu Biotech GmbH

Don't forget to share with your friends.🔥 If you don't know me, I am the founder & CEO of Nezu Biotech, in Heidelberg, Germany. We are part of the European Space Agency Business Incubator, and use AI to develop new drugs for mission on the Moon, Mars and deep-space missions - and for patients on Earth as well.

IQVIA.ai Launch With Nvidia Puts AI At Core Of Pharma Ties - Simply Wall St News

IQVIA Holdings has launched IQVIA.ai, a unified AI platform built with Nvidia technology for the life sciences sector. The platform is designed to combine healthcare grade AI, data and compliance for pharmaceutical and healthcare clients. Early adoption is reported among several top global ...

How Agentic AI Is Reshaping the Launch Playbook for Pharma | PharmExec

The agentic model is emerging as a strategic tool to compress analysis timelines, coordinate cross-functional teams and surface competitive intelligence in real time.

neuroClues raises €10 million Series A to become the brain’s stethoscope for early diagnosis of neurological disorders

neuroClues, a French-Belgian MedTech startup empowering clinicians with biomarkers allowing them to identify neurological disorders years before visible symptoms, has raised a €10 million Series A, along with additional non-dilutive funding, bringing the total capital raised by the company to €25 million.  The round is led by Teampact Ventures, White Fund and the EIC Fund […] The post neuroClues raises €10 million Series A to become the brain’s stethoscope for early diagnosis of neurological disorders appeared first on EU-Startups.

BioAlchemy: Distilling Biological Literature into Reasoning-Ready Reinforcement Learning Training Data

arXiv:2604.03506v1 Announce Type: new Abstract: Despite the large corpus of biology training text, the impact of reasoning models on biological research generally lags behind math and coding. In this work, we show that biology questions from current large-scale reasoning datasets do not align well with modern research topic distributions in biology, and that this topic imbalance may negatively affect performance. In addition, we find that methods for extracting challenging and verifiable research problems from biology research text are a critical yet underdeveloped ingredient in applying reinforcement learning for better performance on biology research tasks. We introduce BioAlchemy, a pipeline for sourcing a diverse set of verifiable question-and-answer pairs from a scientific corpus of biology research text. We curate BioAlchemy-345K, a training dataset containing over 345K scientific reasoning problems in biology. Then, we demonstrate how aligning our dataset to the topic distribution of modern scientific biology can be used with reinforcement learning to improve reasoning performance. Finally, we present BioAlchemist-8B, which improves over its base reasoning model by 9.12% on biology benchmarks. These results demonstrate the efficacy of our approach for developing stronger scientific reasoning capabilities in biology. The BioAlchemist-8B model is available at: https://huggingface.co/BioAlchemy.

Anthropic Acquired Coefficient Bio In A $400 Million Deal (Report)

Anthropic has acquired Coefficient Bio, a stealth-stage biotech AI startup, in an all-stock transaction valued at just over $400 million, underscoring the company’s growing ambitions in healthcare and life sciences. This deal was revealed in a company letter obtained by Eric Newcomer. The post Anthropic Acquired Coefficient Bio In A $400 Million Deal (Report) appeared first on Pulse 2.0.

[AI UNRAVELED SPECIAL] ⚡Surge Compute: Optimizing Biological Hardware Through Intensity (April 03rd 2026)

🛠️ The AI Executive Toolkit: Stop scrolling through generic lists. Get the hand-picked, forensic-vetted implementation stack to bridge the gap between raw innovation and professional-grade governance. Exclusive listener perks on tools like Chatbase, ElevenLabs, AI RIA, and Google Workspace.

Anthropic Acquires Startup Coefficient Bio for About $400 Million

Anthropic acquired AI biotech startup Coefficient Bio for roughly $400 million, adding to its healthcare life sciences group.

Anthropic reportedly acquires medical AI startup Coefficient Bio for $400M+

Anthropic PBC has reportedly acquired Coefficient Bio Inc., a provider of artificial intelligence software for medical researchers. Half the startup’s stock was owned by a healthcare-focused venture capital firm called Dimension. The fund informed investors of the acquisition in a letter that was published today by journalist Eric Newcomer. According to the document, Anthropic is […] The post Anthropic reportedly acquires medical AI startup Coefficient Bio for $400M+ appeared first on SiliconANGLE. Topic group: Economics & Markets

Generare raises $23.2M to discover unknown molecules and advance new drugs

Generare Bioscience SAS, a Paris-based biotechnology company generating never-before-seen molecular data for drug development using artificial intelligence, announced today it has raised €20 million.

Paris-based Generare raises €20 million to generate novel molecular data for drug development from microbial genomes

Generare, a Paris-based BioTech startup generating novel, high-quality molecular data for drug development by decoding microbial genomes, has raised €20 million in Series A funding to increase its drug discovery compound library, grow its team and scale its discovery platform.  The round was co-led by Alven and Daphni with participation from all existing investors, including […] The post Paris-based Generare raises €20 million to generate novel molecular data for drug development from microbial genomes appeared first on EU-Startups. Topic group: Adoption & Impact

Paris-based Generare raises €20 million to generate novel molecular data for drug development from microbial genomes

Generare, a Paris-based BioTech startup generating novel, high-quality molecular data for drug development by decoding microbial genomes, has raised €20 million in Series A funding to increase its drug discovery compound library, grow its team and scale its discovery platform.  The round was co-led by Alven and Daphni with participation from all existing investors, including […] Topic group: Adoption & Impact

The deep-tech founder using AI to address immunology challenges

Camille Bouget discusses how artificial intelligence is impacting innovation in the treatment of diseases affecting the immune system.

AI-led selloff in contract research firms may be misjudging disruption risk

The AI-led selloff in contract research firms may be misjudging disruption risk.

Eli Lilly signs $2bn deal for AI drug development with Hong Kong biotech

Global pharmaceutical companies are aggressively searching for new medicines in China

Eli Lilly strikes $2.75B deal for AI drug development

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Eli Lilly Partners with Insilico Medicine

Eli Lilly partners with Insilico Medicine in a $2.75 billion deal, securing AI-driven drug discovery capabilities to enhance R&D efficiency.

Petasight Acquires Babbage Insight for AI-Led Operations

Petasight Inc. has acquired Babbage Insight Two Inc. to enhance its AI-led operating system for life sciences PMR.

Breakout Ventures: $114 Million Fund III Launched To Back Frontier Science Startups

Breakout Ventures has announced the close of its $114 million Fund III, marking a new phase of investment focused on science-driven startups at the intersection of biology, chemistry, and artificial intelligence. The post Breakout Ventures: $114 Million Fund III Launched To Back Frontier Science Startups appeared first on Pulse 2.0.

Ternary Therapeutics Raises €4.1M for AI-Powered Drug Discovery Platform

Ternary Therapeutics, a London-based biotechnology company, has raised €4. 1 million (£3. 6 million) in a seed funding round to enhance its AI-powered platform for drug discovery.

Persistent Systems and NVIDIA Unite to Revolutionize AI-Driven Drug Discovery

Persistent Systems partners with NVIDIA to enhance AI-driven drug discovery, using NVIDIA's AI platform for more efficient healthcare and life sciences solutions.

Vst Bio Raises $45M for AI-Powered Stroke Therapy

Vst bio, a biotechnology company focused on vascular diseases, has closed a $45 million Series A financing round to advance its lead stroke therapy candidate, vb-001. The round was led by Coefficient Giving, a philanthropic funder dedicated to impactful scientific research.

Sequential Raises $3.5M to Enhance AI-Powered Skin Microbiome Discovery

Sequential, a genomic testing company specializing in non-invasive human clinical samples, has raised $3. 5 million in its first equity funding round, increasing its total funding to $7. 5 million.

Ternary Therapeutics targets “undruggable” proteins with AI-designed molecular glues, raising €4.1 million

London-based BioTech startup Ternary Therapeutics has raised €4. 1 million (£3. 6 million) in Seed funding to scale an AI platform designed to create a new class of medicines known as molecular glues.

AI-Powered mRNA Vaccine Shrinks Tumor

A rescue dog with cancer saw significant tumor reduction after receiving an AI-designed personalized mRNA vaccine. Though promising, experts urge caution due to regulatory and ethical concerns in AI-assisted medical treatments.

AI Used to Help Dying Dog

A tech entrepreneur used AI to help prolong the life of his dying dog. Read the full profile to learn more about this heartwarming story.

Pharma Giants Invest in AI

Leading pharmaceutical companies and biotech firms are heavily investing in AI platforms to revolutionize drug discovery and development.

ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics

Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI systems lack direct access to transcriptomic representations while expression foundation models remain opaque to natural language. Here we introduce ELISA (Embedding-Linked Interactive Single-cell Agent), an interpretable framework that unifies scGPT expression embeddings with BioBERT-based semantic retrieval and LLM-mediated interpretation for interactive s...

First Multi-Behavior Brain Upload

Eon Systems is framing its work as an early whole-brain emulation milestone, building on the fruit fly connectome research that mapped roughly 139,255 neurons and 50 million synaptic connections and on executable fly-brain models that researchers say can help study multiple interacting neural circuits. This is intellectually striking as a possible long-term path beyond today's LLM paradigm, but the immediate business implications remain speculative and the work is much more relevant to neuroscience and basic research than near-term enterprise deployment.

Large Genome Model: Open Source AI Trained on Trillions of Bases

A large genome model is an open source AI trained on trillions of bases.

Liquid AI and Insilico Medicine Partnership

Liquid AI and Insilico Medicine have announced a strategic partnership targeting AI in healthcare and drug discovery. The partnership aims to develop lightweight scientific foundation models that can run on internal compute and be tuned to proprietary biomedical data. This partnership is seen as an early example of the 'small, domain-specific models' trend and a reminder that owning AI capabilities behind the firewall can matter for competitive differentiation.

Antiverse Secures $9.3M to Expand AI Antibody Design Platform

Antiverse has raised $9.3 million in a Series A funding round to enhance its AI-driven antibody design platform and expand its therapeutic pipeline.

GenomeOcean Revolutionizes Genomic Data with AI

GenomeOcean, a JGI initiative, leverages large language models to accelerate genomic research by drastically reducing model training times, aiming for a 30–350 times improvement. With the upcoming Doudna supercomputer and collaborations with NVIDIA and Dell, GenomeOcean emphasizes open science and AI enablement to streamline genomic data processing and enhance precision medicine and environmental research.

r/bioinformatics on Reddit: Every day that I choose AI makes me feel like I'm digging my own grave

I'd say if you ask most good comp scientists how they feel about Claude code they are glad that they don't have to do as much scut coding and can focus on the fun parts of the job like project design and biological interpretation, which Claude sucks at as it has no biological intuition. Just remember, AI manipulates representations of knowledge, it does not interpret reality itself.

I think you are right on people's general misunderstanding of LLMs doing magical drug discovery discovery. But I think one of the goals is to have AI act as a generalist scientist doing experiments like a human would, but scaled. Not there yet, but real progress from Google.

I think you are right on people's general misunderstanding of LLMs doing magical drug discovery discovery. But I think one of the goals is to have AI act as a generalist scientist doing experiments like a human would, but scaled. Not there yet, but real progress from Google.

AI to Help Researchers See the Bigger Picture in Cell Biology

An AI-driven method can provide holistic information on a cell, helping scientists better understand disease mechanisms and plan experiments.

Pfizer Unveils Second AI-Driven Lab

Pfizer and Telescope Innovations have completed the installation of a second Self-Driving Laboratory (SDL), accelerating drug development with AI-guided experimentation and robotics. This move marks a significant step towards full-scale deployment, potentially slashing research costs and timelines by up to 100 times, while extending SDL's application beyond pharmaceuticals.

Pfizer Unveils Second AI-Driven Lab to Revolutionize Drug Development and Cut Costs

Pfizer unveils second AI-driven lab to revolutionize drug development and cut costs.

Flinn: $20 Million Raised To Automate The Product Lifecycle In Medtech And Pharma With AI

Flinn raised $20 million to expand AI-powered automation in MedTech and Pharma product lifecycles, focusing on regulatory and quality processes. The funding will accelerate commercialization and market reach. This investment underscores AI's role in streamlining complex industry operations.

'An AlphaFold 4’ – scientists marvel at DeepMind drug spin-off’s exclusive new AI

'An AlphaFold 4’ – scientists marvel at DeepMind drug spin-off’s exclusive new AI

The next frontier in oncology: Why survival must become a designed metric, not a passive outcome

It is no secret by now that technological advances, especially artificial intelligence, have driven profound structural changes in healthcare. From the speed at which clinical tasks are executed to the precision that reduces human error, the industry is experiencing a long-awaited transformation. In oncology, these shifts are even more pronounced.

Pharmacelera Raises €6 Million To Expand In U.S. And Advance Quantum-AI Platform

Pharmacelera, a deep tech company applying Quantum Mechanics and Artificial Intelligence to drug discovery, announced it has closed a €6 million investment round to accelerate its expansion into the United States and further develop its proprietary platform. The post Pharmacelera Raises €6 Million To Expand In U.S. And Advance Quantum-AI Platform appeared first on Pulse 2.0 .

Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. However, one major issue in molecular generative models is their lower frequency of drug-like compounds. To resolve this problem, we developed a novel framework for optimization of deep generative models integrated with a D-Wave quantum annealing computer, where our Neural Hash Function (NHF) presented herein is used both as the regularization and binarizat...

Mankind Pharma Optimizes Supply Chain Efficiency

In partnership with Accenture, Mankind Pharma has revamped its global supply chain, achieving a 75% reduction in drug stock-outs and a 20% improvement in inventory optimization. This overhaul bolsters operational resilience and supports sustainable growth, enabling the delivery of affordable medicines amid evolving healthcare demands.

Hunt Globally: Deep Research AI Agents for Drug Asset Scouting in Investing, Business Development, and Search & Evaluation

The scientist using AI to hunt for antibiotics just about everywhere

When he was just a teenager trying to decide what to do with his life, César de la Fuente compiled a list of the world’s biggest problems. He ranked them inversely by how much money governments were spending to solve them. Antimicrobial resistance topped the list.

GPT-5 Slashes Lab Costs at Ginkgo Bioworks

Deployment of GPT-5 in Ginkgo Bioworks' cloud lab has reduced protein production costs by 40% and key lab ingredient expenses by 57% across 36,000 experiments. This application of AI demonstrates substantial cost efficiencies in biotechnology operations. The savings highlight AI's role in optimizing resource use and accelerating research, potentially lowering barriers to entry in biomanufacturing and enhancing profitability for AI-integrated labs.

AI startup Phylo nabs $13.5M for its ‘integrated biology environment’

Biology software developer Phylo Inc. today announced that it has raised $13. 5 million in seed funding.

A comparison of the innovation and regulatory environments for ...

Biotechnology has significant potential to drive economic growth and address major societal challenges, but realising this potential across sectors requires.

Does MannKind Corporation stock benefit from AI growth - Portfolio Return Report & Accurate Intraday Trading Signals

MannKind Corporation's stock is analyzed for potential benefits from AI growth in the pharma and biotech sector. The report suggests opportunities for secure, high returns starting with $100 investments.