Marc Moesser

Epsilogen, a biotech firm based in the UK, has taken over the US company TigaTx to form a leading entity in "pan-isotype" cancer antibodies. This merger integrates Epsilogen's proficiency in IgE antibodies with TigaTx's specialization in IgA antibodies, enabling them to choose the most suitable antibody for specific cancer types. The merged pipeline now includes Epsilogen's MOv18 IgE, which is currently in Phase 1b clinical trials for ovarian cancer, and TigaTx's EPS 401 (previously known as TIGA-001), an anti-EGFR IgA antibody anticipated to commence clinical trials next year. This collaboration aims to enhance cancer treatments by employing various antibody isotypes to engage different elements of the immune system.

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📣 Barclays Highlights M&A Catalysts for 4D Molecular Therapeutics 💊 4D-150 🏢 4D Molecular Therapeutics, Inc. 🫀 Wet AMD Barclays recently initiated coverage of 4D Molecular Therapeutics, Inc. (FDMT) with an Overweight rating and a target price of $33, highlighting the potential for the company amid solid biotech fundamentals. An anticipated increase in mergers and acquisitions within the market, alongside a reduction in price sensitivity for medicines, presents strategic tailwinds for FDMT in 2026. The company, known for its innovative Therapeutic Vector Evolution platform, is actively advancing its clinical studies, including the 4FRONT Phase 3 trials for 4D-150 aimed at treating Wet Age-related Macular Degeneration (AMD) and upcoming trials for diabetic macular edema as well as developments in its cystic fibrosis program. Financially, the biotech sector's current undervaluation compared to potential upside, particularly post-M&A activity, reinforces Barclays' bullish outlook on FDMT. While their potential is noted, there is an acknowledgment of alternative high-growth sectors, specifically AI stocks, which might present more favorable risk-reward scenarios for investors. For biopharma strategists, this suggests a need to balance potential investments in innovative biotechnologies like FDMT against rapidly evolving sectors that may also capture market attention due to their strategic positioning in a shifting economic landscape. Continuous updates on FDMT’s clinical trials and market reception will be critical in assessing its investment viability as the industry dynamics unfold throughout 2026. Read full article: https://lnkd.in/gFDhhshB #ophthalmology #4D150 #4DMolecularTherapeuticsInc

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The UK has large reserves of lead-212, a radioactive isotope and an untapped national asset with extraordinary medical potential. The UK's National Nuclear Laboratory (UKNNL) has developed a successful protocol to harvest lead-212 from recycled nuclear fuel. This secures a sustainable, domestic supply of a critical isotope for a raft of new precision cancer medicines called Targeted Alpha Therapies. Pentabind is one of the members of the consortium and will be applying its AI aptamer design platform to support the development of aptamer-based targeted alpha therapy using lead-212. Massive congratulations to Rory Ryan Phil Haynes Miguel Gonzalez Canudas, PhD and the whole team PentaBind - this is immensely exciting

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One of the most exciting developments in applications of #quantumcomputing I've seen this year ! In the words of Jean-Philip Piquemal "we’re already starting to use quantum-computing algorithms to generate data to enhance our models. Leveraging partnerships with quantum hardware companies, more data will continuously be added in the future. What people thought would take until 2035 is already starting to happen today.” Combining #HPC, #AI and QC is a game changer for materials and drug design, and we're barely starting to understand the potential here, with Qubit Pharmaceuticals in the lead. The second blog in the series is out: https://lnkd.in/etK3crnY and I look forward to the next ones. To read absolutely, two preprints : - A Foundation Model for Accurate Atomistic Simulations in Drug Design https://lnkd.in/e765zWc4 - Pushing the Accuracy Limit of Foundation Neural Network Models with Quantum Monte Carlo Forces and Path Integrals https://lnkd.in/eT6RYfgA Robert Marino Anouar Benali

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Drug discovery is famously slow, costly, and high-risk. However, Eli Lilly’s new “TuneLab” platform is changing the equation — providing smaller biotech companies with access to AI-powered R&D models that reduce time and costs. What’s striking here is that the breakthrough isn’t only in science, it’s in commercialisation. By making cutting-edge AI tools accessible, smaller companies can now compete at a level once reserved for pharma giants. That’s exactly the kind of inflexion point where investors can step in and help accelerate growth. At Rex, we look for this combination: brilliant innovation paired with the need for smarter scaling. Whether it’s advanced biotech or a sustainability startup rethinking manufacturing, we help founders build the commercial foundations to match their technical vision. 📣 If you’re a founder with disruptive technology but struggling to commercialise at pace, we’d love to connect. https://lnkd.in/e-9a4EpX

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🧮 What if you could precisely target the “undruggable” proteins that drive cancer — using AI-first drug design? Axiom Therapeutics brings computational precision to traditional chemistry. Their machine learning-powered platform can model covalent drug interactions, enabling the identification of small molecules that bind previously untreatable cancer proteins. Why does it matter? Despite significant advances in AI-enabled drug design for known targets, traditional discovery remains slow, costly, and largely ineffective against so-called “undruggable” proteins. Axiom’s platform aims to reproduce the successes of non-covalent drug design in the covalent space - accelerating discovery and expanding the frontier of tractable targets. 🔍 Axiom’s breakthrough: ✅ AI-driven models for covalent binding modellisation ✅ Computationally-powered Lab-in-the-loop approach ✅ Unlocking undruggable targets with next-gen small molecule discovery 🔗 Discover the science behind Axiom: https://lnkd.in/d8VQdd58 🚀 Don’t miss our upcoming webinar: DEEP TECH DEEP DIVES | Verve's Health and Bio Approach in Action 📅 July 28, 2025 - 🕛 12:00PM CET 👉 Register now https://lnkd.in/drU3_vu5 #Neurotech #Epigenetics #FutureOfMedicine #Biotech #VerveVentures #AxiomTherapeutics #RettSyndrome #HealthTech #BrainScience #DeepTech

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𝗕𝗶𝗼𝘁𝗲𝗰𝗵 𝗶𝘀 𝗲𝗻𝘁𝗲𝗿𝗶𝗻𝗴 𝗮 𝗺𝗼𝗺𝗲𝗻𝘁 𝗼𝗳 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗿𝗲𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁. Last year I wrote about where the biotech ecosystem might be headed by 2035- new research and policy updates call for a new revamp of latest trends in the AI x bio intersection. A few highlights: 🔬 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗶𝗻𝗴 𝗱𝗿𝘂𝗴𝘀: Multimodal models are combining biological and clinical data to create richer patient-level representations, but the debate remains on whether diffusion or autoregressive models are better suited for biological characterization. At the same time, agent-to-agent research systems like "Virtual Biotech" recently released from Stanford are beginning to orchestrate dozens of specialized biological models inside unified workflows. 🏭 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗱𝗿𝘂𝗴𝘀: AI and automation are moving beyond R&D and deeper into the bioprocessing and manufacturing stack. OpenAI + Ginkgo system recently designed and executed iterative protein synthesis experiments autonomously, cutting reaction costs ~40%, while Lilly's latest digital twins of manufacturing factories materially increased peptide output. Meanwhile, regulatory conversations around Bayesian trial design and the FDA's reduction of the “two-trial” approval paradigm could reshape clinical development timelines. 💊 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝗱𝗿𝘂𝗴𝘀: Consumer health platforms are acceleratingly becoming data ecosystems. Companies like ŌURA are exponentially partnering with institutions on continuous biometrics, lab testing, and frontier LLMs to build longitudinal health profiles - pushing care closer to continuous monitoring and earlier intervention. Virtual care clinics increasingly focus at the intersection of mental and physical health, with tailwinds from CMS' new ACCESS program. Read more below 👇

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Chai Discovery has closed a $130M Series B at a $1.3B valuation to scale its AI-native foundation models for drug discovery, accelerating molecular design following the launch of Chai 2. https://lnkd.in/eerDPtEK #AI #BioTech #DrugDiscovery #FoundationModels #GenerativeAI #Funding Lachy Groom OpenAI General Catalyst Oak HC/FT Menlo Ventures Thrive Capital SV Angel Yosemite Glade Brook Capital Partners LLC Emerson Collective

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Biological Foundation Models (BFMs) might finally deliver on AI’s promise in drug discovery -- but capturing their full value needs a smarter playbook. It's hard for AI-for-biotech ventures to bet solely on software licensing or fully owned assets. I believe BFMs (think LLMs for biology: trained on DNA, RNA, proteins, molecules) offer a unique opportunity to blend approaches: ⏲️ Short term: sell software/services early to prove tech, earn revenue, and accelerate learning cycles. ⏳ Long term: leverage proven tech to build a proprietary drug pipeline, capturing far greater value downstream. Examples already emerging: EvolutionaryScale ($142M raised), Latent Labs (~$50M), SandboxAQ. These ventures show BFMs aren’t science fiction—they’re here, well-funded, and gaining momentum. Some of my takeaways: 👑 Proprietary datasets and model IP remain key differentiators. 📈 Pharma-scale risks require patience, but rewards scale similarly. ✌ Focus on startups executing hybrid models: immediate SaaS/services revenue + long-term pipeline ambitions. BFMs aren’t just another AI hype cycle—they could finally crack the biotech value conundrum. For some great insights read Maggie Basta's full article: https://lnkd.in/eNbbnYPS

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We're proud to be featured in the European Dynamism report by redalpine and Sifted, recognised as a #startuptowatch in Unlocking Biology. The report highlights DaltonTx as part of the next wave of AI infrastructure for drug discovery: building a third generation AI drug design platform for collaborative intelligence, based on the extensive industry expertise of our founders. As our CEO Garry Pairaudeau puts it: "I want people to view AI as an opportunity to supercharge human expertise."   Europe has the scientific depth to lead in biology. We are building the platform that makes it happen. #AIdrugdiscovery #collaborativeintelligence #startuptowatch

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The $7.8B question for synthetic data in biotech https://lnkd.in/e7RSsSUt Generative AI in drug discovery has reached an important crossroads. In H1 2025, $7.8B was invested in synthetic data approaches, with projections for 2030 ranging from $1.5B to $20.3B. On one side, we see progress: 31 AI-discovered drugs are in human trials, and early results such as Insilico’s Rentosertib (+98.4 mL FVC in Phase IIa) highlight clinical potential. On the other, challenges remain—95% of pilots fail to scale, bias persists in synthetic patient datasets, and regulatory frameworks are still emerging. The takeaway: synthetic data is moving from concept to implementation, but the field needs robust validation, bias mitigation, and regulatory clarity before it can deliver on its promise at scale. #biotech #syntheticdata #drugdiscovery #genAI #clinicaltrials #pharma #healthtech

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Grounding your AI-driven discovery tools with an exhaustive compound library. Had a great discussion with Martin Razuks Ebels of MolPort at SLAS on the value of their established compound libraries. Access to a high-quality, real-world compound library can streamline the downstream phases of next-gen discovery engines. MolPort’s curated, in-stock database of 5 million screening compounds and 860K building blocks aggregates inventory from over 80 suppliers into a single, reliable source that you can access via API (or download SDF/CSV files for direct integration into your modeling pipelines. Arriving at known compounds also speeds procurement for wetlab validation. Besides training generative models, their library is also ideal for running virtual screens or sourcing building blocks for hit-to-lead campaigns. Catch up with Martin directly (martins.razuks@molport.com) or check out www.molport.com #AI #DrugDiscovery #Cheminformatics #MolPort #MachineLearning #MedicinalChemistry

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The UK’s newly published AI for Science Strategy sets out a decisive vision: scientific discovery must become data-rich, automated, and engineered for scale. This aligns directly with the principles Arctoris was founded upon. At the heart of the strategy is a recognition that the next generation of AI models in drug discovery will be constrained not by compute, but by data quality. High-resolution, reproducible, metadata-complete experimental datasets are now the critical limiting input for progress. These datasets cannot be generated reliably through manual workflows. They require automation, rigorous standardisation, and an engineering-first approach to experimentation. This is the core of Arctoris’ platform. By combining robotic execution with structured data capture, we produce the precise, consistent datasets modern AI demands. Our automated infrastructure transforms experimentation into a scalable, predictable process, capable of powering advanced protein-ligand predictions, biologics developability modelling, and rapid preclinical optimisation. The UK is uniquely positioned to lead this transformation. The country benefits from world-class AI research, a sophisticated drug discovery community, and a deep-tech startup and scale-up ecosystem that understands how to industrialise science. These are strategic national assets. The government’s commitment to autonomous labs, AI-ready datasets, and mission-driven drug discovery builds on strengths that can define the UK’s competitive advantage for decades. Arctoris is proud to be part of this momentum. The alignment between policy, scientific need, and technological capability has never been stronger. This is a pivotal opportunity to build a globally leading, AI-enabled discovery industry that delivers faster innovation, stronger economic productivity, and meaningful advances in human health. We welcome the clarity and ambition shown by the Department for Science, Innovation and Technology, and we look forward to contributing to the future it envisions. #AIDrugDiscovery #AutonomousLabs #DataGeneration #EngineeringBiology #NextGenBiotech #TechBio

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TechBio Funding Brief Dec25 – March26 Capital is flowing — but the story has shifted from “AI novelty” to deployment, integration, and translational proof. I think recent Series financings across techbio show investors backing platforms that close the loop between computation and execution: AI-native discovery engines, lab automation/“AI-ready” infrastructure, and clinically anchored precision platforms. The press consistently frames the best rounds as execution capital (scale, customers, IND readiness), not just model R&D. Quick Snapshot AI discovery & protein design Converge Bio — Series A — $25M (Jan 2026): GenAI drug discovery; press highlighted investor signal + top-tier operator angels. Galux — Series B — $29M (Feb 2026): AI protein design; framed around strengthening platform + advancing pipeline. Precision platforms (data + ML in drug development) Precede Biosciences — Series B — $63.5M equity (+$20M credit) (Jan 2026): liquid biopsy + ML for precision oncology development; coverage emphasized embedded pharma workflow value. Lab infrastructure & “model-to-lab” scaling Automata — Series C — $45M (Jan 2026): operating system for AI-ready labs; press focused on throughput + industrial validation (e.g., Danaher involvement). Medra — Series A — $52M (Dec 2025): “Physical AI scientists” (AI + robotics + closed-loop experiments); positioned as category creation for autonomous labs. Human models & reproducible biology bit.bio — Series C — $50M (Jan 2026): programmable human cell types; framed as scale-up capital for industrialising reproducible biology (and AI training data). Delivery / genetic medicine adjacency BreezeBio (GenEdit) — Series B — $60M (Feb 2026): non-viral delivery platform + pipeline; coverage leaned into “toward the clinic” execution narrative (plus rebrand signal). Clinical AI (deployment capital) Brainomix — Series C extension — +$6.5M (to $25.4M total) (Feb 2026): imaging AI; framed as commercial expansion and deployment. What the press are saying:  “Integration > imagination.” Stories that win coverage are those already tied to pharma R&D workflows: decision support, reproducible lab outputs, and deployment-ready infrastructure.  “The model-to-lab gap is the real bottleneck.” Automation + orchestration + data layers are increasingly portrayed as the missing substrate that makes AI claims real.  “Clinical proximity is the credibility multiplier.” Rounds are framed as proof of execution: IND-enabling work, scalable operations, and measurable productivity gains — not architecture hype.

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Michael Kappler, PhD, PhD (Director & Head of Research Informatics at IDEAYA Biosciences) revealed how his team is delivering treatments to clinic 10-30% faster using an integrated computational platform. ✨ Key highlights: 🌠 Integrated Collaborative Drug Discovery - CDD Vault and #StarDrop alongside other tools as part of their AI/ML-driven approach 🌠Integrated our workspace for molecule design & optimisation 🌠Data-driven decision making on compound progression 🌠Significant reduction in development time and costs Watch the full recording at: https://lnkd.in/e8b7Wyev

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