Latest TechBio Update 👾💻
Cradle Bio 🏗️
Cradle, the enterprise-grade AI platform for protein engineering, just announced that Marcus Schindler, Ph.D. has joined its advisory board to help accelerate the platform’s scientific impact and drive broader adoption of AI-powered protein engineering across the life sciences industry.
✅ Schindler previously served as Executive Vice President for Research & Early Development and Chief Scientific Officer (CSO) at Novo Nordisk, where he oversaw a broad pipeline spanning multiple therapeutic areas and modalities, advanced numerous programs from discovery into clinical development, and strengthened external collaborations to expand access to emerging technologies and scientific capabilities.
✅ Cradle (based in Amsterdam, The Netherlands; Zurich, Switzerland; and the United States) accelerated the adoption and impact of its platform in 2025, which now powers more than 50 protein R&D programs worldwide. The company partners with 6️⃣ of the top 2️⃣5️⃣ global biopharma organizations, including Johnson & Johnson, AbbVie, and Novo Nordisk, and kicked off 2026 announcing a new partnership with Bayer to accelerate antibody discovery.
✅ Cradle deploys a traditional SaaS model that enables in-house talent to easily use integrated tools for protein engineering various modalities including enzymes, peptides, vaccines, and antibodies while retaining all asset rights. Customers are using Cradle’s platform to speed up 🏇 early research and development from 2️⃣ to 1️⃣2️⃣ times, while driving significant cost efficiencies across a diverse range of programs. Cradle is backed by IVP as well as Index Ventures and Kindred Capital.
🗣️ “Marcus is renowned for modernizing drug discovery at some of the world’s largest pharmaceutical companies. He has been transformative in building thriving cultures that turn technology into a genuine competitive advantage, disrupt drug discovery to deliver better outcomes and adopt AI solutions at larger scale. We’re excited to work closely with Marcus and draw on his deep experience driving R&D innovation to apply AI in practical, meaningful ways across biologics discovery and development” .
said Stef van Grieken, co-founder and CEO of Cradle.
🗣️ “Cradle’s team stands out because they appreciate the reality of complex drug discovery and are driving robust, reliable and repeatable results for scientists at the bench. I’ve seen firsthand how their platform can go beyond pilot programs to scalable applications. Due to the nature of their business model this enables the simultaneous building of complementary state-of-the-art AI capabilities internally and I believe this represents the future of AI-powered biotech solutions.”
said Marcus Schindler.
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Bio x AI Hackathon 🎡
Y Combinator will be hosting a Bio + AI hackathon in March 8, 2026 | 8:00 AM – 8:00 PM PT | YC HQ, San Francisco, organized by two awesome 😎 companies BioRender and Tamarind Bio.
✅ Whether you’re a scientist, engineer, or ML researcher curious about life sciences, you can spend the day hacking on problems that actually matter alongside others building the future of bio. Tamarind Bio and BioRender are bringing together builders at the frontier of biology and AI for a full day of hacking at YC HQ in San Francisco, sponsored by Anthropic, OpenAI, and Modal.
✅ Tamarind, that provides web interfaces and APIs for the leading publicly available molecular design tools so users can run models like AlphaFold/Chai-1, RFdiffusion, ProteinMPNN and 200+ others with up to hundreds of thousands of inputs running in parallel, just announced its $13.6M Series A, led by Dimension, with participation from Y Combinator.
BioRender is revolutionizing how science is communicated, one graphic at a time (you can think of them as the Adobe or Canva for science), with a team of passionate scientists, engineers, science illustrators, designers and entrepreneurs backed by an incredible group of investors, including YCombinator.
What you’ll build 🔎
Explore these high-impact areas, or bring your own idea to the table:
AI Scientist - Build an autonomous agent that can reason over biological data, search literature, design experiments, or interpret results end-to-end on a real research problem,
AI Drug Discovery - Apply bio-ML models to a specific drug discovery challenge such as target identification, binder design, or property prediction,
Biological Data Infrastructure - Build scalable data infrastructure and orchestration tooling that transform massive, heterogeneous biological datasets using reproducible pipelines,
Scientific Data Visualization - Generate accurate and compelling visualizations from raw biological data to surface insights a scientist couldn’t see otherwise, and
Bring your own ambitious idea at the intersection of AI and life sciences.
Why attend❓
Build at the intersections of AI and science, guided by real challenges from the field,
Spend the day at Y Combinator HQ alongside founders, researchers, and engineers pushing the boundaries of what’s possible in bio, to
Compete for cash prizes and credits!
Prizes include:
$6000 in cash prizes,
$5k in Claude credits,
$5k in ChatGPT credits,
$20k in Modal credits, and
Guaranteed Y Combinator Interview.
Participation is 🆓.
Breakfast 🍳🥐, lunch 🥙🥪, dinner 🍝🥦, snacks 🥨🍫, and drinks 🍶🍵☕🥤🍷 are provided throughout the day.
Space is limited to 1️⃣5️⃣0️⃣ attendees.
Claude, ChatGPT, and Modal credits will be provided during the event to all participants.
Deep Origin and Arctoris 🤝
Arctoris and Deep Origin have been selected for the Advanced Research + Invention Agency (ARIA) AI Scientist programme, one of the UK’s most competitive innovation initiatives.
✅ The joint effort will develop an AI-enabled autonomous scientist to accelerate research into endometriosis ♀️🤷♀️, a disease affecting millions globally yet still underdiagnosed and underfunded. Progress in this field requires more than better models or more experiments in isolation. It requires tight integration between computation and high-fidelity experimental data.
✅ By combining Deep Origin’s computational drug discovery capabilities with Arctoris’ autonomous Ulysses® platform (that generates reproducible, AI-ready wet lab datasets designed to power modern discovery workflows), the two can convert in silico candidates into validated biological insight in a fraction of the usual time. This collaboration will translate ARIA outputs into robust experimental campaigns that de-risk early discovery and accelerate progression from hit to candidate.
OpenScholar 📚
OpenScholar is outperforming general LLMs in scientific literature reviews with zero hallucinations, as researchers call for a 🆕 publication framework that is natively machine-readable.
✅ Research funding in the United States is at an inflection point. Federal support remains foundational, yet increasingly constrained by ongoing funding cuts, shifting priorities, prolonged review timelines, and growing competition across disciplines. At the same time, researchers are being asked to expand impact, collaborate across fields, and seek alternative funding—often without additional time or infrastructure.
✅ The result is a funding landscape that is not only fragmented, but unsustainably burdensome.
✅ Identifying relevant opportunities requires navigating a complex mix of federal agencies, foundations, nonprofits, and international funders—each with distinct goals, language, and expectations. So discovery has become as challenging as the application process itself.
✅ Accordingly, a new solution is required that supports funding opportunity 💰 alignment while reducing the burden of the current system at scale.
✅ For this reason, at OpenScholar they built OS Match as a response to that need. It is grounded in a deep understanding of how a researcher’s unique profile is established online and how funding opportunities are structured, providing a foundation for smarter, more efficient discovery—so researchers can spend less time searching and more time advancing knowledge.
✅ OpenScholar is the research collaboration platform used by over 40,000 scientists and scholars globally. Organizations from Harvard Medical School, UVA, University of South Carolina, U Mass Boston, Beth Israel Hospital, Joslin Diabetes all leverage THIS platform to turn their important research into content marketing. Built by researchers for researchers, OpenScholar helps hospitals, universities, labs and research centers raise their online visibility quickly in a scalable, cost-effective manner. The results are measurable: attract more funding, talent and partnerships.
Lilly unveils supercomputer to expand AI drug discovery push 🦸♀️
US drugmaker Eli Lilly (NYSE: LLY) has launched a 🆕 NVIDIA-powered supercomputer, dubbed LillyPod, as part of a broader effort to embed AI across its research and manufacturing operations (Lilly unveils supercomputer to expand AI drug discovery push).
✅ The system is built on NVIDIA’s DGX SuperPOD B300 architecture and includes over 1,000 of the tech giant’s leading Blackwell chips. Lilly describes it as the most powerful supercomputer in the pharmaceutical sector.
✅ LillyPod will be integrated with the companies’ recently announced AI co-innovation lab in the San Francisco Bay Area, where Lilly scientists work alongside NVIDIA engineers. The partners have pledged up to $1 billion over five years to support talent, infrastructure and computing capacity.
Designed to help you reduce stress and anxiety, be creative, and stay focused, with microdosing (Lion’s Mane mushroom 🍄 and L-theanine from green tea leaves).
Clarity by Flowdose ☺️
Reduce your Stress with Micro Coffee ☕ (Delicious blend of ✅ medium roast Arabica coffee, ✅ probiotics, ✅ cacao and ✅ organic mushrooms, designed for enhanced focus, improved mood, gut health, and jitter-free energy) and Belgian Dark Chocolate 🍫 (Made with dark chocolate imported from Belgium: fast acting, clean and precise dosing).
What caught my eye most is the shift toward infrastructure. Platforms like Cradle and systems like Lilly’s new supercomputer suggest the real race is being won in compute and tooling, not just models.