Cory Kidd

Big News at Pack Ventures 🚀🐺🧬 We’re thrilled to welcome two outstanding Venture Partners to the Pack Ventures team! Jenny Cronin, PhD and William Canestaro, PhD are two of the most respected and well-connected figures in the Seattle biotech landscape. They strengthen our mission of building the leading pre-seed biotech fund in Seattle—a city we believe is one of the most important biotech markets in the world for the convergence of A.I. and Biotech! Seattle isn’t just home to world-class institutions like the Institute for Protein Design, University of Washington, Fred Hutch, and Seattle Children's —it’s also a global hub where AI and biotech converge. From David Baker’s recent #NobelPrize to the cutting-edge work coming out of the Paul G. Allen School of Computer Science & Engineering, Microsoft, Amazon, and the Allen Institute and Ai2 , the depth of A.I. talent here is extraordinary. At Pack Ventures, we’re doubling down on the Seattle and UW innovation ecosystem. Our belief is simple: the next generation of industry-defining biotech companies will be built here—and we’re here to help founders make that future a reality. With our new Venture Partners on board, we’re better equipped than ever to support early-stage founders transforming science into scalable solutions. #Biotech #VentureCapital #AIxBio #SeattleInnovation #PackVentures #UWSpinouts #TechBio

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Excited to announce that Potato has new funding led by Draper Associates, to build a fully-autonomous AI scientist. "With $4.5 million in fresh funding, Seattle-area startup Potato is sprouting big plans to disrupt scientific research with a fully automated experimental process." https://lnkd.in/gMtY8agr I'm excited to be working with great co-workers David Welte, Ansel Santosa, and Ambrose Sterr again.

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We are thrilled to share our partnership with Peer AI, a pioneering agentic AI platform transforming life sciences regulatory documentation, and its co-founders (Anita Modi, Christopher Ceppi, and Ravi Ramachandran). Documentation is a consistent and mission-critical workflow across every stage of drug development. Each new drug candidate generates 100,000 to 200,000 pages of regulatory documentation — ranging from toxicology reports and study protocols to clinical study reports and investigator brochures. The current state of medical writing is manual, slow, and error-prone. Nearly one-third of FDA submissions contain data quality issues, and more than 70% of IND, BLA, and NDA applications are initially rejected due to incomplete or inaccurate data. These inefficiencies cause costly delays, non-compliance risks, and bottlenecks to getting life-saving therapies to patients. Peer AI is building a purpose-built, agentic AI platform that automates the end-to-end process of regulatory documentation — data extraction, authoring, and quality control. The “agent team” is combined with an intuitive AI-powered user interface that allows medical writers to apply their subject matter expertise at critical control points during documentation workflows. The platform integrates seamlessly into existing workflows: medical writers can upload or integrate into systems with structured and unstructured data, apply configurable templates, and generate drafts ready for multi-author review. We are so thrilled be partnering with Peer AI and our co-investors, including SignalFire (Sooah Cho & Wayne Hu), Greycroft (Brentt Baltimore), and other phenomenal institutional and angel investors. Congrats! ~~ Flare Capital Partners, Uma Veerappan, Varsha Rao https://lnkd.in/eawx_n4u

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Introducing Boltz-2, an open-source model from MIT and Recursion that predicts both protein structures and binding affinities. It reaches free-energy-perturbation (FEP) accuracy at ~1,000× the speed. Use it on Tamarind Bio today: https://lnkd.in/gqnAG8Tz Binding affinity determines whether a drug is potent enough; FEP is precise but slow, while docking is fast but noisy. Deep-learning alternatives have lagged, until now. Performance: • Matches OpenFE on the standard FEP+ benchmark while running 1,000× faster. • Tops the CASP16 affinity challenge (140 complexes). • In MF-PCBA hit-discovery screens, doubles average precision over ML and docking. Coupled with SynFlowNet, Boltz-2 powered a prospective TYK2 screen; ABFE predicted binding for all top-10 generated compounds. For structure prediction, Boltz-2 Matches or exceeds Boltz-1, with major improvements on DNA–protein, RNA, and antibody–antigen complexes. Specify contact constraints, templates, or experimental methods directly in the model. Architecture upgrades: Builds on Boltz-1 with a new affinity head, finer controllability, GPU optimizations, and extensive synthetic + MD training data. Boltz-2 brings FEP-level accuracy to deep learning, at a fraction of the compute cost, and slots cleanly into generative screening pipelines. Excited to see how Boltz-2 will be used for faster small molecule discovery! Code & model: https://lnkd.in/ezDA9pji Paper: https://bit.ly/boltz2-pdf

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Everyone in pharma is racing to launch generative AI tools, even the FDA. However, some things in life take time to flourish… 📈⚕️ As journalist Natalia Mesa outlines in her recent take for BioSpace the FDA’s recent rollout of Elsa, a large language model designed to help with clinical protocol reviews and scientific evaluations, raised some red flags ⚠️. According to Mesa’s reporting, FDA insiders described the implementation as rushed, lacking proper oversight, and prone to incorrect or partially accurate outputs. Some said Elsa wasn’t ready. Others questioned how decisions made with its help could hold up under legal or scientific scrutiny. I don’t know for sure, won’t speculate on FDA’s moves. But I can say one thing clearly: it took a decade for Insilico Medicine, to get to the most advanced and well-validated AI platform in the industry, PharmaAI. Steadily building, testing, and validating…it takes time to grow organically 🌿. For those curious, here is a brief historic note: 🧬 2014 – Founded to bring AI into aging and medicine. 📄 2016–2019 – Published foundational work; GENTRL, their early generative model, created viable molecules in under a month. 🧪 2020–2021 – Launched PharmaAI, combining tools for target discovery (PandaOmics), molecule generation (Chemistry42), and trial prediction (InClinico). 💊 2022 – AI-designed anti-fibrotic drug ISM001-055 entered Phase I trials. 📈 2023–2025 – That same drug, now named Rentosertib, reached Phase II trials—with results published in Nature Medicine in June 2025 confirming safety and initial efficacy in patients. 🚀 Over just a span of several years, PharmaAI platform enabled discovery and design of more than 30 preclinical and clinical drug candidates, and multiple new targets (see image). The lesson here is this: Insilico didn’t skip steps. It published results and AI platform mechanics relentlessly, validated with its own pipeline and client’s projects, and iterated, it took time and patience. Their tools evolved from basic algorithms to a trusted enterprise platform used by major pharma players across the globe… I think Alex Zhavoronkov and Alex Aliper could one day write a book about this roller coaster ride! Meanwhile, I feel like the FDA’s experiment with Elsa shows what happens when there is a push for a quick win in a complex area like medical AI, something Mesa’s reporting hints to… I hope they figure things out fast, the FDA is one of the most important organizations in the world, IMO. We all need them to be doing great, because the health and lives of many-many people depend on that 🙏🩺 Image credit: Insilico Medicine

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