How Startups Are Innovating AI-Designed Medicines

Google DeepMind Spinout Isomorphic Labs Nears Human Trials for AI-Designed Cancer Drugs: 💊The company’s platform is powered by AlphaFold3, the latest iteration of DeepMind’s Nobel-winning AI that predicts protein structures and models drug-target interactions 💊Its lead candidates, including cancer drugs, are currently moving through preclinical development, with human trials expected to begin soon 💊The goal isn’t just one breakthrough drug, but a general-purpose AI engine that can be applied across multiple diseases and modalities 💊The company aims to improve speed, cost, and success rates in drug discovery, reducing pharma’s current 10 percent trial success odds. AlphaFold gives scientists a head start by predicting how well a molecule might bind to a disease-relevant protein target, a key early step in drug design 💊Isomorphic ultimately hopes to turn drug discovery into something closer to design automation: “click a button, get a candidate,” with AI doing the heavy lifting. If successful, it could reshape not just timelines, but how pharma allocates resources and defines early-stage risk 💊Isomorphic has raised $600 million (led by Thrive Capital) and signed major R&D deals with Novartis and Eli Lilly and Company, supporting both external and in-house drug programs #DigitalHealth #AI #Pharma

This new AI tool can design antibody drugs 100x faster than traditional methods - without trial and error. It’s called Chai-2. And it might quietly change how we discover drugs. Here’s why: Normally, designing an antibody drug takes months of testing in the lab - just to find one molecule that works. But Chai-2, an AI model built by Chai Discovery, skips that entire process. It watched hours of real-world data and learned to generate antibody candidates from scratch - with atomic-level precision. This new AI tool can design antibody drugs 100x faster - and with zero trial-and-error. In early tests on 52 new antigens, it produced viable hits for 50% of them - in just two weeks. That too without any screening or manual lab trials. The team calls it “Photoshop for molecules.” And the analogy makes sense - it lets researchers design antibodies with programmable control, rather than waiting for randomness to deliver results. This matters for two big reasons: ▶ 1. It’s faster and cheaper Fewer experiments means lower R&D costs and faster GTM. Especially powerful for early-stage biotechs running on tight timelines and capital. ▶ 2. It’s not just about antibodies Chai-2 can design miniproteins, explore new formats, and expand the kinds of molecules we can even consider therapeutically. As a funding coach and investor in healthtech, I see a signal here: → Founders who can compress the drug discovery loop - even slightly - will unlock investor confidence faster. Would you bet on a drug designed by AI if it meant saving months (and millions)? #entrepreneurship #healthtech #innovation

#AI is shattering the drug-development clock cutting work that once took a decade (and ≈ $2.6 billion per approval) down to a fraction of the time What the new pace looks like: • Discovery in months, not years. #Insilico Medicine, #Recursion and #Exscientia now reach a pre-clinical candidate in just 9-18 months instead of the traditional 40-50 months log. • Fewer molecules, smarter picks. Insilico tests 60-200 compounds per project; old-school programs often screen 3,000-5,000 • First AI-designed drug in the clinic. DSP-1181 went from concept to Phase 1 in 12 months (vs 4-5 years) • #AlphaFold speed run. Researchers used the model to pinpoint an ideal lead in 8 hours—a task that normally lasts a month • Clinical trials on the near horizon. Google #DeepMind’s Demis Hassabis expects multiple AI-designed drugs to enter human studies before the end of 2025, forecasting timelines that drop from years to “months or maybe even weeks” Why this matters: quicker pivots on promising science, lower attrition, and potentially fairer pricing when R-and-D costs fall. Your take: Will AI-first pipelines deliver blockbuster therapies faster or will regulatory and data-quality challenges slow the momentum? Let’s discuss. #AIinHealthcare #DrugDiscovery #PharmaInnovation #MachineLearning #FutureOfMedicine #DrGPT