Chris Picardo’s Post

[Isomorphic Labs: Redefining the AI Drug Discovery Paradigm] 🚀 Google DeepMind's Game-Changing Spinoff is Revolutionizing Drug Development with Nobel Prize-Winning AI The numbers speak volumes: - $3B in partnerships with Eli Lilly and Company Lilly & Novartis in just 3 years - $600M Series A led by Thrive Capital (March 2025) - 2025: First human trials for AI-designed drugs 🧬 What Makes Isomorphic Labs Revolutionary? Traditional Drug Discovery: Screen millions of compounds hoping to find one that works ⏰ 10-15 years, $2B+ cost Isomorphic's Approach: Design molecules from scratch at the atomic level using AlphaFold ⚡ 4 years from concept to clinic 🎯 The AlphaFold Advantage While competitors analyze patterns in existing data, Isomorphic Labs creates solutions: ✅ Atomic-level precision - Designs drugs molecule by molecule ✅ Nobel Prize technology - Built on breakthrough protein folding AI ✅ Alphabet backing - Unlimited access to Google's computational power ✅ Proven partnerships - Big Pharma betting billions on the platform 📊 How They Stack Against Competition 🥇 Isomorphic Labs: Atomic-level drug design → Clinical trials 2025 🥈 Recursion: Cell imaging + AI → 11 years, still no approved drugs 🥉 Schrödinger: Traditional computational chemistry → Slower innovation 🔸 Atomwise: Virtual screening → Limited experimental validation 💡 Why This Matters for the Industry Before: "Let's test millions of compounds and hope something works" After: "Let's design exactly what we need at the molecular level" This isn't just faster drug discovery—it's intelligent drug design. 🔮 The Bottom Line While others are still "searching for needles in haystacks," Isomorphic Labs is manufacturing the needles to exact specifications. For investors: Early-stage access to what could be the AWS of drug discovery For pharma: Partnership opportunity with the future of R&D For patients: Faster, more precise treatments designed for their specific conditions 2025 will be the year we see if AI can truly reinvent drug discovery. Isomorphic Labs is leading that charge. What's your take on AI replacing traditional drug discovery methods? #AI #DrugDiscovery #ArtificialIntelligence #Biotech #AlphaFold #DeepMind #Pharmaceuticals #Google

🇮🇪 Dublin-based Meta-flux, a biotech startup using AI to decode complex biological systems, has raised €1.8 million in Seed funding to accelerate its expansion across Europe and the US. The round includes backing from senior executives at Pfizer, Merck, Gilead Sciences, and technology leaders from Google, Amazon, and Indeed. Founded by Lee Sherlock and Brendan Martin, Meta-Flux has developed an “AI biologist” platform that helps scientists understand how genes, proteins, and metabolic pathways interact, turning massive datasets into actionable insights for preclinical drug development. By enabling faster go/no-go decisions, Meta-Flux reduces failed trials and helps researchers identify which diseases, subtypes, and patient groups are most likely to benefit from new treatments. With the new funding, the company plans to expand its scientific and engineering team, strengthen pharma collaborations in the EU and US, and further develop its decision-support platform.

Tahoe Therapeutics is revolutionizing biotech with AI models that simulate living cells, unlocking new possibilities in drug discovery and disease treatment. With $30 million in funding, this innovation promises to accelerate research and bring life-saving therapies to patients faster. The intersection of AI and biology is redefining what’s possible in medicine. The future of healthcare starts here. #Biotech #LifeSciences  

Bio Protocol raises $6.9M to scale AI-native DeSci biotech platform Bio Protocol VitaDAO Aubrey de Grey First BioAgent generated 1,000+ hypotheses, raised $900K, and drove lab testing within weeks #Investment #longevity #AI #MedicalResearch

𝗔𝗜’𝘀 𝗦𝗽𝗲𝗲𝗱 𝗜𝘀 𝗜𝘁𝘀 𝗦𝘂𝗽𝗲𝗿𝗽𝗼𝘄𝗲𝗿 𝗶𝗻 𝗕𝗶𝗼𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 👉 The deep-learning model DrugReflector, recently published in Science, is a great example of how AI can accelerate drug discovery. DrugReflector doesn’t invent new molecules, propose novel therapeutic targets, or model protein–protein interactions. What it does: leverage publicly available transcriptomic data to predict which known compounds might trigger specific biological effects — like boosting platelet or red blood cell production. 👉 The result? Up to 17× more efficient than random brute-force screening, with significant gains in speed and cost-effectiveness. 👉DrugReflector highlights the efficiency gains achieved when AI speed meets human insight. 👉 This aligns with a broader observation I explored in a recent post about Agents4Science, a bold experiment where AI agents led the entire research process. The meeting’s conclusions were clear: AI excels at speed and pattern recognition, but still needs human-level reasoning and creativity. #pharma #drugdiscovery #biotech #healthtech #AI #machinelearning #bioinformatics #transcriptomics #innovation #ML https://lnkd.in/eaT7A736

💊 AI Is Redefining How Medicines Are Discovered 💡 🤖 From predicting protein structures to designing new compounds, AI is transforming the way pharma companies bring drugs to market. 🔹 DeepMind’s AlphaFold maps protein structures with unmatched accuracy. 🔹 Insilico Medicine creates novel drug compounds using AI. 🔹 BenevolentAI repurposes existing drugs for faster therapies. 📊 With the AI drug discovery market set to surpass $10B by 2030, R&D timelines are being cut from years to months. 💡 At Nimblechapps, we help accelerate pharmaceutical innovation with AI-powered data dashboards and bioinformatics platforms. #Nimblechapps #MobileAppDevelopment #AI #WebsiteDevelopment #DrugDiscovery #ArtificialIntelligence #DeepMind #Insilico #BenevolentAI #HealthcareInnovation #PharmaTech #AIBioinformatics #LifeSciences #MedTech #HealthTech #FutureOfHealthcare #Innovation

#AINews 🌍 Revolutionizing Drug Discovery: KAIST's AI Predictive Technology KAIST scientists have introduced a groundbreaking AI tool that forecasts cellular drug responses, revolutionizing the way we approach drug discovery. This predictive model can anticipate the outcomes of untested cell-drug combinations and cellular reactions to genetic changes. 📊 Key Implications for Your Team: • Predicts novel cell-drug interactions • Models complex cell-drug interactions using a generative approach • Anticipates responses to genetic changes • Accelerates development of targeted therapies 🔍 Strategic Considerations: This technology has the potential to streamline pharmaceutical R&D processes, leading to more efficient and targeted therapy development. 💡 My Take: KAIST's approach of using AI as a modular tool to model cellular dynamics is a big leap forward in precision medicine. This could drive significant changes in how enterprises approach therapeutic innovation. 🤔 How might this AI tool change your approach to drug development strategies? #Innovation #DrugDiscovery #AI #HealthcareTech #SmarterWithAI