Australian tech entrepreneur Paul Conyngham explains how he used ChatGPT/AlphaFold (spent $3,000 with no biology background) to create a custom MRNA vaccine to treat his dog’s cancer tumors. Unreal.

Still incredible that the DeepMind documentary has footage of exact moment Demis is told that AlphaFold can “easily” predict all known (1-2B) protein sequences “in a month” and he says to do it. Then, it shows the moment AlphaFold is released to the world.

I’ve always believed the No.1 application of AI should be to improve human health. That work started with AlphaFold, and now at @IsomorphicLabs with the mission to reimagine drug discovery and one day solve all disease! We are turbocharging that goal with $2.1B in new funding.

AI'S COOLEST USE ISN'T CHATBOTS. IT'S INVENTING NEW MATTER. -AI is brute-forcing new material combinations humans never tried: spacecraft shielding, heat-dissipating materials for space -DeepMind's AlphaFold 3 folds proteins to find compounds that could cure diseases -@TickerSymbolYOU says work that would take humans forever, done in a design space we've barely touched

If Hantavirus mutated into a global threat, it would unleash AI + biotech unlike anything we've ever seen. > genome sequenced and public in 4 hours > AlphaFold maps every protein target > AI screens 10,000 drugs in 24 hrs > 50 vaccine candidates designed simultaneously > AI designed antibodies in days > risk of death computed instantly > decentralized trials launch globally > enroll from home > 20 countries manufacturing at once > first doses in three weeks > real-time dose characterization > your genome + biomarkers determine your protocol > variant map updates every hour No one would wait for governments.

This is great news. As you age, sugar binding to your proteins creates stiff, sticky chemical scars that affect skin, arteries, eyes and more. It was considered irreversible and now may be reversible, restoring to a healthy state. Researches did this by using AlphaFold to search 45,000 oxidases, then screened more than 500 million engineered variants through directed evolution. The work is still ex vivo in a lab setting. Delivering a large bacterial enzyme safely into living tissues, with sufficient penetration and bioavailability, remains a major challenge.

I had a chance to sit down with Demis Hassabis to discuss his goal of solving intelligence and using it to help cure all disease. But what I found most compelling wasn't the scale of the mission, but @demishassabis himself. His level of focus is extraordinary. We talked about the personal cost of pursuing a mission this ambitious, and the tension between the two sides of his work: the CEO of a commercial entity and the scientist driven by a humanitarian vision. It was an absolutely fascinating conversation, and a deep honor to speak with one of the most important minds of our time. Thank you to the @GoogleDeepMind team for having me. (0:00) Curing Every Disease in a Decade (1:04) Gemini for Science (2:01) Changing Science Forever with AlphaFold (4:04) Can Humans Trust AI In The Medical Field? (6:02) Generative AI vs. Practical AI (7:03) The Tension Between CEO & Scientist (8:28) AGI by 2030 (10:07) The Chess Prodigy Turned CEO (11:20) Ender's Game & What This Mission Has Cost

🔬 "It feels like having a team of 50 people doing all the work in a day." A Gemini-based multi-agent system generates, debates, and evolves scientific hypotheses, even surfacing a drug candidate that blocks 91% of scarring responses in liver fibrosis. Title: Co-Scientist: A multi-agent AI partner to accelerate research URL: 📝 Overview Co-Scientist is a collaborative multi-agent AI built on Gemini that generates, critiques, and refines novel scientific hypotheses. By automating the hypothesis generation and evaluation cycle, it acts as an AI research partner that accelerates breakthrough discovery. ❓ Challenges Solved Amid information overload and increasingly complex problems, researchers struggle to form breakthrough hypotheses. Connecting scattered facts across vast literature to identify promising research directions is hard. 💡 Methodology & Proposed Approach It organizes specialized agents into three phases. ・Generation phase: a Generation agent proposes novel hypotheses grounded in literature and data, and a Proximity agent clusters them to ensure diverse exploration ・Debate phase: a Reflection agent critiques as a virtual peer reviewer, and a Ranking agent prioritizes via pairwise comparison and Elo-based tournaments ・Evolution phase: an Evolution agent continuously refines and combines top hypotheses, and a Meta-review agent synthesizes final research proposals ・Most of the compute goes to verification, cross-checking claims against ChEMBL, UniProt, web search, and specialized tools like AlphaFold 🎯 Use Cases It is applied across life sciences: antimicrobial resistance, plant immunity, liver fibrosis treatment discovery, ALS mechanisms, cellular aging reversal, infectious-disease protein identification, metabolic disease, and aging biology. 📊 Results ・In liver fibrosis, it identified a drug candidate that blocks 91% of scarring-linked responses ・In cellular aging, it generated genetic leads that rejuvenated cells in the lab and cut screening analysis from months to days ・Over 100 institutions tested it, with collaborators including Stanford, MIT, Cambridge, and Calico ・Enterprise versions are deployed at organizations like Daiichi Sankyo, Bayer Crop Science, and U.S. National Labs #AIforScience# #AIAgents#