Anthropic had an internal Claude model design small proteins meant to latch onto scorpion neurotoxins, then had outside labs test them. Seventeen of 211 designs bound their targets, a possible early step toward cheaper antivenom, though none have been shown to neutralize venom yet.

• Claude generated about 318,000 candidate designs and picked 180 for testing, mostly running autonomously over 64 hours with four human follow-up prompts.
• Of 211 tested designs, 17 bound one of four dangerous scorpion neurotoxins. None of the designs aimed at several toxins at once worked for more than one.
• Anthropic says binding is no longer the bottleneck; the hard part is turning binders into safe, affordable drugs, and they haven't tested whether these neutralize venom in cells.

Daron Acemoglu argues that while AI's potential for drug discovery is often used to justify massive investments, the evidence shows AI has delivered little so far—and redirecting some of that money toward proven public health interventions would do far more to improve American life expectancy.

• AI has produced minimal gains in actual drug discovery despite significant effort. A recent paper found that while AlphaFold predicted protein structures, there's "no evidence so far that more applied, early-stage drug development is targeting these proteins."
• The US has the lowest life expectancy among wealthy nations (about 5 years behind Switzerland, Sweden, Japan), mostly from preventable deaths before age 70 due to chronic disease, overdoses, and poor public health—not lack of new drugs.
• The US spends far more on healthcare than peer countries but too little on public health (preventive care, nutrition, immunization, primary care access), suggesting reallocating AI investment toward these proven interventions would yield better health outcomes.

Insilico Medicine used artificial intelligence to develop rentosertib, a drug for chronic lung disease, and early trial data suggests it may also reduce biological markers of aging. The findings come with major caveats: the sample size was small, the drug hasn't been tested in healthy people, and the "aging clocks" used to measure results are scientifically debated.

• Rentosertib reduced biological aging markers across six different AI-based "aging clocks" in a clinical trial, according to results published in Nature Biotechnology.
• The drug was originally developed for idiopathic pulmonary fibrosis (IPF) and has only been tested in sick patients, not healthy ones, so anti-aging claims remain speculative.
• The study has significant limitations: small sample size, unproven reliability of aging clock measurements, and the drug is still years away from regulatory approval even for its primary lung disease indication.

Anthropic has kicked off an internal drug discovery effort focused on neglected diseases to sharpen its AI tools for biopharma clients. By running its own research alongside partners, the company aims to gather feedback and demonstrate Claude Science’s capabilities.

• Anthropic is running its own internal drug discovery program targeting neglected diseases to battle-test and improve Claude Science before selling it to pharma partners.
• As a public benefit company, Anthropic claims it can choose projects based on patient need rather than commercial potential, unlike typical biotechs.
• The company hasn't said what happens if it finds a promising drug candidate, leaving unclear how it would handle clinical trials.
• This follows a mixed track record for big tech in healthcare, including Alphabet's life sciences unit, Apple's health features, and Amazon's One Medical/PillPack acquisitions.

The article argues that AI will revolutionize drug discovery long before it can streamline clinical development, creating an abundance of candidate molecules but leaving patient trials as the main constraint. As discovery becomes commoditized and more assets target the same biology, real value will hinge on predictive toxicity, clinical efficacy, and strategic trial design.

• Drug candidate pipelines have doubled in the past decade but novel FDA approvals stayed flat at ~50/year, proving clinical development—not discovery—is the real bottleneck.
• Preclinical assets license for tens of millions, but value jumps to hundreds of millions or low-billions post-Phase 2 proof of concept—a premium set to shrink as AI floods the pipeline with candidates.
• Competition per target is already intense (100+ programs on targets like PD-1/GLP-1) and could double or triple by 2030, making individual molecules less rare and pushing investors to demand better translational data and trial design.
• AI excels at data-rich, fast-feedback problems (virtual screening, protein folding) but struggles with messy, high-variability clinical questions (endpoint selection, immune response prediction, adaptive trials)—so real value will shift to whoever masters those still-slow areas.

10x Science built a platform that uses chemistry-based algorithms and AI agents to interpret complex mass spectrometry data, speeding up protein characterization for drug development. Backed by a $4.8 million seed round, it helps biotechs and pharma quickly validate AI-generated treatment candidates. The startup plans to refine its models and expand offerings by integrating broader cellular data.

• 10x Science raised a $4.8M seed round led by Initialized Capital to build AI that interprets mass spectrometry data for drug candidate validation, addressing the bottleneck created by AI protein-folding tools flooding pipelines with candidates.
• Its platform is fully traceable rather than a black box, which matters for regulatory compliance—unlike prior tools that over-promised or failed on complex molecules.
• Early users like Rilas Technologies report it shaves weeks off workflows, with the AI accurately identifying proteins by name and pulling sequences from public databases.
• Long-term plan is to layer cellular data on top of protein structure to create "molecular intelligence," positioning it as a potential go-to analytics engine for AI-driven drug development.

This post lists nine key quotes from a San Francisco talk by Demis Hassabis and Sebastian Mallaby covering everything from OpenAI’s 50% bankruptcy risk to the need for new “AlphaFold” moments in drug discovery. They debate AGI probabilities, frontier cyber defense access, global AI optimism, and the economic and philosophical challenges of a post-scarcity future.

• Mallaby puts a 50% chance OpenAI goes bankrupt within 18 months
• Hassabis says six more AlphaFold-level breakthroughs are needed to cut drug-delivery timelines from 10 years to months
• Hassabis calls the idea that p(doom) is zero "crazy," acknowledging real existential risk from AI
• Hassabis argues economists, social scientists, and philosophers must join scientists in shaping decisions about how AI's value gets shared

OpenAI trained a new LLM, GPT-Rosalind, on 50 common biological workflows and major public databases to help researchers navigate massive genomic and protein datasets. The model links genotype to phenotype, suggests biological pathways, and prioritizes potential drug targets by leveraging mechanistic understanding.

• OpenAI's GPT-Rosalind is fine-tuned on the 50 most common biological workflows plus major public gene/protein/pathway databases
• It's designed to bridge jargon silos between biology subfields (e.g., helping a plant geneticist interpret neurobiology findings)
• Beyond summarizing data, it suggests biological pathways, links genotype to phenotype, and ranks drug targets to speed up hypothesis generation in drug discovery and synthetic biology

AWS introduced Amazon Bio Discovery, an AI-driven platform that lets researchers run complex drug-design workflows without coding. It provides a library of biological foundation models, an AI agent for workflow setup and analysis, and links to lab partners for synthesis and testing, cutting months of work down to weeks.

• AWS launched Amazon Bio Discovery, a no-code AI platform letting scientists run drug-design workflows using foundation models plus an AI agent, cutting months of research into weeks.
• In a Memorial Sloan Kettering/Twist Bioscience collaboration, nearly 300,000 AI-designed antibodies were narrowed to 100,000 for physical lab testing.
• Bayer, Broad Institute and Voyager Therapeutics are early adopters, and 19 of the top 20 global pharma companies already use AWS cloud services.
• AWS is separately partnering with Boston Consulting Group and Merck on an AI tool to improve clinical trial site selection.

Nvidia Corp. will invest $1 billion over five years to establish a new artificial intelligence laboratory in collaboration with Eli Lilly & Co. The facility, located in Silicon Valley, aims to accelerate the application of AI in the pharmaceutical industry by leveraging Lilly's lab expertise.

• Nvidia will invest $1 billion over five years to build an AI drug discovery lab with Eli Lilly, located in Silicon Valley
• The lab will use Lilly's lab expertise combined with Nvidia's computational tools to predict how compounds interact with biological systems
• The goal is to speed up and cut costs in drug discovery while improving precision in targeting specific diseases
• The deal signals Nvidia's strategic push into healthcare as a growth area beyond its core computing and gaming business

Boltz is launching a transformative approach to drug design and biological research by combining AI and open science, enabling over 100,000 scientists to innovate faster. With a newly raised $28 million seed round and a partnership with Pfizer, Boltz aims to break down barriers in drug development through open-source models and accessible computational tools.

• Boltz raised a $28 million seed round and partnered with Pfizer to advance open-source drug design models.
• Over 100,000 scientists are already using Boltz's tools, positioning it as a widely-adopted open-science alternative to closed AI drug discovery platforms.
• The core bet is that open-source, freely accessible AI models can accelerate biological research and drug development faster than proprietary approaches.