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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.
Anthropic released two versions of Claude 5.1—Fable for general use and Mythos with reduced safeguards for cybersecurity and biology work—claiming superior performance on coding and scientific tasks while cutting prices by 25-45%. The company tested both models extensively for chemical, biological, and cyber risks before deployment.
- Claude Mythos 5.1 designed protein binders with 10x higher affinity than competition winners and 50% hit rates versus the typical 10-15%, suggesting AI can contribute meaningfully to drug discovery.
- Fable 5.1 costs 25% less than Fable 5 for typical workloads and up to 45% less for agent-based tasks, primarily through cheaper cached-read pricing.
- Mythos 5.1 optimized deep learning models by up to 2.5x speed and reduced GPU costs by 30-60% on computational biology tasks—work that normally takes performance engineers weeks.
OpenAI released Rosalind Workbench, a unified environment that consolidates fragmented scientific tools and data into one workspace for life science researchers. It's built on GPT-Rosalind, a specialized model for biology, and available through ChatGPT with guided workflows for tasks like protein design, genomics analysis, and molecular docking.
- Solves a real workflow problem: researchers currently juggle data in one system, analysis in another, and experimental records elsewhere, losing context between steps
- Includes specialized viewers and guided tasks (protein design, small-molecule design, genomics, experimental validation) that keep biological questions connected across different tools
- Offers two access modes—Explore mode for general questions and Research mode for complex analysis, with verified organizations able to request access now and individual access coming later
OpenMed developed a pipeline that transforms protein concepts into codon-optimized DNA sequences. They compared various transformer architectures for codon-level language modeling, finding CodonRoBERTa-large-v2 to outperform others in biological relevance. The project includes detailed results and runnable code for each stage.
- CodonRoBERTa-large-v2 hit perplexity 4.10 and Spearman CAI correlation 0.40 across 381,000 sequences from 25 species, beating other tested architectures.
- ModernBERT badly underperformed classic RoBERTa on codon modeling (perplexity 26.24 vs 4.01), showing that advanced NLP architectures and pretrained weights don't transfer well to biological sequence data.
- The entire training pipeline ran on just 4 A100 GPUs for a total cost of $165.