56 summarised stories about Drug Discovery, each linking back to the original source. Browse all topics →
Product Hunt 1 month ago 37 ● 6 sources
AlphaGenome Atlas launches today as a Google DeepMind AI-powered map of how human DNA mutations may affect biology. It includes precomputed predictions for 9 billion possible single-letter DNA changes in a 1-petabyte dataset. Researchers can now explore and prioritize variants through a free web interface, with API access for deeper work.
The New York Times 1 month ago 38 ● 3 sources
Rentosertib, an AI-assisted drug candidate, showed early evidence that it may slow aging beyond treating chronic lung disease. It reduced biological-age markers across six aging clocks. The results mean the drug’s potential is expanding to aging, though it is still years away from regulatory approval.
Nature Publishing Group US 1 month ago 8 ● 3 sources
Insilico’s AI-designed TNIK inhibitor rentosertib (INS018_055) was evaluated in a phase 2a IPF trial using serum proteomics analyzed by six proteomic aging clocks. At 12 weeks, 21 of 54 treatment-vs-placebo clock comparisons were statistically significant, with most occurring at week 4. All six clocks showed reduced predicted biological age in treated groups versus minimal change in placebo, shifting trial readouts toward measurable aging-rate deceleration alongside disease effects.
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15 October 2026
Meta’s big move today wasn’t just another model release—it was a licensing-and-distribution statement. Muse Spark Open ships as an 816B-parameter, coding-optimized multimodal model with a 1 million–token context window and 42B active parameters per token, now available as downloads for developers (with restrictions) and deployable through Amazon SageMaker JumpStart into customer-controlled endpoints. The “open” part comes with strings: a custom Muse Model License, advance notification above $20 million revenue, and a required “Built with Muse Spark Open” attribution. Meta still keeps its top systems closed, but it’s clearly betting that more developers will build long-running agent workflows when they can run, fine-tune, and adapt the weights.
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