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.

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.

Three MIT PhD students reverse-engineered Google's AlphaFold 3, creating Boltz-1 as an open-source alternative for drug discovery. Their platform enables pharmaceutical companies to conduct rapid and cost-effective drug-binding predictions while maintaining free access to the underlying models. Boltz aims to challenge commercial restrictions and offer a more accessible solution within the competitive landscape of AI in drug discovery.

• Three MIT PhD students reverse-engineered AlphaFold 3's methodology and released it as open-source Boltz-1, bypassing Google's restrictive licensing.
• Pharmaceutical companies can now run drug-binding predictions rapidly and cheaply without paying for or being restricted by Google's commercial terms.
• The project directly challenges the trend of AI drug-discovery tools being locked behind corporate control, pushing the field back toward open access.