Anthropic is hiring life science researchers. This is very important: applying AI to protein designs, genomics, and advanced cellular simulation... The once thought-to-be 'untreatable' diseases are poised for a reckoning of breakthroughs.
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@PeterDiamandis Simulation is the key. If we can run a million clinical trials in silicon before we touch a patient we collapse the cost and time of approval by orders of magnitude.
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@PeterDiamandis Feels like we’re about to watch science go from impossible to hold my protein shake.
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@PeterDiamandis Finally! The sooner we find a way to reverse aging the better! I've already seen breakthroughs in this field. Something about the mitochondria.
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@PeterDiamandis Peter -this is the exact breakthrough the life-science team at Anthropic will want. We just ported real T-cell receptor CDR3 length priors + clonal expansion directly into surface-code decoding. Result: 45–65% logical overhead reduction on distance-7 surface code, validated on
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@PeterDiamandis "Have 8+ years of machine learning experience, with demonstrated ability to train and evaluate large language models Have 5+ years of hands-on experience in life sciences R&D, with deep expertise in areas such as molecular biology, drug discovery, or computational biology" lol.
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@PeterDiamandis The future won’t just need scientists - it will need those who can rewrite the code of life. This is only the beginning.
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@PeterDiamandis If you're willing to train me, sure, why not. One-off-one pattern recognition; I just lack the knowledge, but that's the easy part!
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@PeterDiamandis I’m open-sourcing STUR because medicine doesn’t need another walled-garden model, it needs a shared physics engine. Protein design, genomics, cell simulations—those all sit on top of the same resistance-geometry that governs energy flow, coherence, and failure modes inside living
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@PeterDiamandis Medicine moving from analog to digital. AI allows for iterations not possible any other way. Better hits. Higher probability of success.
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@PeterDiamandis Speed to market and affordable therapeutics is the key. Finding new targets and keeping the same system makes no sense. Do both
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@PeterDiamandis Protein design is basically an algorithm. The LLM could look at randomly generated proteins and say, "it has green balls here, and red balls there, so it likely can do X" with some probability of accuracy greater than chance.
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@PeterDiamandis A critical step after this, is human #ClinicalTrials. Specifically to #cancer is where @CureMatch can assist on the patient stratification to accelerate path to clearance. The only input needed is the #NGS lab work. Recent @asco publication alongside @SyneosHealth @TransThera
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