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Robotic labs guided by AI that design experiments on tumour models, run them, read the results and design the next ones, around the clock, with every result published openly.
Autonomous laboratories exist in chemistry and materials science, and cloud labs and robotic organoid culture are emerging in biology. Cancer biology is limited by slow, poorly reproducible manual experimentation. The proposal is a network of self-driving cancer labs: automated organoid and cell line culture, perturbation, imaging and sequencing readouts, active-learning experiment selection against defined questions (resistance mechanisms, combination synergy, dependency mapping), and automatic public deposition of raw data and protocols.
Shares Recursion Pharmaceuticals, Estimation of clinical trial success rates and related parameters, CRISPR functional genomics, AI-driven drug & target discovery.
Shares Estimation of clinical trial success rates and related parameters, Preclinical results do not reproduce, AI-driven drug & target discovery, Patient-derived organoids.
Shares Estimation of clinical trial success rates and related parameters, Preclinical results do not reproduce, Lab models that fail to predict what happens in patients, The valley of death between lab and product.
Shares Recursion Pharmaceuticals, CRISPR functional genomics, AI-driven drug & target discovery.
Shares Estimation of clinical trial success rates and related parameters, Preclinical results do not reproduce, Lab models that fail to predict what happens in patients.
Shares Preclinical results do not reproduce, Patient-derived organoids, The valley of death between lab and product.
Shares Estimation of clinical trial success rates and related parameters, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares Estimation of clinical trial success rates and related parameters, CRISPR functional genomics, Lab models that fail to predict what happens in patients.