DIGITAL HEALTHCARE
Bringing together physical experimentation, data structures and algorithms in this way creates a self-learning system that can accelerate innovation.
This becomes particularly powerful when biopharma organisations create closed-loop environments where physical laboratories, computational models and AI systems continuously learn from one another. Experimental results generated in a wet lab feed directly into AI models, which refine hypotheses and recommend the next set of experiments. AI has proven to excel in protein design where the model itself is making decisions on the unique string of amino acids for the optimal 3D structure of a specific protein.
For example, in our Cambridge Consultants labs, we were able to achieve the brightest Green Fluorescent Protein( GFP) expression variant with only 46 data points compared to an estimated 80,000 required through directed evolution. Those experiments then generate new data that further improves the models. This is particularly important because scientific research requires highly specialised models trained on domain-specific data.
Q. WHERE DO YOU SEE THE BIGGEST OPPORTUNITIES FOR AI TO IMPROVE CLINICAL TRIAL EFFICIENCY?
ยป Our research finds that 60 % of R & D leaders believe AI will substantially improve clinical trial efficiency, with
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