Application of AI for Advanced R&D
Generate Novel Drug Candidates
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Analyze data sets, form hypotheses and generate novel insights
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Identify novel drug candidates
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Analyze data from patient samples in both healthy and diseased states to generate novel biomarkers and therapeutic targets
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Predict binding affinity and other pharmacological properties of molecules
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Allow filtering for drug-like properties of molecules
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Reduce complexity in protein design
Clinical Trials
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Optimize clinical trial study design
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Transform diverse streams of biomedical and healthcare data into computer models representative of individual patients
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Deliver personalized medicine at scale by revealing optimal health interventions for individual patients
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Analyze medical records to find patients for clinical trials
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Automate matching cancer patients to clinical trials through personal medical history and genetic analysis
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Improve pathology analysis
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Identify patients that would benefit from novel therapies
Design and Processing of Preclinical Experiments
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Reduce time, money, and uncertainty in planning experiments
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Decode open- and closed-access data on reagents and get actionable insights
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Automate selection, manipulation, and analysis of cells
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Expedite development of cell lines and automate manufacturing of cellular therapeutics
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Automate sample analysis with a robotic cloud laboratory
Repurposing of Existing Drugs
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Rapidly identify new indications for many known drugs
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Match existing drugs with rare diseases
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Conduct experimental biology at scale by testing 1000+ of compounds on 100+ of cellular disease models in parallel
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Generate novel biomarkers and therapeutic targets
Aggregation and Synthesis of Information
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Extract knowledge from literature
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Generate insights from thousands of unrelated data sources
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Improve decision-making
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Eliminate blind spots in research
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Identify competitive whitespace