Sanofi is trying to shorten the diagnostic journey and accelerate drug discovery, development, and clinical research by finding useful signals across complex clinical, molecular, and real-world datasets.
Reported impact
Sanofi says the program helps accelerate R&D, shorten the diagnostic journey, improve drug-target identification and prioritization, discover digital biomarkers, anticipate flare-ups, and improve public health and the standard of care for diseases.
Sanofi describes an in-house AI program embedded across its R&D organization rather than a single packaged product deployment. The company says it is building scalable data platforms across therapeutic areas to support data use at every stage of drug discovery and development. Into those platforms, Sanofi brings together clinical and molecular data as well as real-world evidence from smart devices, wearables, electronic health records, genomics, proteomics, and real-time environmental measures. It then applies deep neural networks for target identification, active learning for drug and molecule design, graphical models to integrate clinical and molecular data for improved clinical trials, and AI plus bioinformatics to discover and validate digital biomarkers. Outputs are used by R&D teams and collaborators to prioritize targets, model disease, and anticipate patient flare-ups, with Open Targets explicitly cited as a collaboration vehicle for drug-target identification and prioritization.
Sanofi aggregates multimodal R&D and real-world evidence data from smart devices, wearables, electronic health records, genomics, proteomics, and real-time environmental measures into scalable internal data platforms. AI methods, bioinformatics, deep neural networks, active learning, and graphical models analyze those data to identify targets, design molecules, improve trials, discover biomarkers, and anticipate flare-ups; findings are then used by R&D teams and partners such as Open Targets.
"At every level of R&D, we’re building scalable data platforms to shorten the diagnostic journey and improve both public health and the standard of care for diseases."
"For example, to anticipate flare-ups in patients with some conditions, we can use AI and data from smart devices, wearables, electronic health records, genomics, proteomics, and real-time environmental measures."
"Through Open Targets, we work with academic and industry partners to improve drug-target identification and prioritization."
"To leverage data at all stages of drug discovery, we develop, extend, and apply AI methods including: deep neural networks and other technologies for target identification; active learning for drug and molecule design; graphical models for integrating clinical and molecular data to improve clinical trials."
"We use AI, bioinformatics, and other methods to find meaningful patterns in complex, multi-dimensional datasets."
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
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