AI and Machine Learning Services

CAS Services - Advanced Content Solutions

Capture the promise of big data

AI can transform your R&D innovation. Powerful predictions start with quality data, tailored to the focus of your initiative. Commonly found datasets rarely generate novel outcomes. This is why access to quality data is the top roadblock to AI success*.  AI and ML algorithms must be optimized with large volumes of high-quality, structured and diverse datasets to generate more accurate predictions, improve transferability, and drive innovative decisions.

* State of Data Scientists Crowdflower, 2017

AI can transform your R&D innovation. Powerful predictions start with quality data, tailored to the focus of your initiative. Commonly found datasets rarely generate novel outcomes. This is why access to quality data is the top roadblock to AI success*.  AI and ML algorithms must be optimized with large volumes of high-quality, structured and diverse datasets to generate more accurate predictions, improve transferability, and drive innovative decisions.

* State of Data Scientists Crowdflower, 2017

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Partner with CAS to drive innovation

Are your AI initiatives meeting expectations?  High-quality, scientific data from CAS has shown to improve AI prediction accuracy by more than 30%. 

The CAS content collection comprehensively covers areas such as synthesis, substance properties, biosequences, and drug discovery. Our highly structured, big data environment includes proven data standards, architecture, schemas, and taxonomies that support highly flexible and transferable applications of specialized datasets. Power your model with content from the CAS collection or commission our scientists to curate a dataset configured to your unique specifications.  

Services include:

  • Content licensing for algorithm training and model validation 
  • Custom curation of datasets configured to unique model requirements
  • Data harmonization and structuring
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Impact of Data Quality on Machine Learning Results

See how enhancing the quality of the input data alone, without changing the algorithm, impacts prediction accuracy of an algorithm designed to assess the biological activity of compounds relative to different targets in this quantitative study by CAS data scientists. 

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