Elsevier platform makes data ‘AI-ready’ to help researchers overcome R&D challenges

By Melissa Fassbender

- Last updated on GMT

(Image: Getty/monsitj)
(Image: Getty/monsitj)

Related tags Artificial intelligence AI Elsevier machine learning

The information analytics business Elsevier has launched a new cloud-based data platform, Entellect, to de-silo, contextualize, and connect drug, target, and disease data.

According to the company, the platform is designed to help life sciences companies overcome research and development (R&D) challenges by organizing data and presenting it in an artificial intelligence (AI) ready environment.

Tim Miller, vice president of life sciences platform solutions, Elsevier, said this process helps life science organizations overcome the challenges faced around data interoperability across “many disparate knowledge bases.”

Read: ‘The opportunities for AI to revolutionize the pharmaceutical industry are clear’: Report

“Entellect gives users the full power of data science within their R&D workflows and delivers AI-ready data, allowing scientists to focus on their work, rather than on manipulating data,”​ Miller told us.

The platform aims to unlock the potential of AI in life sciences – potential which Miller said is huge. “[AI] promises to accelerate R&D by automating the most time-consuming activities researchers undertake around processing data,” ​he said.

Nevertheless, this potential can only be unleashed if the obstacles to using data are removed.

The value of AI

While not all areas will be disrupted by the use of AI, there are few that will not benefit in some way.

For workflows that require scientists to process large data volumes to find an “answer,”​ Miller said the potential is “particularly promising.” ​This includes preclinical research in which researchers must sift through myriad sources to identify potential compounds for development.

“Given only one in every 5,000 compounds enters drug discovery, accelerating this process will be invaluable,”​ Miller said. “Similarly, drug safety and pharmacovigilance are data-heavy areas that stand to benefit from AI.”

Elsevier customer are currently using Entellect in this area to standardize and search thousands of different unstructured medical documents.

“We have access today to volumes of data that were unthinkable just a few years ago – but captured in different formats, ways, and for different purposes, so it’s a huge challenge to manage,”​ explained Miller. “Value from AI will come when these data can be intelligently combined to provide a holistic view.”

Miller added that Entellect will allow companies to outsource labor-intensive data preparation, freeing data scientists to focus interrogating and understanding the results.

How is the platform different from similar solutions? The generalist design of existing AI platforms has limited success to date, Miller said. Such platforms “seek to solve not only scientific problems, but also financial, automotive, and engineering problems.”

“However, because of the difficulties involved in processing the different types of data, one tool which provides a singular experience cannot meet the needs of multiple different researchers – specialization is essential,”​ he explained.

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