Scientific information solutions provider CAS, a division of the American Chemical Society, and WorldQuant Predictive (WQP), an AI predictive products company, have partnered to develop an innovative methodology that can save time and lives by helping research teams rapidly prioritize the most promising drug candidates to treat COVID-19 and other critical diseases. The scientific paper detailing this methodology and collaboration was recently published in the peer-reviewed journal, ACS Omega.
Overlaying WQP’s novel AI technology platform, Quanto™, with the exhaustive repository of data from the CAS REGISTRY®, the groundbreaking initiative was led by WQP scientific advisor and former Pfizer Senior Vice President in research and development, Dr. Kelvin Cooper. The goal of this project was to create a virtual screening tool model that can be replicated by outside researchers and biopharmaceutical companies to quickly identify compounds to treat COVID-19 and other diseases. Using a well-studied medicinal chemistry tool known as quantitative structure activity relationships, or QSAR, the team evolved the model from human-led input.
This project applied AI tools such as feature engineering, embedding, and other novel modeling techniques in crucial areas. The result is smarter detection of patterns that might otherwise not be seen by researchers, enabling them to distinguish compounds based on a target fingerprint.
The findings, along with a detailed description of the methodology, key data sets, and identified candidates are available to the research community on the WorldQuant Predictive website.
Click here to read the original press release.
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