CAS, a division of the American Chemical Society that specialises in scientific information solutions, has collaborated with the Massachusetts Institute of Technology (MIT) on research to enhance predictive chemical synthesis planning. Through the agreement, CAS will provide MIT’s Department of Chemical Engineering Connor Coley’s Research Group a collection of high-quality, scientist-curated chemical reaction data, enabling them to train advanced algorithms in retrosynthesis and reaction prediction.
Efficient synthesis planning is critical in bringing new drugs and chemical products to market. Scientists are increasingly turning to predictive retrosynthesis to identify cost-effective, novel pathways to create molecules not previously known in literature and more efficient pathways to synthesise known molecules. Progress on the application of machine learning to this complex challenge has been slow as models that generate accurate synthetic predictions depend on the complex interplay of sophisticated algorithms, high-quality input and training data, and scientific expertise.
MIT has been pioneering research into the application of machine learning to synthesis planning. The MIT research team will leverage highly structured chemical reaction content from CAS to increase the volume, quality and diversity of data used to train advanced algorithms.
The CAS reaction collection is the most complete compilation of reaction information in the world. Updated daily, it covers over 130 million single- and multi-step reactions sourced from patents, journals and other reference works and includes reaction schemes, step-by-step experimental procedures, detailed reaction conditions, and product yields curated by expert scientists.
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