Exploring Spectrum‐based Molecular Descriptors for Reaction Performance Prediction

Despite the availability and accuracy of modern spectroscopic characterization, the utilization of spectral information in chemical machine learning is still primitive. Here, we report an optical character recognition‐based automatic process to utilize spectral information as molecular descriptors,...

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Bibliographic Details
Published in:Chemistry, an Asian journal an Asian journal, 2023-04, Vol.18 (7), p.e202300011-n/a
Main Authors: Tang, Miao‐Jiong, Xu, Li‐Cheng, Zhang, Shuo‐Qing, Hong, Xin
Format: Article
Language:English
Subjects:
Chemistry
Cross coupling
Halides
Machine learning
molecular descriptor
Optical character recognition
Performance prediction
quantitative structure-activity relationship
spectrum
yield prediction
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Summary:Despite the availability and accuracy of modern spectroscopic characterization, the utilization of spectral information in chemical machine learning is still primitive. Here, we report an optical character recognition‐based automatic process to utilize spectral information as molecular descriptors, which directly transforms experimental spectrum images to readable vectors. We demonstrate its machine learning application in the reaction yield dataset of Pd‐catalyzed Buchwald‐Hartwig cross‐coupling with aryl halides. In addition, we also show that the predicted spectrum can serve as an alternative encoding source to support the model training. Spectroscopy, as one of the most widely applied characterization techniques, contains a wealth of chemical information, yet its application in chemical machine learning is still limited. This work reports the OCR processing of spectrum images to machine learning‐readable descriptors, and these descriptors were found effective in the modeling of yield prediction.
ISSN:1861-4728
1861-471X
DOI:10.1002/asia.202300011