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(accessed\nMay 29, 2024)."},{"key":"ref47\/cit47","unstructured":"DeepChem 2024 https:\/\/deepchem.io\/. (accessed\nMay 29, 2024)."},{"key":"ref48\/cit48","doi-asserted-by":"publisher","DOI":"10.1021\/ci034160g"},{"key":"ref49\/cit49","unstructured":"Moss, H. B.; Griffiths, R.R. Gaussian Process Molecular Property Prediction with FlowMO, arXiv:2010.01118v2. arXiv.org e-Print archive, 2020. https:\/\/arxiv.org\/abs\/2010.01118v2."},{"key":"ref50\/cit50","unstructured":"Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P. 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