On the use of real-world datasets for reaction yield prediction.

Autor: Saebi M; Department of Computer Science and Engineering and Lucy Family Institute for Data and Society, University of Notre Dame Notre Dame IN 46556 USA nchawla@nd.edu., Nan B; Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA owiest@nd.edu., Herr JE; Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA owiest@nd.edu., Wahlers J; Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA owiest@nd.edu., Guo Z; Department of Computer Science and Engineering and Lucy Family Institute for Data and Society, University of Notre Dame Notre Dame IN 46556 USA nchawla@nd.edu., Zurański AM; Department of Chemistry, Princeton University Princeton New Jersey 08544 USA., Kogej T; Molecular AI, Discovery Sciences, R&D, AstraZeneca Pepparedsleden 1, SE-431 83 Mölndal Gothenburg Sweden., Norrby PO; Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Pepparedsleden 1, SE-431 83 Mölndal Gothenburg Sweden., Doyle AG; Department of Chemistry, Princeton University Princeton New Jersey 08544 USA.; Department of Chemistry and Biochemistry, University of California Los Angeles California 90095 USA., Chawla NV; Department of Computer Science and Engineering and Lucy Family Institute for Data and Society, University of Notre Dame Notre Dame IN 46556 USA nchawla@nd.edu., Wiest O; Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA owiest@nd.edu.

Publikováno v: Chemical science [Chem Sci] 2023 Mar 13; Vol. 14 (19), pp. 4997-5005. Date of Electronic Publication: 2023 Mar 13 (Print Publication: 2023).