@article {CaltechAUTHORS_https://authors.library.caltech.edu/id/eprint/107875,
    title ="Total Synthesis of Ritterazine B",
    author = "Nakayama, Yasuaki and Maser, Michael R.",
    
    
    
    
    month = "February",
    year = "2021",
    
    
    
    url = "https://resolver.caltech.edu/CaltechAUTHORS:20210202-123615131",
    note = "LICENCE: CC BY-NC-ND 4.0. \n\n31.01.2021 - Submission date; 01.02.2021 - First online date, Posted date. \n\nDr. Scott Virgil and the Caltech Center for Catalysis and Chemical Synthesis are gratefully acknowledged for access to analytical equipment. We thank Arthur Han for early studies. Fellowship support was provided by the NSF (M. R. M., Grant No. DGE-1144469), the Japan Society for the Promotion of Science (Y. N.), and Ishihara Sangyo Kaisha (T. O.). S.E.R. is a Heritage Medical Research Institute Investigator, and acknowledges partial financial support from the NIH (R35GM118191) and the American Cancer Society.",
    revision_no = "9",
    abstract = "The first total synthesis of the cytotoxic alkaloid ritterazine B is reported. The synthesis features a unified approach to both steroid subunits, employing a titanium-mediated propargylation reaction to achieve divergence from a common precursor. Other key steps include gold-catalyzed cycloisomerizations that install both spiroketals, and late stage C–H oxidation to incorporate the C7′ alcohol.",
}
@article {CaltechAUTHORS_https://authors.library.caltech.edu/id/eprint/107462,
    title ="Nickel-Catalyzed Asymmetric Reductive Cross-Coupling of α-Chloroesters with (Hetero)Aryl Iodides",
    author = "DeLano, Travis J. and Dibrell, Sara E.",
    
    
    
    
    month = "January",
    year = "2021",
    
    
    
    url = "https://resolver.caltech.edu/CaltechAUTHORS:20210113-152642442",
    note = "License: CC BY-NC-ND 4.0. \n\nHistory: 03.01.2021 - Submission date; 04.01.2021 - First online date, Posted date. \n\nDr. Scott Virgil and the Caltech Center for Catalysis and Chemical Synthesis are gratefully acknowledged for access to analytical equipment. We thank Yoshihiro Ogura for early studies and Raymond Turro for the preparation of L1. M. S. S. thanks the NIH (R35 GM136271) for support. Fellowship support was provided by the NSF (T. J. D., S. E. D., C. R. L., K. E. P., Grant No. DGE-1144469). S.E.R. is a Heritage Medical Research Institute Investigator, and acknowledges financial support from the NIH (R35 GM118191).\n\nDeclaration of Conflict of Interest: No conflict of interest.",
    revision_no = "15",
    abstract = "An asymmetric reductive cross-coupling of alpha-chloroesters and (hetero)aryl iodides is reported. This nickel-catalyzed reaction proceeds with a chiral BiOX ligand under mild conditions, affording alpha-arylesters in good yields and enantioselectivities. The reaction is tolerant of a variety of functional groups, and the resulting products can be converted to pharmaceutically-relevant chiral building blocks. A multivariate linear regression model was developed to quantitatively relate the influence of the alpha-chloroester substrate and ligand on enantioselectivity.",
}
@article {CaltechAUTHORS_https://authors.library.caltech.edu/id/eprint/106598,
    title ="Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions",
    author = "Ryou, Serim and Maser, Michael R.",
    journal = "arXiv",
    
    
    
    month = "July",
    year = "2020",
    
    
    
    url = "https://resolver.caltech.edu/CaltechAUTHORS:20201110-154207213",
    note = "© 2020 by the author(s). \n\nTo appear in the ICML 2020 Workshop on Graph Representation\nLearning and Beyond (GRLB). \n\nWe thank the reviewers for their insightful comments and Prof Pietro Perona for mentorship guidance and helpful discussions on this work. Fellowship support was provided by the NSF (M.R.M., T.J.D. Grant No. DGE-1144469). S.E.R. is a Heritage Medical Research Investigator. Financial support from the Research Corporation Cottrell Scholars Program is acknowledged.",
    revision_no = "8",
    abstract = "We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.",
}