Search Results - "Chuang, Kangway V"

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  1. 1

    Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline by Tang, Ziqi, Chuang, Kangway V., DeCarli, Charles, Jin, Lee-Way, Beckett, Laurel, Keiser, Michael J., Dugger, Brittany N.

    Published in Nature communications (15-05-2019)
    “…Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however,…”
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    Journal Article
  2. 2

    A 15-step synthesis of (+)-ryanodol by Chuang, Kangway V., Xu, Chen, Reisman, Sarah E.

    “…(+)-Ryanodine and (+)-ryanodol are complex diterpenoids that modulate intracellular calcium-ion release at ryanodine receptors, ion channels critical for…”
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  3. 3

    Copper-Catalyzed Diastereoselective Arylation of Tryptophan Derivatives: Total Synthesis of (+)-Naseseazines A and B by Kieffer, Madeleine E, Chuang, Kangway V, Reisman, Sarah E

    Published in Journal of the American Chemical Society (17-04-2013)
    “…A copper-catalyzed arylation of tryptophan derivatives is reported. The reaction proceeds with high site- and diastereoselectivity to provide aryl…”
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  4. 4

    Short, Enantioselective Total Syntheses of (−)-8-Demethoxyrunanine and (−)-Cepharatines A, C, and D by Chuang, Kangway V., Navarro, Raul, Reisman, Sarah E.

    Published in Angewandte Chemie International Edition (26-09-2011)
    “…All together! A unified synthetic strategy has resulted in the first enantioselective total syntheses of the natural products 8‐demethoxyrunanine and…”
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    Journal Article
  5. 5

    CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning by Grambow, Colin A., Weir, Hayley, Cunningham, Christian N., Biancalani, Tommaso, Chuang, Kangway V.

    Published in Scientific data (09-08-2024)
    “…Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and…”
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  6. 6

    Learning Molecular Representations for Medicinal Chemistry by Chuang, Kangway V, Gunsalus, Laura M, Keiser, Michael J

    Published in Journal of medicinal chemistry (27-08-2020)
    “…The accurate modeling and prediction of small molecule properties and bioactivities depend on the critical choice of molecular representation. Decades of…”
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  7. 7

    Comment on "Predicting reaction performance in C-N cross-coupling using machine learning" by Chuang, Kangway V, Keiser, Michael J

    “…Ahneman (Reports, 13 April 2018) applied machine learning models to predict C-N cross-coupling reaction yields. The models use atomic, electronic, and…”
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  8. 8

    Learning Molecular Representations for Medicinal Chemistry: Miniperspective by Chuang, Kangway V, Gunsalus, Laura M, Keiser, Michael J

    Published in Journal of medicinal chemistry (27-08-2020)
    “…The accurate modeling and prediction of small molecule properties and bioactivities depend on the critical choice of molecular representation. Decades of…”
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  9. 9

    Adversarial Controls for Scientific Machine Learning by Chuang, Kangway V, Keiser, Michael J

    Published in ACS chemical biology (19-10-2018)
    “…New machine learning methods to analyze raw chemical and biological data are now widely accessible as open-source toolkits. This positions researchers to…”
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  10. 10

    Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks by Gale-Day, Zachary J., Shub, Laura, Chuang, Kangway V., Keiser, Michael J.

    “…Message passing neural networks (MPNNs) on molecular graphs generate continuous and differentiable encodings of small molecules with state-of-the-art…”
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  11. 11

    A Mild and General Larock Indolization Protocol for the Preparation of Unnatural Tryptophans by Chuang, Kangway V, Kieffer, Madeleine E, Reisman, Sarah E

    Published in Organic letters (16-09-2016)
    “…A mild and general protocol for the Pd(0)-catalyzed heteroannulation of o-bromoanilines and alkynes is described. Application of a Pd(0)/P­( t Bu)3 catalyst…”
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  12. 12
  13. 13

    CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning by Grambow, Colin A, Weir, Hayley, Cunningham, Christian N, Biancalani, Tommaso, Chuang, Kangway V

    Published 09-08-2024
    “…Sci. Data 11, 859 (2024) Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to…”
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  14. 14

    Attention-Based Learning on Molecular Ensembles by Chuang, Kangway V, Keiser, Michael J

    Published 25-11-2020
    “…The three-dimensional shape and conformation of small-molecule ligands are critical for biomolecular recognition, yet encoding 3D geometry has not improved…”
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  15. 15

    Short, Enantioselective Total Syntheses of (−)-8-Demethoxyrunanine and (−)-Cepharatines A, C, and D by Chuang, Kangway V., Navarro, Raul, Reisman, Sarah E.

    Published in Angewandte Chemie (26-09-2011)
    “…Alle gemeinsam! Eine vereinheitlichte Synthesestrategie ermöglichte die erste enantioselektive Totalsynthese der Naturstoffe 8‐Demethoxyrunanin, Cepharatin A,…”
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  16. 16

    Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusion by Grambow, Colin A, Weir, Hayley, Diamant, Nathaniel L, Scalia, Gabriele, Biancalani, Tommaso, Chuang, Kangway V

    Published 30-05-2023
    “…Macrocyclic peptides are an emerging therapeutic modality, yet computational approaches for accurately sampling their diverse 3D ensembles remain challenging…”
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  17. 17

    Improving Graph Generation by Restricting Graph Bandwidth by Diamant, Nathaniel, Tseng, Alex M, Chuang, Kangway V, Biancalani, Tommaso, Scalia, Gabriele

    Published 25-01-2023
    “…Deep graph generative modeling has proven capable of learning the distribution of complex, multi-scale structures characterizing real-world graphs. However,…”
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  18. 18

    A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning by Atsango, Austin, Diamant, Nathaniel L, Lu, Ziqing, Biancalani, Tommaso, Scalia, Gabriele, Chuang, Kangway V

    Published 03-11-2022
    “…Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property…”
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