| Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline Z Tang, KV Chuang, C DeCarli, LW Jin, L Beckett, MJ Keiser, BN Dugger Nature communications 10 (1), 2173, 2019 | 202 | 2019 |
| Copper-catalyzed diastereoselective arylation of tryptophan derivatives: Total synthesis of (+)-naseseazines A and B ME Kieffer, KV Chuang, SE Reisman Journal of the American Chemical Society 135 (15), 5557-5560, 2013 | 155 | 2013 |
| Comment on “Predicting reaction performance in C–N cross-coupling using machine learning” KV Chuang, MJ Keiser Science 362 (6416), 2018 | 154 | 2018 |
| Learning molecular representations for medicinal chemistry: miniperspective KV Chuang, LM Gunsalus, MJ Keiser Journal of Medicinal Chemistry 63 (16), 8705-8722, 2020 | 143 | 2020 |
| A 15-step synthesis of (+)-ryanodol KV Chuang, C Xu, SE Reisman Science 353 (6302), 912-915, 2016 | 111 | 2016 |
| A copper-catalyzed arylation of tryptamines for the direct synthesis of aryl pyrroloindolines ME Kieffer, KV Chuang, SE Reisman Chemical Science 3 (11), 3170-3174, 2012 | 95 | 2012 |
| Benzoquinone-derived sulfinyl imines as versatile intermediates for alkaloid synthesis: Total synthesis of (–)-3-demethoxyerythratidinone KV Chuang, R Navarro, SE Reisman Chemical Science 2 (6), 1086-1089, 2011 | 77 | 2011 |
| Short, Enantioselective Total Syntheses of (−)‐8‐Demethoxyrunanine and (−)‐Cepharatines A, C, and D KV Chuang, R Navarro, SE Reisman Angewandte Chemie 123 (40), 9619-9623, 2011 | 69 | 2011 |
| Adversarial controls for scientific machine learning KV Chuang, MJ Keiser ACS chemical biology 13 (10), 2819-2821, 2018 | 52 | 2018 |
| A mild and general Larock indolization protocol for the preparation of unnatural tryptophans KV Chuang, ME Kieffer, SE Reisman Organic letters 18 (18), 4750-4753, 2016 | 44 | 2016 |
| Attention-based learning on molecular ensembles KV Chuang, MJ Keiser arXiv preprint arXiv:2011.12820, 2020 | 5 | 2020 |
| Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks ZJ Gale-Day, L Shub, KV Chuang, MJ Keiser | 1 | 2024 |
| Improving graph generation by restricting graph bandwidth NL Diamant, AM Tseng, KV Chuang, T Biancalani, G Scalia International Conference on Machine Learning, 7939-7959, 2023 | 1 | 2023 |
| CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning CA Grambow, H Weir, CN Cunningham, T Biancalani, KV Chuang arXiv preprint arXiv:2305.08057, 2023 | 1 | 2023 |
| A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning A Atsango, NL Diamant, Z Lu, T Biancalani, G Scalia, KV Chuang arXiv preprint arXiv:2211.02130, 2022 | 1 | 2022 |
| Synthetic route to anhydroryanodol, ryanodol and structural analogues S Reisman, KV Chuang, C Xu US Patent 9,862,696, 2018 | 1 | 2018 |
| RINGER: Conformer Ensemble Generation of Macrocyclic Peptides with Sequence-Conditioned Internal Coordinate Diffusion CA Grambow, H Weir, NL Diamant, T Biancalani, G Scalia, KV Chuang | | 2023 |
| Learning to Explain Hypergraph Neural Networks S Maleki, E Hajiramezanali, G Scalia, T Biancalani, KV Chuang | | 2023 |
| RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion CA Grambow, H Weir, NL Diamant, AM Tseng, T Biancalani, G Scalia, ... arXiv preprint arXiv:2305.19800, 2023 | | 2023 |
| Deep Fitness Inference for Drug Discovery with Directed Evolution NL Diamant, Z Lu, C Helmling, KV Chuang, C Cunningham, T Biancalani, ... NeurIPS 2022 Workshop on Learning Meaningful Representations of Life, 2022 | | 2022 |
Sarah E. ReismanCALIFORNIA INSTITUTE OF TECHNOLOGYVerified email at caltech.edu
