methodology-8
NMR J-coupling
3260 papers • 126 benchmarks • 313 datasets
https://github.com/larsbratholm/champs_kaggle
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A community-powered search of machine learning strategy space to find NMR property prediction models
J. Z. Kolter, Luka Stojanovic, G. Rakocevic, Brandon M. Anderson, R. Kondor, Lars A. Bratholm, W. Gerrard, Shaojie Bai, Sunghwan Choi, Lam Dang, Pavel Hanchar, Addison Howard, Guillaume Huard, Sanghoon Kim, M. Kornbluth, Youhan Lee, Youngsoo Lee, J. Mailoa, Thanh Tu Nguyen, Milos Popovic, Walter Reade, Wonho Song, E. Thiede, Nebojsa Tijanic, A. Torrubia, Devin Willmott, C. Butts, D. Glowacki, Kaggle participants•Wed Aug 12 2020
An online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules highlights the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties.