| Equibind: Geometric deep learning for drug binding structure prediction H Stärk, O Ganea, L Pattanaik, R Barzilay, T Jaakkola International conference on machine learning, 20503-20521, 2022 | 286 | 2022 |
| Deep learning of activation energies CA Grambow, L Pattanaik, WH Green The journal of physical chemistry letters 11 (8), 2992-2997, 2020 | 153 | 2020 |
| Geomol: Torsional geometric generation of molecular 3d conformer ensembles O Ganea, L Pattanaik, C Coley, R Barzilay, K Jensen, W Green, ... Advances in Neural Information Processing Systems 34, 13757-13769, 2021 | 144 | 2021 |
| Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry CA Grambow, L Pattanaik, WH Green Scientific data 7 (1), 137, 2020 | 129 | 2020 |
| Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors Y Guan, CW Coley, H Wu, D Ranasinghe, E Heid, TJ Struble, L Pattanaik, ... Chemical science 12 (6), 2198-2208, 2021 | 117 | 2021 |
| Molecular representation: going long on fingerprints L Pattanaik, CW Coley Chem 6 (6), 1204-1207, 2020 | 80 | 2020 |
| Selectively converting glucose to fructose using immobilized tertiary amines N Deshpande, L Pattanaik, MR Whitaker, CT Yang, LC Lin, NA Brunelli Journal of Catalysis 353, 205-210, 2017 | 60 | 2017 |
| Generating transition states of isomerization reactions with deep learning L Pattanaik, JB Ingraham, CA Grambow, WH Green Physical Chemistry Chemical Physics 22 (41), 23618-23626, 2020 | 56 | 2020 |
| Fast predictions of reaction barrier heights: toward coupled-cluster accuracy KA Spiekermann, L Pattanaik, WH Green The Journal of Physical Chemistry A 126 (25), 3976-3986, 2022 | 46 | 2022 |
| High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions K Spiekermann, L Pattanaik, WH Green Scientific Data 9 (1), 417, 2022 | 42 | 2022 |
| Learning 3d representations of molecular chirality with invariance to bond rotations K Adams, L Pattanaik, CW Coley arXiv preprint arXiv:2110.04383, 2021 | 38 | 2021 |
| Message passing networks for molecules with tetrahedral chirality L Pattanaik, OE Ganea, I Coley, KF Jensen, WH Green, CW Coley arXiv preprint arXiv:2012.00094, 2020 | 23 | 2020 |
| Recrystallization improves the mechanical properties of sintered electrospun polycaprolactone MT Nelson, L Pattanaik, M Allen, M Gerbich, K Hux, M Allen, JJ Lannutti journal of the mechanical behavior of biomedical materials 30, 150-158, 2014 | 17 | 2014 |
| International Conference on Machine Learning H Stärk, O Ganea, L Pattanaik, R Barzilay, T Jaakkola PMLR, 2022 | 15 | 2022 |
| Equibind: Geometric deep learning for drug binding structure prediction. arXiv 2022 H Stärk, OE Ganea, L Pattanaik, R Barzilay, T Jaakkola arXiv preprint arXiv:2202.05146 10, 0 | 9 | |
| Comment on ‘physics-based representations for machine learning properties of chemical reactions’ KA Spiekermann, T Stuyver, L Pattanaik, WH Green Machine Learning: Science and Technology 4 (4), 048001, 2023 | 7 | 2023 |
| ConfSolv: Prediction of Solute Conformer-Free Energies across a Range of Solvents L Pattanaik, A Menon, V Settels, KA Spiekermann, Z Tan, FH Vermeire, ... The Journal of Physical Chemistry B 127 (47), 10151-10170, 2023 | 6 | 2023 |
| An Automated Workflow to Rapidly and Accurately Generate Transition State Structures Using Machine Learning L Pattanaik, X Dong, K Spiekermann, W Green 2021 AIChE Annual Meeting, 2021 | 2 | 2021 |
| Towards Automated Reaction Kinetics with Message Passing Neural Networks L Pattanaik Massachusetts Institute of Technology, 2023 | 1 | 2023 |
| An End-to-End Workflow for Diverse Transition State Conformer Generation Using Machine Learning L Pattanaik, X Dong, H Wu, K Spiekermann, HW Pang, W Green 2022 AIChE Annual Meeting, 2022 | | 2022 |
