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Lagnajit Pattanaik
Lagnajit Pattanaik
Department of Chemical Engineering, MIT
Verified email at mit.edu
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Cited by
Cited by
Year
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
2052022
Deep learning of activation energies
CA Grambow, L Pattanaik, WH Green
The journal of physical chemistry letters 11 (8), 2992-2997, 2020
1282020
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
1072020
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
1052021
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
992021
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
572017
Molecular representation: going long on fingerprints
L Pattanaik, CW Coley
Chem 6 (6), 1204-1207, 2020
562020
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
502020
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
382022
Learning 3d representations of molecular chirality with invariance to bond rotations
K Adams, L Pattanaik, CW Coley
arXiv preprint arXiv:2110.04383, 2021
312021
High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions
K Spiekermann, L Pattanaik, WH Green
Scientific Data 9 (1), 417, 2022
282022
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
202020
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
152014
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
10
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
22023
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
22021
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
12023
Towards Automated Reaction Kinetics with Message Passing Neural Networks
L Pattanaik
Massachusetts Institute of Technology, 2023
12023
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
Towards Fully Automated Generation of Predictive Detailed Kinetic Models: 2, 3-Dimethylbut-2-Ene Oxidation Modeling As an Example
X Dong, L Pattanaik, K Spiekermann, W Green
2021 AIChE Annual Meeting, 2021
2021
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