| MoleculeNet: a benchmark for molecular machine learning Z Wu, B Ramsundar, EN Feinberg, J Gomes, C Geniesse, AS Pappu, ... Chemical science 9 (2), 513-530, 2018 | 3077 | 2018 |
| Efficient exploration of chemical space with docking and deep learning Y Yang, K Yao, MP Repasky, K Leswing, R Abel, BK Shoichet, SV Jerome Journal of Chemical Theory and Computation 17 (11), 7106-7119, 2021 | 213 | 2021 |
| Epik: pKa and Protonation State Prediction through Machine Learning RC Johnston, K Yao, Z Kaplan, M Chelliah, K Leswing, S Seekins, ... Journal of chemical theory and computation 19 (8), 2380-2388, 2023 | 133 | 2023 |
| Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy B Ramsundar, P Eastman, P Walters, V Pande, K Leswing, Z Wu Drug Discovery, and More 1, 2019 | 131 | 2019 |
| Reaction-based enumeration, active learning, and free energy calculations to rapidly explore synthetically tractable chemical space and optimize potency of cyclin-dependent … KD Konze, PH Bos, MK Dahlgren, K Leswing, I Tubert-Brohman, ... Journal of chemical information and modeling 59 (9), 3782-3793, 2019 | 122 | 2019 |
| Transferable neural network potential energy surfaces for closed-shell organic molecules: Extension to ions LD Jacobson, JM Stevenson, F Ramezanghorbani, D Ghoreishi, ... Journal of Chemical Theory and Computation 18 (4), 2354-2366, 2022 | 52 | 2022 |
| High-dimensional neural network potential for liquid electrolyte simulations S Dajnowicz, G Agarwal, JM Stevenson, LD Jacobson, ... The Journal of Physical Chemistry B 126 (33), 6271-6280, 2022 | 46 | 2022 |
| Combining cloud-based free-energy calculations, synthetically aware enumerations, and goal-directed generative machine learning for rapid large-scale chemical exploration and … P Ghanakota, PH Bos, KD Konze, J Staker, G Marques, K Marshall, ... Journal of Chemical Information and Modeling 60 (9), 4311-4325, 2020 | 44 | 2020 |
| Design of organic electronic materials with a goal-directed generative model powered by deep neural networks and high-throughput molecular simulations HS Kwak, Y An, DJ Giesen, TF Hughes, CT Brown, K Leswing, ... Frontiers in Chemistry 9, 800370, 2022 | 28 | 2022 |
| MoleculeNet: a benchmark for molecular machine learning. Chem Sci 9: 513–530 Z Wu, B Ramsundar, EN Feinberg, J Gomes, C Geniesse, AS Pappu, ... | 25 | 2018 |
| Schr\" odinger-ANI: An Eight-Element Neural Network Interaction Potential with Greatly Expanded Coverage of Druglike Chemical Space JM Stevenson, LD Jacobson, Y Zhao, C Wu, J Maple, K Leswing, ... arXiv preprint arXiv:1912.05079, 2019 | 20 | 2019 |
| De Novo Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen G Marques, K Leswing, T Robertson, D Giesen, MD Halls, A Goldberg, ... The Journal of Physical Chemistry A 125 (33), 7331-7343, 2021 | 19 | 2021 |
| Development of scalable and generalizable machine learned force field for polymers S Mohanty, J Stevenson, AR Browning, L Jacobson, K Leswing, MD Halls, ... Scientific Reports 13 (1), 17251, 2023 | 14 | 2023 |
| Impacting drug discovery projects with large-scale enumerations, machine learning strategies, and free-energy predictions JL Knight, K Leswing, PH Bos, L Wang Free energy methods in drug discovery: current state and future directions …, 2021 | 13 | 2021 |
| De Novo Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen: Part 2 J Staker, K Marshall, K Leswing, T Robertson, MD Halls, A Goldberg, ... The Journal of Physical Chemistry A 126 (34), 5837-5852, 2022 | 10 | 2022 |
| FEP protocol builder: optimization of free energy perturbation protocols using active learning C de Oliveira, K Leswing, S Feng, R Kanters, R Abel, S Bhat Journal of Chemical Information and Modeling 63 (17), 5592-5603, 2023 | 8 | 2023 |
| Leveraging multitask learning to improve the transferability of machine learned force fields L Jacobson, J Stevenson, F Ramezanghorbani, S Dajnowicz, K Leswing | 7 | 2023 |
| Leveraging high-throughput molecular simulations and machine learning for formulation design AK Chew, MAF Afzal, Z Kaplan, EM Collins, S Gattani, M Misra, ... | 1 | 2024 |
| Large-scale Atomistic Simulations of Lithium Diffusion in a Graphite Anode with a Machine Learning Force Field J Cheng, J Stevenson, G Agarwal, J Weber, L Jacobson, K Leswing | | 2025 |
| Leveraging High-throughput Molecular Simulations and Machine Learning for the Design of Chemical Mixtures AK Chew, MAF Afzal, Z Kaplan, EM Collins, S Gattani, M Misra, ... | | 2025 |
Vijay PandeAndreessen Horowitz, Stanford UniversityPatvirtintas el. paštas stanford.edu