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Molecular geometry prediction of flexible molecules, or conformer search, is a long-standing challenge in computational chemistry.
A generative model for molecular distance geometry
Simm, G. N. and Hernández-Lobato, J. M. (2019) · 1909
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Solving rubik’s cube with a robot hand
Akkaya, I., Andrychowicz, M., Chociej, M., Litwin, M., McGrew, B., Petron, A., Paino, A., Plappert, M., Powell, G., Ribas, R., et al. (2019) · 1910
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How to fold graciously
Levinthal, C. (1969) · 1969
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Complexity analysis of real-time reinforcement learning
Koenig, S. and Simmons, R. G. (1993) · 1993
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Merck molecular force field. iv. conformational energies and geometries for mmff94
Halgren, T. A. and Nachbar, R. B. (1996) · 1996
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Basic terminology of stereochemistry (IUPAC recommendations 1996)
Moss, G. P. (1996) · 1996
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Long short-term memory
Hochreiter, S. and Schmidhuber, J. (1997) · 1997
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Curriculum learning for reinforcement learning domains: A framework and survey
Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M. E., and Stone, P. (2020) · 2003
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Self-guided langevin dynamics simulation method
Wu, X. and Brooks, B. R. (2003) · 2003
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Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J. (2009) · 2009
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Charmm: the biomolecular simulation program
Brooks, B. R., Brooks, C. L, r., Mackerell Jr., A. D., Nilsson, L., Petrella, R. J., Roux, B., Won, Y., Archontis, G., Bartels, C., Boresch, S., Caflisch, A., Caves, L., Cui, Q., Dinner, A. R., Feig, M., Fischer, S., Gao, J., Hodoscek, M., Im, W., Kuczera, K., Lazaridis, T., Ma, J., Ovchinnikov, V., Paci, E., Pastor, R. W., Post, C. B., Pu, J. Z., Schaefer, M., Tidor, B., Venable, R. M., Woodcock, H. L., Wu, X., Yang, W., York, D. M., and Karplus, M. (2009) · 2009
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Scaling of multimillion-atom biological molecular dynamics simulation on a petascale supercomputer
Schulz, R., Lindner, B., Petridis, L., and Smith, J. C. (2009) · 2009
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Confab - systematic generation of diverse low-energy conformers
O’Boyle, N. M., Vandermeersch, T., Flynn, C. J., Maguire, A. R., and Hutchison, G. R. (2011) · 2011
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Freely available conformer generation methods: how good are they?
Ebejer, J.-P., Morris, G. M., and Deane, C. M. (2012) · 2012
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Tfd: torsion fingerprints as a new measure to compare small molecule conformations
Schulz-Gasch, T., Scharfer, C., Guba, W., and Rarey, M. (2012) · 2012
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Computational investigation of the pyrolysis product selectivity for α \alpha -hydroxy phenethyl phenyl ether and phenethyl phenyl ether: Analysis of substituent effects and reactant conformer selection
Beste, A. and Buchanan, A. C. (2013) · 2013
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Efficient exploration and value function generalization in deterministic systems
Wen, Z. and Van Roy, B. (2013) · 2013
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Lignin valorization: Improving lignin processing in the biorefinery
Ragauskas, A. J., Beckham, G. T., Biddy, M. J., Chandra, R., Chen, F., Davis, M. F., Davison, B. H., Dixon, R. A., Gilna, P., Keller, M., Langan, P., Naskar, A. K., Saddler, J. N., Tschaplinski, T. J., Tuskan, G. A., and Wyman, C. E. (2014) · 2014
Cited alongside, same era.
Contextual markov decision processes
Hallak, A., Di Castro, D., and Mannor, S. (2015) · 2015
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Better informed distance geometry: Using what we know to improve conformation generation
Riniker, S. and Landrum, G. A. (2015) · 2015
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Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P. (2015) · 2015
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Principal component analysis on a torus: Theory and application to protein dynamics
Sittel, F., Filk, T., and Stock, G. (2017) · 2017
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Generating equilibrium molecules with deep neural networks
Gebauer, N. W., Gastegger, M., and Schütt, K. T. (2018) · 2018
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Adversarial deep reinforcement learning in portfolio management
Liang, Z., Chen, H., Zhu, J., Jiang, K., and Li, Y. (2018) · 2018
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Modularized implementation of deep rl algorithms in pytorch
Shangtong, Z. (2018) · 2018
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Bright side of lignin depolymerization: Toward new platform chemicals
Sun, Z., Fridrich, B., de Santi, A., Elangovan, S., and Barta, K. (2018) · 2018
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Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W. (2016) · 2016
Cited alongside, same era.
Source task creation for curriculum learning
Narvekar, S., Sinapov, J., Leonetti, M., and Stone, P. (2016) · 2016
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An overview of gradient descent optimization algorithms
Ruder, S. (2016) · 2016
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Order matters: Sequence to sequence for sets
Vinyals, O., Bengio, S., and Kudlur, M. (2016) · 2016
Cited alongside, same era.
Initial mechanisms for an overall behavior of lignin pyrolysis through large-scale reaxff molecular dynamics simulations
Zhang, T., Li, X., Qiao, X., Zheng, M., Guo, L., Song, W., and Lin, W. (2016) · 2016
Cited alongside, same era.
Reverse curriculum generation for reinforcement learning
Florensa, C., Held, D., Wulfmeier, M., Zhang, M., and Abbeel, P. (2017) · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E. (2017) · 2017
Cited alongside, same era.
Nervenet: Learning structured policy with graph neural networks
Wang, T., Liao, R., Ba, J., and Fidler, S. (2018) · 2018
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Curriculum learning by transfer learning: Theory and experiments with deep networks
Weinshall, D., Cohen, G., and Amir, D. (2018) · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
You, J., Liu, B., Ying, Z., Pande, V., and Leskovec, J. (2018) · 2018
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Reinforcement and imitation learning for diverse visuomotor skills
Zhu, Y., Wang, Z., Merel, J., Rusu, A., Erez, T., Cabi, S., Tunyasuvunakool, S., Kramár, J., Hadsell, R., de Freitas, N., and Heess, N. (2018) · 2018
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E. (2019) · 2019
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Molecular geometry prediction using a deep generative graph neural network
Mansimov, E., Mahmood, O., Kang, S., and Cho, K. (2019) · 2019
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Lignin-kmc: A toolkit for simulating lignin biosynthesis
Orella, M. J., Gani, T. Z. H., Vermaas, J. V., Stone, M. L., Anderson, E. M., Beckham, G. T., Brushett, F. R., and Román-Leshkov, Y. (2019) · 2019
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No-regret exploration in contextual reinforcement learning
Modi, A. and Tewari, A. (2020) · 2020
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Improved protein structure prediction using potentials from deep learning
Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W., Bridgland, A., et al. (2020) · 2020
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Designing for a green chemistry future
Zimmerman, J. B., Anastas, P. T., Erythropel, H. C., and Leitner, W. (2020) · 2020
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