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Recent research in molecular discovery has primarily been devoted to small, drug-like molecules, leaving many similarly important applications in material design without adequate technology.
The Carcinogenic Activities of Certain Halogen Derivatives of 4-Dimethylaminoazobenzene in the Rat*
Miller, J. A., Sapp, R. W., and Miller, E. C · 1949
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Random processes with reinforcement
Pemantle, R. A · 1988
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Compendium of Chemical Terminology
IUPAC · 1997
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Correlation and prediction of gas permeability in glassy polymer membrane materials via a modified free volume based group contribution method
Park, J. and Paul, D. R · 1997
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The Predictive Toxicology Challenge 2000–2001
Helma, C., King, R. D., Kramer, S., and Srinivasan, A · 2001
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New speciality surfactants with natural structural motifs
Blunk, D., Bierganns, P., Bongartz, N., Tessendorf, R., and Stubenrauch, C · 2006
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On the art of compiling and using ’drug-like’ chemical fragment spaces
Degen, J., Wegscheid-Gerlach, C., Zaliani, A., and Rarey, M · 2008
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Memory efficient anonymous graph exploration
Gasieniec, L. and Radzik, T · 2008
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Extended-connectivity fingerprints
Rogers, D. and Hahn, M · 2010
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Metabolic activation in drug-induced liver injury
Leung, L., Kalgutkar, A. S., and Obach, R. S · 2012
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Memory in network flows and its effects on spreading dynamics and community detection
Rosvall, M., Esquivel, A. V., Lancichinetti, A., West, J. D., and Lambiotte, R · 2014
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Modeling epoxidation of drug-like molecules with a deep machine learning network
Hughes, T. B., Miller, G. P., and Swamidass, S. J · 2015
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Zinc 15–ligand discovery for everyone
Sterling, T. and Irwin, J. J · 2015
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Rdkit: Open-source cheminformatics software
Landrum, G · 2016
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The harvard organic photovoltaic dataset
Lopez, S. A., Pyzer-Knapp, E. O., Simm, G. N., Lutzow, T., Li, K., Seress, L. R., Hachmann, J., and Aspuru-Guzik, A · 2016
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Random walks and diffusion on networks
Masuda, N., Porter, M. A., and Lambiotte, R · 2017
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50th anniversary perspective: Conducting/semiconducting conjugated polymers. a personal perspective on the past and the future
Swager, T. M · 2017
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Learning heat diffusion graphs
Thanou, D., Dong, X., Kressner, D., and Frossard, P · 2017
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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
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Learning deep generative models of graphs
Li, Y., Vinyals, O., Dyer, C., Pascanu, R., and Battaglia, P · 2018
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Constrained graph variational autoencoders for molecule design
Liu, Q., Allamanis, M., Brockschmidt, M., and Gaunt, A · 2018
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Polymers of intrinsic microporosity for energy-intensive membrane-based gas separations
Wang, Y., Ma, X., Ghanem, B., Alghunaimi, F., Pinnau, I., and Han, Y · 2018
Cited alongside, same era.
Graph convolutional policy network for goal-directed molecular graph generation
You, J., Liu, B., Ying, Z., Pande, V., and Leskovec, J · 2018
Cited alongside, same era.
Molecular hypergraph grammar with its application to molecular optimization
Kajino, H · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Revisiting group contribution theory for estimating fractional free volume of microporous polymer membranes
Wu, A. X., Lin, S., Rodriguez, K. M., Benedetti, F. M., Joo, T., Grosz, A. F., Storme, K. R., Roy, N., Syar, D., and Smith, Z. P · 2021
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A graph representation of molecular ensembles for polymer property prediction
Aldeghi, M. and Coley, C. W · 2022
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Equivariant subgraph aggregation networks
Bevilacqua, B., Frasca, F., Lim, D., Srinivasan, B., Cai, C., Balamurugan, G., Bronstein, M., and Maron, H · 2022
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Graph coarsening: from scientific computing to machine learning
Chen, J., Saad, Y., and Zhang, Z · 2022
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Understanding and extending subgraph gnns by rethinking their symmetries
Frasca, F., Bevilacqua, B., Bronstein, M. M., and Maron, H · 2022
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Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
Cited alongside, same era.
The role of chemical design in the performance of organic semiconductors
Bronstein, H., Nielsen, C. B., Schroeder, B. C., and McCulloch, I · 2020
Cited alongside, same era.
Retro*: Learning retrosynthetic planning with neural guided a* search
Chen, B., Li, C., Dai, H., and Song, L · 2020
Cited alongside, same era.
Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Marinka Zitnik, P. L., Pande, V., and Leskovec, J · 2020
Cited alongside, same era.
Hierarchical generation of molecular graphs using structural motifs
Jin, W., Barzilay, R., and Jaakkola, T · 2020
Cited alongside, same era.
Self-referencing embedded strings (selfies): A 100
Krenn, M., Häse, F., Nigam, A., Friederich, P., and Aspuru-Guzik, A · 2020
Cited alongside, same era.
Surfactant-like peptides: From molecular design to controllable self-assembly with applications
Li, J., Wang, J., Zhao, Y., Zhou, P., Carter, J., Li, Z., Waigh, T. A., Lu, J. R., and Xu, H · 2020
Cited alongside, same era.
Guo, M., Shou, W., Makatura, L., Erps, T., Foshey, M., and Matusik, W · 2022
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Multigran-smiles: multi-granularity smiles learning for molecular property prediction
Jiang, J., Zhang, R., Zhao, Z., Ma, J., Liu, Y., Yuan, Y., and Niu, B · 2022
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A strategic approach to machine learning for material science: How to tackle real-world challenges and avoid pitfalls
Karande, P., Gallagher, B., and Han, T. Y.-J · 2022
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Design and synthesis of novel oxime ester photoinitiators augmented by automated machine learning
Lee, W. J., Kwak, H. S., Lee, D.-r., Oh, C., Yum, E. K., An, Y., Halls, M. D., and Lee, C.-W · 2022
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Molecular contrastive learning of representations via graph neural networks
Wang, Y., Wang, J., Cao, Z., and Farimani, A. B · 2022
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Learning substructure invariance for out-of-distribution molecular representations
Yang, N., Zeng, K., Qitian Wu, X. J., and Yan, J · 2022
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Dynamic polypyrrole core–shell chemomechanical actuators
Yuan, W., Vijayamohanan, H., Luo, S.-X. L., Husted, K., Johnson, J. A., and Swager, T. M · 2022
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A gromov–wasserstein geometric view of spectrum-preserving graph coarsening
Chen, Y., Yao, R., Yang, Y., and Chen, J · 2023
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A cover time study of a non-markovian algorithm
Fang, G., Samorodnitsky, G., and Xu, Z · 2023
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Context-enriched molecule representations improve few-shot drug discovery
Schimunek, J., Seidl, P., Friedrich, L., Kuhn, D., Rippmann, F., Hochreiter, S., and Klambauer, G · 2023
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Epoxy thermosets designed for chemical recycling
Türel, T., Dağlar, Ö., Eisenreich, F., and Tomović, Ž · 2023
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The mechanical performance prediction of steel materials based on random forest
Wang, S. and Wu, X · 2023
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Uni-mol: A universal 3d molecular representation learning framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., and Ke, G · 2023
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Drug discovery informatics market set to surge at 10.9
Sawlani, N · 2024
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