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Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.
Contrastive representation distillation
Tian, Y., Krishnan, D., and Isola, P · 1910
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The properties of known drugs. 1. molecular frameworks
Bemis, G. W. and Murcko, M. A · 1996
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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A survey on transfer learning
Pan, S. J. and Yang, Q · 2010
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and von Lilienfeld, O. A · 2014
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Rdkit: Open-source cheminformatics software, 2016
Landrum, G · 2016
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Learning graph-level representation for drug discovery
Li, J., Cai, D., and He, X · 2017
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Automatic differentiation in pytorch, 2017
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Schütt, K., Kindermans, P., Felix, H. E. S., Chmiela, S., Tkatchenko, A., and Müller, K · 2017
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Moleculenet: A benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V. S · 2017
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Mutual information neural estimation
Belghazi, M. I., Baratin, A., Rajeswar, S., Ozair, S., Bengio, Y., Hjelm, R. D., and Courville, A. C · 2018
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Predicting molecular properties with covariant compositional networks
Hy, T. S., Trivedi, S., Pan, H., Anderson, B. M., and Kondor, R · 2018
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Representation learning with contrastive predictive coding
van den Oord, A., Li, Y., and Vinyals, O · 2018
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A graph-convolutional neural network model for the prediction of chemical reactivity
Coley, C., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., and Jensen, K. F · 2019
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
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Exploration of chemical compound, conformer, and reaction space with meta-dynamics simulations based on tight-binding quantum chemical calculations
Grimme, S · 2019
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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y · 2019
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On the spectral bias of neural networks
Rahaman, N., Baratin, A., Arpit, D., Draxler, F., Lin, M., Hamprecht, F., Bengio, Y., and Courville, A · 2019
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Recent advances and applications of machine learning in solid-state materials science
Schmidt, J., Marques, M. R. G., Botti, S., and Marques, M. A. L · 2019
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Graph convolutional neural networks for predicting drug-target interactions
Torng, W. and Altman, R. B · 2019
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Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges
Unke, O. T. and Meuwly, M · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X., Zhou, J., Ma, C., Yu, L., Gai, Y., Xiao, T., He, T., Karypis, G., Li, J., and Zhang, Z · 2019
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Analyzing learned molecular representations for property prediction
Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., Palmer, A., Settels, V., Jaakkola, T., Jensen, K., and Barzilay, R · 2019
A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility
Tang, B., Kramer, S. T., Fang, M., Qiu, Y., Wu, Z., and Xu, D · 2020
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Building attention and edge message passing neural networks for bioactivity and physical-chemical property prediction
Withnall, M., Lindelöf, E., Engkvist, O., and Chen, H · 2020
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Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
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Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Bardes, A., Ponce, J., and LeCun, Y · 2021
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Bronstein, M. M., Bruna, J., Cohen, T., and Velickovic, P · 2021
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Geom: Energy-annotated molecular conformations for property prediction and molecular generation
Axelrod, S. and Gomez-Bombarelli, R · 2020
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Molecular machine learning with conformer ensembles
Axelrod, S. and Gómez-Bombarelli, R · 2020
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Gnn-film: Graph neural networks with feature-wise linear modulation
Brockschmidt, M · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. E · 2020
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Exploring simple siamese representation learning
Chen, X. and He, K · 2020
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Principal neighbourhood aggregation for graph nets
Corso, G., Cavalleri, L., Beaini, D., Liò, P., and Veličković, P · 2020
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Geomol: Torsional geometric generation of molecular 3d conformer ensembles, 2021
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Highly accurate protein structure prediction with alphafold
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Spherical message passing for 3d graph networks
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E(n) equivariant graph neural networks
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Learning gradient fields for molecular conformation generation
Shi, C., Luo, S., Xu, M., and Tang, J · 2021
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Molclr: Molecular contrastive learning of representations via graph neural networks
Wang, Y., Wang, J., Cao, Z., and Farimani, A. B · 2021
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Learning neural generative dynamics for molecular conformation generation
Xu, M., Luo, S., Bengio, Y., Peng, J., and Tang, J · 2021
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Self-supervised graph-level representation learning with local and global structure
Xu, M., Wang, H., Ni, B., Guo, H., and Tang, J · 2021
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Graph contrastive learning automated
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Barlow twins: Self-supervised learning via redundancy reduction
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Dual-view molecule pre-training
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Pre-training molecular graph representation with 3d geometry
Liu, S., Wang, H., Liu, W., Lasenby, J., Guo, H., and Tang, J · 2022
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