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Advances in machine learning have led to graph neural network-based methods for drug discovery, yielding promising results in molecular design, chemical synthesis planning, and molecular property prediction.
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Langley, P · 2000
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Benchmark data set for in silico prediction of ames mutagenicity
Hansen, K., Mika, S., Schroeter, T., Sutter, A., Ter Laak, A., Steger-Hartmann, T., Heinrich, N., and Müller, K.-R · 2009
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Comprehensive characterization of cytochrome p450 isozyme selectivity across chemical libraries
Veith, H., Southall, N., Huang, R., James, T., Fayne, D., Artemenko, N., Shen, M., Inglese, J., Austin, C. P., Lloyd, D. G., et al · 2009
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Computationally efficient algorithm to identify matched molecular pairs (mmps) in large data sets
Hussain, J. and Rea, C · 2010
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Toxalerts: a web server of structural alerts for toxic chemicals and compounds with potential adverse reactions, 2012
Sushko, I., Salmina, E., Potemkin, V. A., Poda, G., and Tetko, I. V · 2012
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Recent progress in understanding activity cliffs and their utility in medicinal chemistry: miniperspective
Stumpfe, D., Hu, Y., Dimova, D., and Bajorath, J · 2014
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Data-driven identification of structural alerts for mitigating the risk of drug-induced human liver injuries
Liu, R., Yu, X., and Wallqvist, A · 2015
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Zinc 15–ligand discovery for everyone
Sterling, T. and Irwin, J. J · 2015
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” why should i trust you?” explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 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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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
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Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Relational inductive biases, deep learning, and graph networks
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., et al · 2018
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Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism
Xiong, Z., Wang, D., Liu, X., Zhong, F., Wan, X., Li, X., Li, Z., Luo, X., Chen, K., Jiang, H., et al · 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., et al · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Ying, R., Bourgeois, D., You, J., Zitnik, M., and Leskovec, J · 2019
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Drug discovery with explainable artificial intelligence
Jiménez-Luna, J., Grisoni, F., and Schneider, G · 2020
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Multi-objective molecule generation using interpretable substructures
Jin, W., Barzilay, R., and Jaakkola, T · 2020
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A survey of methods for explaining black box models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D · 2018
Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
Cited alongside, same era.
Constrained graph variational autoencoders for molecule design
Liu, Q., Allamanis, M., Brockschmidt, M., and Gaunt, A. L · 2018
Cited alongside, same era.
Construction of an integrated database for herg blocking small molecules
Sato, T., Yuki, H., Ogura, K., and Honma, T · 2018
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Graph Attention Networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Unmasking clever hans predictors and assessing what machines really learn
Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., and Müller, K.-R · 2019
Cited alongside, same era.
Using attribution to decode binding mechanism in neural network models for chemistry
McCloskey, K., Taly, A., Monti, F., Brenner, M. P., and Colwell, L. J · 2019
Cited alongside, same era.
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I · 2020
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Evaluating attribution for graph neural networks
Sanchez-Lengeling, B., Wei, J., Lee, B., Reif, E., Wang, P., Qian, W. W., McCloskey, K., Colwell, L., and Wiltschko, A · 2020
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Communicative representation learning on attributed molecular graphs
Song, Y., Zheng, S., Niu, Z., Fu, Z.-H., Lu, Y., and Yang, Y · 2020
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Retroxpert: Decompose retrosynthesis prediction like a chemist
Yan, C., Ding, Q., Zhao, P., Zheng, S., Yang, J., Yu, Y., and Huang, J · 2020
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Coloring molecules with explainable artificial intelligence for preclinical relevance assessment
Jiménez-Luna, J., Skalic, M., Weskamp, N., and Schneider, G · 2021
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Molrep: A deep representation learning library for molecular property prediction
Rao, J., Zheng, S., Song, Y., Chen, J., Li, C., Xie, J., Yang, H., Chen, H., and Yang, Y · 2021
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Graph information bottleneck for subgraph recognition
Yu, J., Xu, T., Rong, Y., Bian, Y., Huang, J., and He, R · 2021
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