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Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery.
Announcing the worldwide Protein Data Bank
Berman, H., Henrick, K., and Nakamura, H · 2003
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Glide: a new approach for rapid, accurate docking and scoring. 2. enrichment factors in database screening
Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., and Banks, J. L · 2004
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Open babel, 2005
Open Babel development team · 2005
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ehits: a new fast, exhaustive flexible ligand docking system
Zsoldos, Z., Reid, D., Simon, A., Sadjad, S. B., and Johnson, A. P · 2007
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Dock 6: Combining techniques to model rna–small molecule complexes
Lang, P. T., Brozell, S. R., Mukherjee, S., Pettersen, E. F., Meng, E. C., Thomas, V., Rizzo, R. C., Case, D. A., James, T. L., and Kuntz, I. D · 2009
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Active site prediction using evolutionary and structural information
Sankararaman, S., Sha, F., Kirsch, J. F., Jordan, M. I., and Sjölander, K · 2010
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Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Trott, O. and Olson, A. J · 2010
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Exploring chemical space for drug discovery using the chemical universe database
Reymond, J.-L. and Awale, M · 2012
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Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise
Koes, D. R., Baumgartner, M. P., and Camacho, C. J · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Better informed distance geometry: using what we know to improve conformation generation
Riniker, S. and Landrum, G. A · 2015
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Wallach, I., Dzamba, M., and Heifets, A · 2015
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Group equivariant convolutional networks
Cohen, T. and Welling, M · 2016
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Insights into protein–ligand interactions: mechanisms, models, and methods
Du, X., Li, Y., Xia, Y.-L., Ai, S.-M., Liang, J., Sang, P., Ji, X.-L., and Liu, S.-Q · 2016
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Rdkit: Open-source cheminformatics software, 2016
Landrum, G · 2016
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A systematic analysis of atomic protein–ligand interactions in the pdb
de Freitas, R. F. and Schapira, M · 2017
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Protein-ligand blind docking using quickvina-w with inter-process spatio-temporal integration
Hassan, N. M., Alhossary, A. A., Mu, Y., and Kwoh, C.-K · 2017
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Deepsite: protein-binding site predictor using 3d-convolutional neural networks
Jiménez, J., Doerr, S., Martínez-Rosell, G., Rose, A. S., and De Fabritiis, G · 2017
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Forging the basis for developing protein–ligand interaction scoring functions
Liu, Z., Su, M., Han, L., Liu, J., Yang, Q., Li, Y., and Wang, R · 2017
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Protein–ligand scoring with convolutional neural networks
Ragoza, M., Hochuli, J., Idrobo, E., Sunseri, J., and Koes, D. R · 2017
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Interpretable drug target prediction using deep neural representation
Gao, K. Y., Fokoue, A., Luo, H., Iyengar, A., Dey, S., and Zhang, P · 2018
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Comprehensive assessment of flexible-ligand docking algorithms: current effectiveness and challenges
Huang, S.-Y · 2018
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Ligand-biased ensemble receptor docking (ligbend): a hybrid ligand/receptor structure-based approach
Lam, P. C.-H., Abagyan, R., and Totrov, M · 2018
Cited alongside, same era.
Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., and Riley, P · 2018
Cited alongside, same era.
Deepaffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks
Karimi, M., Wu, D., Wang, Z., and Shen, Y · 2019
Cited alongside, same era.
Hybrid receptor structure/ligand-based docking and activity prediction in icm: development and evaluation in d3r grand challenge 3
Lam, P. C.-H., Abagyan, R., and Totrov, M · 2019
Cited alongside, same era.
Graph matching networks for learning the similarity of graph structured objects
Li, Y., Gu, C., Dullien, T., Vinyals, O., and Kohli, P · 2019
Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., and Kozinsky, B · 2021
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Geometric and physical quantities improve e (3) equivariant message passing
Brandstetter, J., Hesselink, R., van der Pol, E., Bekkers, E., and Welling, M · 2021
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Protein interaction interface region prediction by geometric deep learning
Dai, B. and Bailey-Kellogg, C · 2021
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Structure-aware generation of drug-like molecules
Drotár, P., Jamasb, A. R., Day, B., Cangea, C., and Liò, P · 2021
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Protein complex prediction with alphafold-multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A. W., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., Ronneberger, O., Bodenstein, S., Zielinski, M., Bridgland, A., Potapenko, A., Cowie, A., Tunyasuvunakool, K., Jain, R., Clancy, E., Kohli, P., Jumper, J., and Hassabis, D · 2021
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Cited alongside, same era.
Predicting drug–target interaction using a novel graph neural network with 3d structure-embedded graph representation
Lim, J., Ryu, S., Park, K., Choe, Y. J., Ham, J., and Kim, W. Y · 2019
Cited alongside, same era.
Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies
Macari, G., Toti, D., and Polticelli, F · 2019
Cited alongside, same era.
End-to-end learning on 3d protein structure for interface prediction
Townshend, R., Bedi, R., Suriana, P., and Dror, R · 2019
Cited alongside, same era.
Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes
Eismann, S., Townshend, R. J., Thomas, N., Jagota, M., Jing, B., and Dror, R. O · 2020
Cited alongside, same era.
Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design
Francoeur, P. G., Masuda, T., Sunseri, J., Jia, A., Iovanisci, R. B., Snyder, I., and Koes, D. R · 2020
Cited alongside, same era.
Se(3)-transformers: 3d roto-translation equivariant attention networks
Fuchs, F. B., Worrall, D. E., Fischer, V., and Welling, M · 2020
Cited alongside, same era.
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Gainza, P., Sverrisson, F., Monti, F., Rodola, E., Boscaini, D., Bronstein, M., and Correia, B · 2020
Cited alongside, same era.
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Structure-based protein function prediction using graph convolutional networks
Gligorijević, V., Renfrew, P. D., Kosciolek, T., Leman, J. K., Berenberg, D., Vatanen, T., Chandler, C., Taylor, B. C., Fisk, I. M., Vlamakis, H., et al · 2021
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Learning physics confers pose-sensitivity in structure-based virtual screening
Gniewek, P., Worley, B., Stafford, K., Bedem, H. v. d., and Anderson, B · 2021
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Deep generative design with 3d pharmacophoric constraints
Imrie, F., Hadfield, T. E., Bradley, A. R., and Deane, C. M · 2021
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Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions
Jiang, D., Hsieh, C.-Y., Wu, Z., Kang, Y., Wang, J., Wang, E., Liao, B., Shen, C., Xu, L., Wu, J., et al · 2021
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Side-chain packing using se (3)-transformer
Jindal, A., Kotelnikov, S., Padhorny, D., Kozakov, D., Zhu, Y., Chowdhury, R., and Vajda, S · 2021
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Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity
Li, S., Zhou, J., Xu, T., Huang, L., Wang, F., Xiong, H., Huang, W., Dou, D., and Xiong, H · 2021
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Predicting molecular conformation via dynamic graph score matching
Luo, S., Shi, C., Xu, M., and Tang, J · 2021
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Gnina 1.0: molecular docking with deep learning
McNutt, A. T., Francoeur, P., Aggarwal, R., Masuda, T., Meli, R., Ragoza, M., Sunseri, J., and Koes, D. R · 2021
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A geometric deep learning approach to predict binding conformations of bioactive molecules
Méndez-Lucio, O., Ahmad, M., del Rio-Chanona, E. A., and Wegner, J. K · 2021
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Instadock: A single-click graphical user interface for molecular docking-based virtual high-throughput screening
Mohammad, T., Mathur, Y., and Hassan, M. I · 2021
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E(n)-equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
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The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction
Shen, C., Hu, X., Gao, J., Zhang, X., Zhong, H., Wang, Z., Xu, L., Kang, Y., Cao, D., and Hou, T · 2021
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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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Multi-scale representation learning on proteins
Somnath, V. R., Bunne, C., and Krause, A · 2021
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Fast end-to-end learning on protein surfaces
Sverrisson, F., Feydy, J., Correia, B. E., and Bronstein, M. M · 2021
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Molgensurvey: A systematic survey in machine learning models for molecule design
Du, Y., Fu, T., Sun, J., and Liu, S · 2022
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