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Being able to identify regions within or around proteins, to which ligands can potentially bind, is an essential step to develop new drugs.
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Structure-based maximal affinity model predicts small-molecule druggability
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Understanding and Predicting Druggability. A High-Throughput Method for Detection of Drug Binding Sites
Schmidtke, P. and Barril, X · 2010
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A Critical Comparative Assessment of Predictions of Protein-Binding Sites for Biologically Relevant Organic Compounds
Chen, K., Mizianty, M. J., Gao, J., and Kurgan, L · 2011
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ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., and Overington, J. P · 2011
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sc-PDB: a 3D-database of ligandable binding sites—10 years on
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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
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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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Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N. and Welling, M · 2017
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Learning Graph-Level Representation for Drug Discovery
Li, J., Cai, D., and He, X · 2017
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Graph Classification via Deep Learning with Virtual Nodes
Pham, T., Tran, T., Dam, H., and Venkatesh, S · 2017
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SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
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P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
Krivák, R. and Hoksza, D · 2018
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Schütt, K., Unke, O., and Gastegger, M · 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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Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., Velankar, S., Kleywegt, G. J., Bateman, A., Evans, R., Pritzel, A., Figurnov, M., Ronneberger, O., Bates, R., Kohl, S. A. A., Potapenko, A., Ballard, A. J., Romera-Paredes, B., Nikolov, S., Jain, R., Clancy, E., Reiman, D., Petersen, S., Senior, A. W., Kavukcuoglu, K., Birney, E., Kohli, P., Jumper, J., and Hassabis, D · 2021
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Batatia, I., Kovacs, D. P., Simm, G., Ortner, C., and Csányi, G · 2022
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Decoupled Weight Decay Regularization
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Language models of protein sequences at the scale of evolution enable accurate structure predictionE
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