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Predicting the bioactivity of a ligand is one of the hardest and most important challenges in computer-aided drug discovery.
Relationship between the inhibition constant ( K I {K}_{I} ) and the concentration of inhibitor which causes 50 percent inhibition ( I 50 {I}_{50} ) of an enzymatic reaction
Cheng, Y.-C. & Prusoff, W. H · 1973
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Berman, H. M. et al · 2000
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The PDBbind Database: Collection of Binding Affinities for Protein-Ligand Complexes with Known Three-Dimensional Structures
Wang, R., Fang, X., Lu, Y. & Wang, S · 2004
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Biopython: freely available Python tools for computational molecular biology and bioinformatics
Cock, P. J. A. et al · 2009
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The experimental uncertainty of heterogeneous public ki data
Kramer, C., Kalliokoski, T., Gedeck, P. & Vulpetti, A · 2012
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Comparability of mixed ic50 data – a statistical analysis
Kalliokoski, T., Kramer, C., Vulpetti, A. & Gedeck, P · 2013
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Adam: A method for stochastic optimization (2017)
Kingma, D. P. & Ba, J · 2017
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Low data drug discovery with one-shot learning
Altae-Tran, H., Ramsundar, B., Pappu, A. S. & Pande, V · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P. & Levine, S · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K. & Zemel, R · 2017
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Deepdta: deep drug–target binding affinity prediction
Öztürk, H., Özgür, A. & Ozkirimli, E · 2018
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Most ligand-based classification benchmarks reward memorization rather than generalization
Wallach, I. & Heifets, A · 2018
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Garnelo, M. et al · 2018
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Ultra-large library docking for discovering new chemotypes
Lyu, J. et al · 2019
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ChEMBL: towards direct deposition of bioassay data
Mendez, D. et al · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A. et al · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Wang, M. et al · 2019
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All-assay-max2 pqsar: Activity predictions as accurate as four-concentration ic50s for 8558 novartis assays
Martin, E. J. et al · 2019
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Imputation of assay bioactivity data using deep learning
Whitehead, T. M., Irwin, B. W. J., Hunt, P., Segall, M. D. & Conduit, G. J · 2019
Synthon-based ligand discovery in virtual libraries of over 11 billion compounds
Sadybekov, A. A. et al · 2022
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Exploration of ultralarge compound collections for drug discovery
Warr, W. A., Nicklaus, M. C., Nicolaou, C. A. & Rarey, M · 2022
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https://enamine.net/library-synthesis/real-compounds/real-space-navigator (2022)
Enamine REAL Space · 2022
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https://www.labnetwork.com/frontend-app/p/##!/library/virtual (2022)
GalaXi Space · 2022
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Metadta: Meta-learning-based drug-target binding affinity prediction (2022)
Lee, E., Yoo, J., Lee, H. & Hong, S · 2022
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On the frustration to predict binding affinities from protein–ligand structures with deep neural networks
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Attentive neural processes
Kim, H. et al · 2019
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GraphDTA: predicting drug–target binding affinity with graph neural networks
Nguyen, T. et al · 2020
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Notes on mean embeddings and covariance operators (2020)
Gretton, A · 2020
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Meta-learning initializations for low-resource drug discovery
Nguyen, C. Q., Kreatsoulas, C. & Branson, K. M · 2020
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Making graph neural networks worth it for low-data molecular machine learning
Pappu, A. & Paige, B · 2020
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Improved protein–ligand binding affinity prediction with structure-based deep fusion inference
Jones, D. et al · 2021
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Volkov, M. et al · 2022
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Multi-task proteochemometric modelling
Pentina, A. & Clevert, D.-A · 2022
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MetaDTA: Meta-learning-based drug-target binding affinity prediction
Lee, E., Yoo, J., Lee, H. & Hong, S · 2022
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Conditional neural processes for molecules
Garcia-Ortegon, M., Bender, A. & Bacallado, S · 2022
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Multi-task processes
Kim, D., Cho, S., Lee, W. & Hong, S · 2022
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LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions
Daga, A. et al · 2023
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Multi-task bioassay pre-training for protein-ligand binding affinity prediction (2023)
Yan, J. et al · 2023
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Hac-net: A hybrid attention-based convolutional neural network for highly accurate protein–ligand binding affinity prediction
Kyro, G. W., Brent, R. I. & Batista, V. S · 2023
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