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In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score.
Empirical scoring functions: I. the development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes
Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V., and Mee, R. P · 1997
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Further development and validation of empirical scoring functions for structure-based binding affinity prediction
Wang, R., Lai, L., and Wang, S · 2002
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Improved protein–ligand docking using gold
Verdonk, M. L., Cole, J. C., Hartshorn, M. J., Murray, C. W., and Taylor, R. D · 2003
Earlier work this paper cites.
Glide: a new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy
Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al · 2004
Earlier work this paper cites.
Docking and scoring in virtual screening for drug discovery: methods and applications
Kitchen, D. B., Decornez, H., Furr, J. R., and Bajorath, J · 2004
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Rapid context-dependent ligand desolvation in molecular docking
Mysinger, M. M. and Shoichet, B. 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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Molecular docking and structure-based drug design strategies
Ferreira, L. G., Dos Santos, R. N., Oliva, G., and Andricopulo, A. D · 2015
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Phase ii and phase iii failures: 2013–2015
Harrison, R. K · 2016
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Vinardo: A scoring function based on autodock vina improves scoring, docking, and virtual screening
Quiroga, R. and Villarreal, M. A · 2016
Earlier work this paper cites.
De novo design at the edge of chaos: Miniperspective
Schneider, P. and Schneider, G · 2016
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Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials. sci. transl. med. 11: eaaw8412, 2019
Lin, A., Giuliano, C., Palladino, A., John, K., Abramowicz, C., Yuan, M., Sausville, E., Lukow, D., Liu, L., Chait, A., et al · 2019
Cited alongside, same era.
Estimation of clinical trial success rates and related parameters
Wong, C. H., Siah, K. W., and Lo, A. W · 2019
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.
Generating 3d molecular structures conditional on a receptor binding site with deep generative models, 2020
Masuda, T., Ragoza, M., and Koes, D. R · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Zero-shot 3d drug design by sketching and generating
Long, S., Zhou, Y., Dai, X., and Zhou, H · 2022
Later among the works it cites.
Pocket2mol: Efficient molecular sampling based on 3d protein pockets
Peng, X., Luo, S., Guan, J., Xie, Q., Peng, J., and Ma, J · 2022
Later among the works it cites.
Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations
Zhao, M., Bao, F., Li, C., and Zhu, J · 2022
Later among the works it cites.
Improving protein–ligand docking and screening accuracies by incorporating a scoring function correction term
Zheng, L., Meng, J., Jiang, K., Lan, H., Wang, Z., Lin, M., Li, W., Guo, H., Wei, Y., and Mu, Y · 2022
Later among the works it cites.
DrugCLIP: Contrasive protein-molecule representation learning for virtual screening
Gao, B., Qiang, B., Tan, H., Jia, Y., Ren, M., Lu, M., Liu, J., Ma, W.-Y., and Lan, Y · 2023
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A 3d generative model for structure-based drug design
Luo, S., Guan, J., Ma, J., and Peng, J · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
Cited alongside, same era.
E (n) equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
Cited alongside, same era.
Equivariant energy-guided sde for inverse molecular design
Bao, F., Zhao, M., Hao, Z., Li, P., Li, C., and Zhu, J · 2022
Cited alongside, same era.
e3nn: Euclidean neural networks, 2022
Geiger, M. and Smidt, T · 2022
Cited alongside, same era.
Equivariant diffusion for molecule generation in 3d
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M · 2022
Cited alongside, same era.
3d equivariant diffusion for target-aware molecule generation and affinity prediction
Guan, J., Qian, W. W., Peng, X., Su, Y., Peng, J., and Ma, J
Cited in the paper.
Harris, C., Didi, K., Jamasb, A. R., Joshi, C. K., Mathis, S. V., Lio, P., and Blundell, T · 2023
Later among the works it cites.
Modeling the expansion of virtual screening libraries
Lyu, J., Irwin, J. J., and Shoichet, B. K · 2023
Later among the works it cites.
Planet: A multi-objective graph neural network model for protein–ligand binding affinity prediction
Zhang, X., Gao, H., Wang, H., Chen, Z., Zhang, Z., Chen, X., Li, Y., Qi, Y., and Wang, R · 2023
Later among the works it cites.
Learning subpocket prototypes for generalizable structure-based drug design
Zhang, Z. and Liu, Q · 2023
Later among the works it cites.
Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences
Buttenschoen, M., Morris, G. M., and Deane, C. M · 2024
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