Issue
Natl Sci Open
Volume 3, Number 2, 2024
Special Topic: AI for Chemistry
Article Number 20230058
Number of page(s) 10
Section Chemistry
DOI https://doi.org/10.1360/nso/20230058
Published online 08 March 2024
  • van Montfort RLM, Workman P. Structure-based drug design: Aiming for a perfect fit. Essays in biochemistry 2017; 61: 431-437. [Google Scholar]
  • Wang X, Song K, Li L, et al. Structure-based drug design strategies and challenges. Current Top Med Chem 2018; 18: 998-1006. [Article] [Google Scholar]
  • Eberhardt J, Santos-Martins D, Tillack AF, et al. AutoDock vina 1.2.0: New docking methods, expanded force field, and python bindings. J Chem Inf Model 2021; 61: 3891-3898. [Article] [Google Scholar]
  • Yu Y, Cai C, Wang J, et al. Uni-Dock: GPU-accelerated docking enables ultralarge virtual screening. J Chem Theor Comput 2023; 19: 3336-3345. [Article] [Google Scholar]
  • Pagadala NS, Syed K, Tuszynski J. Software for molecular docking: A review. Biophys Rev 2017; 9: 91-102. [Article] [Google Scholar]
  • Wang Z, Sun H, Yao X, et al. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: The prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys 2016; 18: 12964-12975. [Article] [Google Scholar]
  • McNutt AT, Francoeur P, Aggarwal R, et al. GNINA 1.0: Molecular docking with deep learning. J Cheminform 2021; 13: 43. [Article] [Google Scholar]
  • Shen C, Zhang X, Deng Y, et al. Boosting protein-ligand binding pose prediction and virtual screening based on residue-atom distance likelihood potential and graph transformer. J Med Chem 2022; 65: 10691-10706. [Article] [Google Scholar]
  • Shen C, Ding J, Wang Z, et al. From machine learning to deep learning: Advances in scoring functions for protein-ligand docking. REs Comput Mol Sci 2020; 10: e1429. [Article] [Google Scholar]
  • Bai Q, Liu S, Tian Y, et al. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. REs Comput Mol Sci 2022; 12: e1581. [Article] [Google Scholar]
  • Gentile F, Agrawal V, Hsing M, et al. Deep docking: A deep learning platform for augmentation of structure based drug discovery. ACS Cent Sci 2020; 6: 939-949. [Article] [Google Scholar]
  • Zhang X, Zhang O, Shen C, et al. Efficient and accurate large library ligand docking with KarmaDock. Nat Comput Sci 2023; 3: 789-804. [Article] [Google Scholar]
  • Zhou G, Gao Z, Ding Q, et al. Uni-Mol: A universal 3D molecular representation learning framework. In: Proceedings of the Eleventh International Conference on Learning Representations. Kigali, 2023. [Google Scholar]
  • Buttenschoen M, Morris GM, Deane CM. PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chem Sci 2024; 15: 3130-3139. [Article] [Google Scholar]
  • Hartshorn MJ, Verdonk ML, Chessari G, et al. Diverse, high-quality test set for the validation of protein-ligand docking performance. J Med Chem 2007; 50: 726-741. [Article] [Google Scholar]
  • Su M, Yang Q, Du Y, et al. Comparative assessment of scoring functions: The CASF-2016 update. J Chem Inf Model 2019; 59: 895-913. [Article] [Google Scholar]
  • Burley SK, Berman HM, Kleywegt GJ, et al. Protein data bank (PDB): The single global macromolecular structure archive. Methods Mol Biol 2017; 1607: 627-641. [Google Scholar]

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