Open Access
| 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. [CrossRef] [PubMed] [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] [CrossRef] [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] [CrossRef] [PubMed] [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] [CrossRef] [PubMed] [Google Scholar]
- Pagadala NS, Syed K, Tuszynski J. Software for molecular docking: A review. Biophys Rev 2017; 9: 91-102. [Article] [CrossRef] [PubMed] [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] [CrossRef] [PubMed] [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] [CrossRef] [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] [CrossRef] [MathSciNet] [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] [CrossRef] [PubMed] [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] [CrossRef] [PubMed] [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] [CrossRef] [PubMed] [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] [CrossRef] [PubMed] [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. [CrossRef] [PubMed] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.