The DOI matches its Crossref record; 152 of the 152 references it cites were found in a registry or an index.
Molecular dynamics simulations and novel drug discovery
Abstract
INTRODUCTION: Molecular dynamics (MD) simulations can provide not only plentiful dynamical structural information on biomacromolecules but also a wealth of energetic information about protein and ligand interactions. Such information is very important to understanding the structure-function relationship of the target and the essence of protein-ligand interactions and to guiding the drug discovery and design process. Thus, MD simulations have been applied widely and successfully in each step of modern drug discovery. Areas covered: In this review, the authors review the applications of MD simulations in novel drug discovery, including the pathogenic mechanisms of amyloidosis diseases, virtual screening and the interaction mechanisms between drugs and targets. Expert opinion: MD simulations have been used widely in investigating the pathogenic mechanisms of diseases caused by protein misfolding, in virtual screening, and in investigating drug resistance mechanisms caused by mutations of the target. These issues are very difficult to solve by experimental methods alone. Thus, in the future, MD simulations will have wider application with the further improvement of computational capacity and the development of better sampling methods and more accurate force fields together with more efficient analysis methods.
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Cited by
Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development
Salo-Ahen, O. M. H., Alanko, I., Bhadane, R., Bonvin, A. M. J. J., Honorato, R. V., Hossain, S., Juffer, A. H., Kabedev, A., Lahtela-Kakkonen, M., Larsen, A. S., et al. (2020) · Processes · doi:10.3390/pr9010071 · Cited by 659
Making it Rain: Cloud-Based Molecular Simulations for Everyone
Arantes, P. R., Polêto, M. D., Pedebos, C., & Ligabue-Braun, R. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00998 · Cited by 148
Breaking the barriers of data scarcity in drug–target affinity prediction
Pei, Q., Wu, L., Zhu, J., Xia, Y., Xie, S., Qin, T., Liu, H., Liu, T.-Y., & Yan, R. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad386 · Cited by 36
Self-citationKumar, P., Kumari, I., Prasad, R., Ray, S., Banerjee, A., & Prakash, A. (2025) · Molecular Diversity · doi:10.1007/s11030-024-11078-2 · Cited by 1
(2024) · doi:10.1371/journal.pone.0284642 · Cited by 1
Metadata
- Authors
- Liu, X., Shi, D., Zhou, S., Liu, H., Liu, H., & Yao, X.
- Venue
- Expert Opinion on Drug Discovery
- Publisher
- Informa UK Limited
- Published
- 2017-11-15
- Volume
- 13
- Issue
- 1
- Pages
- 23-37
- ISSN
- 1746-0441 (ISSN-L)
- 1746-045X
- OpenAlex
- W2768702618
- Cited by
- 518
- References
- 152
- FWCI
- 13.76
- Type
- journal article
- Language
- en
- Open access
- closed
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