Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors.
- DOI
- 10.1080/07391102.2021.1905559
- Published
- 2022 Oct
- Container
- Journal of biomolecular structure & dynamics
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1080/07391102.2021.1905559,
title = {Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors.},
author = {Andrianov AM and Nikolaev GI and Shuldov NA and Bosko IP and Anischenko AI and Tuzikov AV},
year = {2022},
journal = {Journal of biomolecular structure \& dynamics},
doi = {10.1080/07391102.2021.1905559},
url = {https://doi.org/10.1080/07391102.2021.1905559}
}RIS
TY - JOUR TI - Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors. AU - Andrianov AM AU - Nikolaev GI AU - Shuldov NA AU - Bosko IP AU - Anischenko AI AU - Tuzikov AV PY - 2022 JO - Journal of biomolecular structure & dynamics DO - 10.1080/07391102.2021.1905559 UR - https://doi.org/10.1080/07391102.2021.1905559 ER -
APA
AM, A., GI, N., NA, S., IP, B., AI, A., & AV, T. (2022). Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors.. Journal of biomolecular structure & dynamics. https://doi.org/10.1080/07391102.2021.1905559
Source records
- pubmed · retrieved 2026-09-26T12:56:22.664Z