NextTopDocker: A Large-Scale Docking-Power Benchmark Reveals Limitations of Current End-to-End Machine-Learning Docking and the Strength of Hybrid Rescoring.
- DOI
- 10.1021/acs.jmedchem.6c01143
- Published
- 2026 Aug 27
- Container
- Journal of medicinal chemistry
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jmedchem.6c01143,
title = {NextTopDocker: A Large-Scale Docking-Power Benchmark Reveals Limitations of Current End-to-End Machine-Learning Docking and the Strength of Hybrid Rescoring.},
author = {Truong CM and Ballester PJ and Taboureau O and Tran-Nguyen VK},
year = {2026},
journal = {Journal of medicinal chemistry},
doi = {10.1021/acs.jmedchem.6c01143},
url = {https://doi.org/10.1021/acs.jmedchem.6c01143}
}RIS
TY - JOUR TI - NextTopDocker: A Large-Scale Docking-Power Benchmark Reveals Limitations of Current End-to-End Machine-Learning Docking and the Strength of Hybrid Rescoring. AU - Truong CM AU - Ballester PJ AU - Taboureau O AU - Tran-Nguyen VK PY - 2026 JO - Journal of medicinal chemistry DO - 10.1021/acs.jmedchem.6c01143 UR - https://doi.org/10.1021/acs.jmedchem.6c01143 ER -
APA
CM, T., PJ, B., O, T., & VK, T. (2026). NextTopDocker: A Large-Scale Docking-Power Benchmark Reveals Limitations of Current End-to-End Machine-Learning Docking and the Strength of Hybrid Rescoring.. Journal of medicinal chemistry. https://doi.org/10.1021/acs.jmedchem.6c01143
Source records
- pubmed · retrieved 2026-09-26T06:37:22.006Z