TRIZ-guided computational innovation framework for the automated generation and prioritization of novel azole-like antifungal candidates targeting Candida auris CYP51.
- DOI
- 10.1007/s10822-026-00933-z
- Published
- 2026 Sep 1
- Container
- Journal of computer-aided molecular design
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s10822-026-00933-z,
title = {TRIZ-guided computational innovation framework for the automated generation and prioritization of novel azole-like antifungal candidates targeting Candida auris CYP51.},
author = {Khaled JM and Alharbi NS},
year = {2026},
journal = {Journal of computer-aided molecular design},
doi = {10.1007/s10822-026-00933-z},
url = {https://doi.org/10.1007/s10822-026-00933-z}
}RIS
TY - JOUR TI - TRIZ-guided computational innovation framework for the automated generation and prioritization of novel azole-like antifungal candidates targeting Candida auris CYP51. AU - Khaled JM AU - Alharbi NS PY - 2026 JO - Journal of computer-aided molecular design DO - 10.1007/s10822-026-00933-z UR - https://doi.org/10.1007/s10822-026-00933-z ER -
APA
JM, K., & NS, A. (2026). TRIZ-guided computational innovation framework for the automated generation and prioritization of novel azole-like antifungal candidates targeting Candida auris CYP51.. Journal of computer-aided molecular design. https://doi.org/10.1007/s10822-026-00933-z
Source records
- pubmed · retrieved 2026-09-26T01:34:24.885Z