Pretrained graph transformer-driven de novo design and machine learning prioritization of computationally predicted ENR-InhA (InhA) inhibitors with potential relevance to drug-resistant Mycobacterium tuberculosis.
- DOI
- 10.1080/1062936x.2026.2751659
- Published
- 2026 Oct 27
- Container
- SAR and QSAR in environmental research
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1080/1062936x.2026.2751659,
title = {Pretrained graph transformer-driven de novo design and machine learning prioritization of computationally predicted ENR-InhA (InhA) inhibitors with potential relevance to drug-resistant Mycobacterium tuberculosis.},
author = {Nisar H and Alwahaishi S and Khan A and Al-Ghafli H and Ur Rahman A and Agouni A},
year = {2026},
journal = {SAR and QSAR in environmental research},
doi = {10.1080/1062936x.2026.2751659},
url = {https://doi.org/10.1080/1062936x.2026.2751659}
}RIS
TY - JOUR TI - Pretrained graph transformer-driven de novo design and machine learning prioritization of computationally predicted ENR-InhA (InhA) inhibitors with potential relevance to drug-resistant Mycobacterium tuberculosis. AU - Nisar H AU - Alwahaishi S AU - Khan A AU - Al-Ghafli H AU - Ur Rahman A AU - Agouni A PY - 2026 JO - SAR and QSAR in environmental research DO - 10.1080/1062936x.2026.2751659 UR - https://doi.org/10.1080/1062936x.2026.2751659 ER -
APA
H, N., S, A., A, K., H, A., A, U. R., & A, A. (2026). Pretrained graph transformer-driven de novo design and machine learning prioritization of computationally predicted ENR-InhA (InhA) inhibitors with potential relevance to drug-resistant Mycobacterium tuberculosis.. SAR and QSAR in environmental research. https://doi.org/10.1080/1062936x.2026.2751659
Source records
- pubmed · retrieved 2026-11-10T18:16:26.701Z