Search for Zinc Complexes with High Affinity in Pyrazinamidase from Mycobacterium Tuberculosis Resistant to Pyrazinamide-Reference-Cited by-同舟云学术

Search for Zinc Complexes with High Affinity in Pyrazinamidase from Mycobacterium Tuberculosis Resistant to Pyrazinamide

Published:2022 Issue: Volume: Page:109-120
ISSN:0302-9743
Container-title:Advances in Bioinformatics and Computational Biology
language:
Short-container-title:

Author:

Alvarado-Huayhuaz Jesus Antonio^ORCID,Talaverano-Rojas Daniel Alonso^ORCID,Quispe Reneé Isabel Huamán^ORCID,Balboni Maurício Dorneles Caldeira^ORCID,Arce Oscar Emilio Arrúa^ORCID,Werhli Adriano Velasque^ORCID,Machado Karina dos Santos^ORCID,Valderrama-Negrón Ana Cecilia^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-21175-1_12

Reference34 articles.

1. Adeniyi, A.A., Ajibade, P.A.: Comparing the suitability of autodock, gold and glide for the docking and predicting the possible targets of RU (II)-based complexes as anticancer agents. Molecules 18(4), 3760–3778 (2013)

2. Ain, Q.U., et al.: Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip. Rev. Comput. Mol. Sci. 5(6), 405–424 (2015)

3. Arce, O.E.A.: Função de escore baseada em machine learning para docagem molecular proteína-ligante. Master’s thesis (2020)

4. Balcells, D., Skjelstad, B.B.: tmQM dataset-quantum geometries and properties of 86k transition metal complexes. J. Chem. Inf. Model. 60(12), 6135–6146 (2020)

5. Bannwarth, C., et al.: GFN2-xTB-an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J. Chem. Theory Comput. 15(3), 1652–1671 (2019)