Integrating traditional QSAR and read-across-based regression models for predicting potential anti-leishmanial azole compounds

2024/12/09 by Rajat Nandi, Anupama Sharma, A.K. Priya +2 · 1 voice
Computer Science · Mathematics · Medicine · #Artificial intelligence #Biology #Boosting (machine learning) #Computational Drug Discovery Methods #Computational biology #Computer science #Docking (animal) #Linear regression #Machine learning #Mathematics #Medicine #Quantitative structure–activity relationship #Regression #Research on Leishmaniasis Studies #Statistics

paper · doi:10.1007/s11030-024-11070-w

openalex publication_date 2024/12/09 · crossref created 2024/12/09 · crossref issued 2024/12/10 · crossref published 2024/12/10 · crossref published-online 2024/12/10 · crossref published-print 2025/08/01 · crossref deposited 2025/09/06 · openalex created_date 2025/10/10 · crossref indexed 2026/08/01 · openalex updated_date 2026/08/05

Citations

• Basic local alignment search tool
• Computer-aided drug design platform using PyMOL
• AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
• QSAR without borders
• ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties
• First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability
• Multiwfn: A multifunctional wavefunction analyzer
• Open Babel: An open chemical toolbox

Discussions

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