Mixtures of QSAR models: Learning application domains of p K predicto rs-Reference-Cited by-同舟云学术

Mixtures of QSAR models: Learning application domains of p K predicto rs

Published:2020-04 Issue:4 Volume:34 Page:
ISSN:0886-9383
Container-title:Journal of Chemometrics
language:en
Short-container-title:Journal of Chemometrics

Author:

Dörgő Gyula¹²^ORCID,Péter Hamadi Omar¹²^ORCID,Varga Tamás¹²^ORCID,Abonyi János¹²^ORCID

Affiliation:

1. MTA – PE Lendület Complex Systems Monitoring Research GroupPannon Egyetem Veszprém Hungary

2. Department of Process EngineeringUniversity of Pannonia Veszprém Hungary

Publisher

Wiley

Subject

Applied Mathematics,Analytical Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cem.3223

Reference42 articles.

1. Towards the Revival of Interpretable QSAR Models

2. Design, synthesis, activity evaluation and QSAR studies of novel antimalarial 1,2,3-triazolo-β-lactam derivatives

3. Interpretation of ANN-based QSAR models for prediction of antioxidant activity of flavonoids

4. QSAR study on anti‐HIV‐1 activity of 4‐oxo‐1, 4‐dihydroquinoline and 4‐oxo‐4h‐pyrido [1, 2‐a] pyrimidine derivatives using SW‐MLR, artificial neural network and filtering methods;Hajimahdi Z;Iranian J Pharmaceut Res IJPR,2015

5. QSAR models for prediction of chromatographic behavior of homologous Fab variants

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