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Akhondi, S.A., Rey, H., Schwörer, M., Maier, M., Toomey, J., Nau, H., et al. (2019) Automatic Identification of Relevant Chemical Compounds from Patents. Database, 2019, Article No. baz001.
https://doi.org/10.1093/database/baz001

has been cited by the following article:

• TITLE: Biological Activity Relationship of Cyclic and Noncyclic Alkanes Using Quantum Molecular Descriptors

  AUTHORS: Ahmad Nazib Alias, Zubainun Mohamed Zabidi, Nurul Aimi Zakaria, Zaidatul Salwa Mahmud, Rosliza Ali

  KEYWORDS: 3D-QSAR, Quantum Molecular Descriptor, Biological Activity, Toxicology

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.11 No.8, August 31, 2021

  ABSTRACT: A 3-Dimension-Quantitative Structure-Activity Relationship (3D-QSAR1) approach is applied for the prediction of accurate chemical products made from biological activity and toxicity. Quantum chemical technique allows the construction of the molecular descriptors. The molecular quantum descriptors are classified into five principal component factors. Various linear regression equations are obtained using the statistical technique. In this study, the researchers propose the three best regression equations based on quantum molecular descriptors discussed earlier in this study. The observed EC50 vs calculated EC50 is plotted using the best fitting with the quantum descriptors.