Journals / Journal of Physical Chemistry and Functional Materials / 2026 / Volume: 9 Issue: 1

Computational Descriptor Profiling and QSAR-Based Toxicity Assessment of a Nitro-Amino Substituted Uracil Derivative

Mehmet Hanifi Kebiroglu(MALATYA TURGUT ÖZAL ÜNİVERSİTESİ, DARENDE BEKİR ILICAK MESLEK YÜKSEKOKULU, TIBBİ HİZMETLER VE TEKNİKLER BÖLÜMÜ)0000-0002-6764-3364
Pages
—
Publication Date
2026-06-29
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study provides the first integrated computational evaluation of ANDMU, offering quantitative insights into its structural organization and potential toxicological behavior.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:6-Amino-5-nitro-1,3-dimethyluracil (ANDMU) is a nitrogen- and oxygen-rich heterocyclic compound structurally analogous to uracil derivatives.
📊
Key Findings & Data:The molecule exhibits a heteroatom-dense architecture with a high Sanderson electronegativity (Se= 23.48; PaDEL-normalized carbon-scaled value) and pronounced electrotopological variation (Ss=48.67), reflecting strong intramolecular electronic polarization.
💡
Conclusion & Impact:This study provides the first integrated computational evaluation of ANDMU, offering quantitative insights into its structural organization and potential toxicological behavior.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:ANDMU · Toxicity Prediction · Computational Chemistry

Abstract

6-Amino-5-nitro-1,3-dimethyluracil (ANDMU) is a nitrogen- and oxygen-rich heterocyclic compound structurally analogous to uracil derivatives. Although such substituted pyrimidinone frameworks exhibit diverse physicochemical and biological behaviors, their computational characterization remains limited. In this study, a comprehensive descriptor-based profiling of ANDMU was performed to elucidate its constitutional, topological, electro-topological, and physicochemical features. The molecule exhibits a heteroatom-dense architecture with a high Sanderson electronegativity (Se= 23.48; PaDEL-normalized carbon-scaled value) and pronounced electrotopological variation (Ss=48.67), reflecting strong intramolecular electronic polarization. Toxicity estimates obtained using the U.S. EPA Toxicity Estimation Software Tool (T.E.S.T.) indicate a fathead minnow LC50 value consistent with moderate aquatic toxicity and a bioconcentration factor (log10 BCF) indicative of low bioaccumulation. Additional physicochemical predictions such as density (1.30 g/cm³) and vapor pressure (log10 mmHg = -0.85) support a low-volatility profile. StopTox consensus classifiers suggest moderate acute oral toxicity, a high likelihood of eye irritation (80% confidence), and low inhalation and dermal toxicity. Applicability domain assessments confirm that most predictions lie within reliable chemical space. This study provides the first integrated computational evaluation of ANDMU, offering quantitative insights into its structural organization and potential toxicological behavior.

🔖

Cite This Article

Kebiroglu, M. H., Yılmaz, M. (2026). Computational Descriptor Profiling and QSAR-Based Toxicity Assessment of a Nitro-Amino Substituted Uracil Derivative. Journal of Physical Chemistry and Functional Materials, Volume: 9 Issue: 1. https://doi.org/10.54565/jphcfum.1832379

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (27)

  1. Kaur, R., Roychowdhury, T., Kinarivala, N., Kaur, K., Sanduja, M., & Sharma, S. (2025). Recent Expansions in the Potential of Uracil Derivatives as Chemotherapeutic, Antimicrobial, and Antiviral Agents: A Review. ChemistrySelect, 10(10), e202406021.
  2. Du, X., Lai, Q., Zhang, S., Yin, P., Xia, Y., & Pang, S. (2025). Skeletal editing of pyrimidines: new approaches to diverse high energy density materials. Materials Chemistry Frontiers, 9(6), 894-909.
  3. Hssaın, A., & Kebiroglu, M. H. Effect of Metal Doping on the Electronic and Optical Properties of Norepinephrine: A DFT Study on Fe, Mg, and Zn Modifications for Enhanced Bioactivity. Journal of Physical Chemistry and Functional Materials, 8(1), 1-14.
  4. Jain, K. S., Chitre, T. S., Miniyar, P. B., Kathiravan, M. K., Bendre, V. S., Veer, V. S.,. & Jeankumar, V. U. (2006). Biological and medicinal significance of pyrimidines. Current Science, 90(6), 793-803.
  5. He, J., Guo, H., & Zou, M. (2025). Research advances in nanomaterials with aromatic functional groups for the treatment of inflammatory bowel disease. Frontiers in Bioengineering and Biotechnology, 13, 1614939.
  6. Kayacık Bi̇li̇r, S., Çankaya, N., & Kebi̇roğlu, M. H. (2025). Synthesis, Characterization and Molecular Modeling of Novel Oxoethyl methacrylate Polymers. ACS omega, 10(37), 42913-42932.
  7. Al-Jubouri, A. A., et al. (2021). Theoretical investigation of electronic structure and spectroscopic properties of 5-nitrouracil. Journal of Molecular Structure, 1225, 129255.
  8. Yılmaz, M. (2025). Comprehensive DFT study of 3-(2-furyl)-1 H-pyrazole-5-carboxylic acid: structural, spectroscopic, and electronic properties with optoelectronic implications. Scientific Reports, 15(1), 21983.
  9. Valerio Jr, L. G. (2009). In silico toxicology for the pharmaceutical sciences. Toxicology and Applied Pharmacology, 241(3), 356-370.
  10. Waghmode, M. S., Sahoo, D. K., Patil, N. N., Abhyankar, P. S., Chirra, S., & Sohajda, T. (2025). Unveiling the environmental threat of acetaminophen: ecotoxicology, microbial remediation, and molecular modelling insights. Environmental Monitoring and Assessment, 197(12), 1326.
  11. Çankaya, N., Kebiroğlu, M. H., & Azarkan, S. Y. (2025). Comprehensive assessment of 2-(3-methoxyphenylamino)-2-oxoethyl methacrylate: spectroscopic, computational, toxicological, molecular Docking and dynamic studies with STAT3 protein. Brazilian Journal of Physics, 55(6), 269.
  12. U.S. EPA. (2016). User’s Guide for T.E.S.T. (Version 4.1) (Toxicity Estimation Software Tool): A Program to Estimate Toxicity from Molecular Structure. Washington, DC: U.S. Environmental Protection Agency.
  13. Schultz, L. E., Wang, Y., Jacobs, R., & Morgan, D. (2025). A general approach for determining applicability domain of machine learning models. npj Computational Materials, 11(1), 95.
  14. Tudoran, C., et al. (2021). QSAR modeling and molecular docking studies of some heterocyclic compounds. Journal of Molecular Structure, 1225, 129141.
  15. Çankaya, N., Kebiroğlu, M. H., & Temüz, M. M. (2024). A comprehensive study of experimental and theoretical characterization and in silico toxicity analysis of new molecules. Drug and Chemical Toxicology, 47(6), 1226-1240.
  16. Fernandes, A. D. O., Paixão, V. V. M., Santos, Y. J. A., Alves, E. B., Rodrigues, R. P., Chagas-Paula, D. A.,. & Oliveira, T. B. (2025). Identification of Pharmacophore Groups with Antimalarial Potential in Flavonoids by QSAR-Based Virtual Screening. Drugs and Drug Candidates, 4(3), 33.
  17. Malik, J., Karande, G., Murugesan, S., & Sekhar, K. V. G. C. (2025). Recent advances in triazole hybrid molecules for the therapeutic management of neglected tropical diseases. RSC Medicinal Chemistry, 16(11), 5292-5317.
  18. Stoyanova‐Slavova, I. B., Slavov, S. H., Pearce, B., Buzatu, D. A., Beger, R. D., & Wilkes, J. G. (2014). Partial least square and k‐nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity. Environmental toxicology and chemistry, 33(6), 1271-1282.
  19. Kotzé, A., Lawton, T., Howell, S. J., O'Driscoll, R., Odling‐Smee, M., Shangguan, L.,. & Wong, D. C. (2025). Development, external validation and integration into clinical workflow of machine learning models to support pre‐operative assessment in the UK. Anaesthesia.
  20. Ak, F., & Kebiroglu, M. H. (2024). Theoretical investigation of the chemical reactivity of acrylic acid molecules: a DFT study with UV-Vis, NMR, and FT-IR spectroscopy using STO-3G basis set. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 29(2), 438-446.
  21. Elallaf, A., Jacobs, N., Ye, X., Chen, M., & Liang, G. (2025). Beta Distribution Learning for Reliable Roadway Crash Risk Assessment. arXiv preprint arXiv:2511.04886.
  22. Verma, R. P., & Pastor, M. (2019). QSAR models for predicting eye irritation and corrosion: a review. Journal of Applied Toxicology, 39(8), 1104-1118.
  23. Kebiroglu, H., & Yılmaz, M. (2023). Investigation of UV-Visible absorption quantum effects doped of norepinephrine, Mg+ 2 atom by using DFT method. Journal of Physical Chemistry and Functional Materials, 6(2), 145-151.
  24. El-Geneedy, M., El-Din Moustafa, H., Khater, H., Abd-Elsamee, S., & Gamel, S. A. (2025). A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction. Scientific Reports, 15(1), 26048
  25. Jain, K. S., Chitre, T. S., Miniyar, P. B., Kathiravan, M. K., Bendre, V. S., Veer, V. S.,. & Jeankumar, V. U. (2006). Biological and medicinal significance of pyrimidines. Current Science, 90(6), 793-803.
  26. Fernandes, A. D. O., Paixão, V. V. M., Santos, Y. J. A., Alves, E. B., Rodrigues, R. P., Chagas-Paula, D. A.,. & Oliveira, T. B. (2025). Identification of Pharmacophore Groups with Antimalarial Potential in Flavonoids by QSAR-Based Virtual Screening. Drugs and Drug Candidates, 4(3), 33.
  27. Kotzé, A., Lawton, T., Howell, S. J., O'Driscoll, R., Odling‐Smee, M., Shangguan, L.,. & Wong, D. C. (2025). Development, external validation and integration into clinical workflow of machine learning models to support pre‐operative assessment in the UK. Anaesthesia.