Insights into the EGFR SAR of N-phenylquinazolin-4-amine-derivatives using quantum mechanical pairwise-interaction energies

  • Saw Simeon
  • Nathjanan Jongkon
  • Warot Chotpatiwetchkul
  • M. Paul GleesonEmail author


Protein kinases are an important class of enzymes that play an essential role in virtually all major disease areas. In addition, they account for approximately 50% of the current targets pursued in drug discovery research. In this work, we explore the generation of structure-based quantum mechanical (QM) quantitative structure–activity relationship models (QSAR) as a means to facilitate structure-guided optimization of protein kinase inhibitors. We explore whether more accurate, interpretable QSAR models can be generated for a series of 76 N-phenylquinazolin-4-amine inhibitors of epidermal growth factor receptor (EGFR) kinase by comparing and contrasting them to other standard QSAR methodologies. The QM-based method involved molecular docking of inhibitors followed by their QM optimization within a ~ 300 atom cluster model of the EGFR active site at the M062X/6-31G(d,p) level. Pairwise computations of the interaction energies with each active site residue were performed. QSAR models were generated by splitting the datasets 75:25 into a training and test set followed by modelling using partial least squares (PLS). Additional QSAR models were generated using alignment dependent CoMFA and CoMSIA methods as well as alignment independent physicochemical, e-state indices and fingerprint descriptors. The structure-based QM-QSAR model displayed good performance on the training and test sets (r2 ~ 0.7) and was demonstrably more predictive than the QSAR models built using other methods. The descriptor coefficients from the QM-QSAR models allowed for a detailed rationalization of the active site SAR, which has implications for subsequent design iterations.


EGFR kinase Quinazoline Pairwise interactions Quantum mechanics 3D-QSAR 



M.P.G. would like to acknowledge the financial support provided by the Thailand Research Fund (RSA6180073) and King Mongkut’s Institute of Technology Ladkrabang. S.S. is grateful for financial support from the National Research University (NRU) for supporting his Ph.D. Studies. We would like to thank the Large Scale Research Laboratory of the National Electronics and Computer Technology (NECTEC) for SYBYLX2.0 software.

Supplementary material

10822_2019_221_MOESM1_ESM.docx (710 kb)
Supplementary Material 1 (DOCX 710 kb)
10822_2019_221_MOESM2_ESM.xlsx (156 kb)
Supplementary Material 2 (XLSX 156 kb)


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Authors and Affiliations

  1. 1.Interdisciplinary Graduate Program in Bioscience, Faculty of ScienceKasetsart UniversityBangkokThailand
  2. 2.Center for Advanced Studies in Nanotechnology for Chemical, Food and Agricultural Industries, KU Institute for Advanced StudiesKasetsart UniversityBangkokThailand
  3. 3.Department of Social and Applied Science, College of Industrial TechnologyKing Mongkut’s University of Technology North BangkokBangkokThailand
  4. 4.Department of Chemistry, Faculty of ScienceKing Mongkut’s Institute of Technology LadkrabangBangkokThailand
  5. 5.Department of Chemistry, Faculty of ScienceKasetsart UniversityBangkokThailand
  6. 6.Department of Biomedical Engineering, Faculty of EngineeringKing Mongkut’s Institute of Technology LadkrabangBangkokThailand

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