Structural Chemistry

, Volume 30, Issue 6, pp 2093–2107 | Cite as

3D-QSAR (CoMFA, CoMSIA, HQSAR and topomer CoMFA), MD simulations and molecular docking studies on purinylpyridine derivatives as B-Raf inhibitors for the treatment of melanoma cancer

  • Jaydeepsinh Chavda
  • Hardik BhattEmail author
Original Research


As per the World Health Organization (WHO), cancer is the second most leading cause of death after cardiovascular diseases in worldwide with around 9.88 million total new cases and 1.08 million were observed due to skin cancer in 2018. Amongst two types of skin cancer, progression of melanoma cancer is increasing day by day due to the environmental changes than non-melanoma cancer. Most of B-Raf mutation, specifically B-RafV600E, is responsible for the progression of the melanoma cancer. Here, various 3D-QSAR techniques like comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), molecular hologram QSAR (HQSAR) and topomer CoMFA were used to design novel B-Raf inhibitors by using 28 synthetic B-Raf inhibitors. Except for topomer CoMFA model, remaining models were generated by three different alignment methods in which distil-based alignment method was found best and gave prominent statistical values. After performing N-fold statistical validation, in CoMFA, q2, r2 and r2pred values were found to be 0.638, 0.969 and 0.848, respectively. Similarly, q2, r2 and r2pred values were found to be 0.796, 0.978 and 0.891 in CoMSIA (SHD) and 0.761, 0.973 and 0.852 in CoMSIA (SH) by N-fold statistical validation. In HQSAR analysis, statistical values were found for q2 as 0.984, r2 as 0.999 and r2pred as 0.634 with 97 as best hologram length (BHL). The results of topomer CoMFA showed the q2 value of 0.663 and the r2 value of 0.967. Important features of purinylpyridine were identified by contour map analysis of all 3D-QSAR techniques, which could be useful to design the novel molecules as B-Raf inhibitors for the treatment of melanoma cancer.


Melanoma B-Raf Purinylpyridine 3D-QSAR MD/MS 



Rapidly accelerated fibrosarcoma


Retrovirus-associated DNA sequences


Mitogen-activated protein kinase


Receptor tyrosine kinase


Extracellular regulated kinase


Three-dimensional quantitative structural activity relationship


Comparative molecular field analysis


Comparative molecular similarity indices analysis


Molecular hologram QSAR


Molecular docking and molecular simulation



The authors are thankful to Nirma University, Ahmedabad, India, for providing the needful facilities to carry out research work.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11224_2019_1334_MOESM1_ESM.docx (242 kb)
ESM 1 (DOCX 241 kb)


  1. 1.
  2. 2.
    List of rare diseases and synonyms (2018) Accessed 5 Oct 2018
  3. 3.
    Liu L, Xu J, Yang J, Feng C, Miao Y (2017). Bioorg Med Chem Lett 27:4952CrossRefGoogle Scholar
  4. 4.
    Neidle S (2013) Cancer drug design and discovery, 2nd edn. Acedamic Press, LondonGoogle Scholar
  5. 5.
    Rahman MA, Salajegheh A, Smith RA, Lam AKY (2013). Exp Mol Pathol 95:336CrossRefGoogle Scholar
  6. 6.
    Verma J, Khedkar VM, Coutinho EC (2010). Curr Top Med Chem 10:95CrossRefGoogle Scholar
  7. 7.
    Zhang S, Lin Z, Pu Y, Zhang Y, Zhang L, Zuo Z (2017). Comput Biol Chem 67:38CrossRefGoogle Scholar
  8. 8.
    Yu S, Yuan J, Shi J, Ruan X, Zhang T, Wang Y et al (2015). Chemom Intell Lab Syst 146:34CrossRefGoogle Scholar
  9. 9.
    Yang W, Chen Y, Zhou X, Gu Y, Qian W, Zhang F et al (2015). Eur J Med Chem 89:581CrossRefGoogle Scholar
  10. 10.
    Sybyl X (2011) Molecular modelling software. Tripos Certara, V.1.2, St. LouisGoogle Scholar
  11. 11.
    Borisa A, Bhatt H (2015). Eur J Pharm Sci 79:1CrossRefGoogle Scholar
  12. 12.
    Wang W, Tian Y, Wan Y, Gu S, Ju X, Luo X, Liu G (2018). Struct Chem 30:385–397CrossRefGoogle Scholar
  13. 13.
    Elham G, Mohammad H (2018). J Chin Chem Soc 65:1CrossRefGoogle Scholar
  14. 14.
    Jianbo T, Pei Z, Xiang S, Wang W (2017). J Chemom 31:2934CrossRefGoogle Scholar
  15. 15.
    Wold S, Ruhe A, Wold H, Dunn III WJ (1984). J Sci Stat Comput 5:735CrossRefGoogle Scholar
  16. 16.
    Zambre V, Murumkar P, Giridhar R, Yadav M (2010). J. Mol. Graph. Model 29:229CrossRefGoogle Scholar
  17. 17.
    Tanga H, Yanga L, Li J, Chen J (2016). J Taiwan Inst Chem Eng 68:64–73CrossRefGoogle Scholar
  18. 18.
    Clark M, Cramer RD., (1993), Quant. Struct. Relationships 12:137.Google Scholar
  19. 19.
    Patel P, Bhatt H (2016). Bioorg Med Chem Lett 28:2328Google Scholar
  20. 20.
    Golbraikh A, Tropsha A (2002). J Mol Graph Model 20:269CrossRefGoogle Scholar
  21. 21.
    Markus Böhm, Jörg Stürzebecher and, Gerhard Klebe (1999),. J Med Chem42:458.Google Scholar
  22. 22.
    Mohammed AA, Janarthanan T, Naga ST (2017). Struct Chem 28:1187–1200CrossRefGoogle Scholar
  23. 23.
    Chaube U, Bhatt H (2017). Mol Divers 21:741CrossRefGoogle Scholar
  24. 24.
    Chaube U, Chhatbar D, Bhatt H (2016). Bioorg Med Chem Lett 26:864CrossRefGoogle Scholar
  25. 25.
    Ballu S, Itteboina R, Sivan SK, Manga V (2017). Struct Chem 29:593–605CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Pharmaceutical Chemistry, Institute of PharmacyNirma UniversityAhmedabadIndia

Personalised recommendations