In silico design of novel FAK inhibitors using integrated molecular docking, 3D-QSAR and molecular dynamics

Pouria Shirvani1, Afshin Fassihi1,2

  • 1Department of Medicinal Chemistry, Faculty of Pharmacy and Pharmaceutical Science, Isfahan University of Medical Science, Isfahan, Iran.

Insights

This study used molecular modeling to design novel focal adhesion kinase (FAK) inhibitors. Computational analysis revealed key structural features for improved FAK inhibition, guiding the development of potentially more effective cancer therapeutics.

Area of Science:

  • Medicinal Chemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Focal adhesion kinase (FAK) is a key regulator of cellular functions and is often overexpressed in tumors.
  • FAK is a significant therapeutic target for developing selective inhibitors for cancer treatment.

Purpose of the Study:

  • To gain structural insights into FAK inhibitory activity.
  • To design novel 7H-pyrrolo[2,3-d]pyrimidine and thieno[3,2-d]pyrimidine based FAK inhibitors.

Main Methods:

  • Employed molecular docking, 3D-QSAR (CoMFA, CoMSIA), and molecular dynamics (MD) simulations.
  • Utilized ligand-based, docking-based, and receptor-based alignment techniques for 3D-QSAR model development.
  • Evaluated stability and binding free energies of novel inhibitors using MD simulations and MM-PBSA.

Main Results:

  • Receptor-based alignment yielded superior CoMFA and CoMSIA models, with CoMSIA showing higher predictive ability (q²=0.679, r²=0.954, r²pred=0.888).
  • Contour map analysis identified crucial structural features for FAK inhibition, consistent with docking results.
  • Designed novel inhibitors demonstrated enhanced predicted activity compared to the reference compound.
  • MD simulations and MM-PBSA confirmed the stability of designed compounds and highlighted the importance of van der Waals and H-bond interactions.

Conclusions:

  • The study provides significant structural insights for the development of more effective FAK inhibitors.
  • The combined computational approach successfully guided the design of novel, potent FAK inhibitors.

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