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Updated: Aug 10, 2025
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Three-Dimensional-QSAR and Relative Binding Affinity Estimation of Focal Adhesion Kinase Inhibitors
Suparna Ghosh1, Seung Joo Cho1,2
1Department of Biomedical Sciences, College of Medicine, Chosun University, Gwangju 501-759, Republic of Korea.
Abstract:
Precise binding affinity predictions are essential for structure-based drug discovery (SBDD). Focal adhesion kinase (FAK) is a member of the tyrosine kinase protein family and is overexpressed in a variety of human malignancies. Inhibition of FAK using small molecules is a promising therapeutic option for several types of cancer. Here, we conducted computational modeling of FAK-targeting inhibitors using three-dimensional structure-activity relationship (3D-QSAR), molecular dynamics (MD), and hybrid topology-based free energy perturbation (FEP) methods. The structure-activity relationship (SAR) studies between the physicochemical descriptors and inhibitory activities of the chemical compounds were performed with reasonable statistical accuracy using CoMFA and CoMSIA. These are two well-known 3D-QSAR methods based on the principle of supervised machine learning (ML). Essential information regarding residue-specific binding interactions was determined using MD and MM-PB/GBSA methods. Finally, physics-based relative binding free energy (ΔΔGRBFEA→B) terms of analogous ligands were estimated using alchemical FEP simulation. An acceptable agreement was observed between the experimental and computed relative binding free energies. Overall, the results suggested that using ML and physics-based hybrid approaches could be useful in synergy for the rational optimization of accessible lead compounds with similar scaffolds targeting the FAK receptor.
Insights
Computational modeling accurately predicts FAK inhibitors for cancer therapy. Combining machine learning (ML) and physics-based methods enhances drug discovery for focal adhesion kinase (FAK) targeting.
Area of Science:
- Computational chemistry
- Drug discovery
- Molecular modeling
Background:
- Precise binding affinity predictions are crucial for structure-based drug discovery (SBDD).
- Focal adhesion kinase (FAK) is a key target in oncology due to its overexpression in various cancers.
- FAK inhibition represents a promising therapeutic strategy for cancer treatment.
Purpose of the Study:
- To computationally model and predict the binding affinity of small molecule inhibitors targeting FAK.
- To explore the synergy between machine learning (ML) and physics-based approaches for optimizing FAK inhibitors.
Main Methods:
- Employed three-dimensional quantitative structure-activity relationship (3D-QSAR) methods (CoMFA, CoMSIA) utilizing supervised ML.
- Utilized molecular dynamics (MD) simulations and MM-PB/GBSA for analyzing binding interactions.
- Applied alchemical free energy perturbation (FEP) simulations to estimate relative binding free energies.
Main Results:
- 3D-QSAR studies demonstrated reasonable statistical accuracy in correlating physicochemical properties with inhibitory activities.
- MD simulations provided insights into residue-specific binding interactions within the FAK active site.
- Computed relative binding free energies showed acceptable agreement with experimental data.
Conclusions:
- Hybrid approaches combining ML (3D-QSAR) and physics-based simulations (MD, FEP) are effective for rational drug design.
- These integrated computational strategies can accelerate the optimization of lead compounds targeting FAK.
- The study highlights the potential of synergistic computational methods in structure-based drug discovery for oncology.
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