Virtual lead identification of farnesyltransferase inhibitors based on ligand and structure-based pharmacophore...

Area of Science:

• Medicinal Chemistry
• Computational Drug Design
• Oncology

Background:

• Farnesyltransferase enzyme (FTase) is vital in the Ras signaling pathway implicated in various cancers.
• Inhibiting FTase is a promising strategy for developing effective anticancer therapeutics.

Purpose of the Study:

• To generate and validate pharmacophore hypotheses for FTase inhibitors.
• To identify and refine potential drug candidates targeting FTase using computational methods.

Main Methods:

• Structure-based and ligand-based pharmacophore modeling using Discovery Studio v3.1.
• Inclusion of zinc-binding features in pharmacophore generation and validation via ROC analysis.
• Screening of 3D databases, followed by drug-likeness (Lipinski's Rule of Five) and ADMET filtering.
• Molecular docking of candidate compounds (ZINC39323901, ZINC01034774) using CDOCKER and GOLD.

Main Results:

• Validated pharmacophore models incorporating essential zinc-binding features were developed.
• Computational screening identified potential FTase inhibitor candidates.
• Two compounds, ZINC39323901 and ZINC01034774, were selected for further optimization based on binding affinity and drug-like properties.

Conclusions:

• The study successfully generated and validated pharmacophore models for FTase inhibition.
• Computational approaches identified promising lead compounds for anticancer drug development targeting FTase.
• The identified compounds warrant further investigation for their therapeutic potential in cancer treatment.