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AI-Guided Pan-Cancer Target Discovery and Molecular Dynamics-Based Lead Optimization

Computational cancer drug discovery using docking, molecular dynamics, binding free energy, ADMET, and lead optimization.

Computational cancer drug discovery showing EGFR target protein, Gefitinib ligand docking, molecular dynamics RMSD graph, and MM/GBSA binding free energy analysis

Event Information

  • Location In Silico Research | AMRA Lab
  • Start Time 8:00 am May 13, 2025
  • Finish Time 5:00 pm December 1, 2026

PROJECT DESCRIPTION

Cancer remains one of the most challenging areas in modern drug discovery due to complex signaling pathways, mutation-driven resistance, and target-specific selectivity issues. This research project applies computational chemistry, structure-based drug design, and molecular simulation to identify and optimize potential inhibitors against cancer-related protein targets.

PROJECT CONTENT

  • AI-assisted target and ligand prioritization
  • Structure-based virtual screening and molecular docking
  • Protein–ligand molecular dynamics simulation
  • RMSD, RMSF, Rg, SASA and hydrogen bond analysis
  • MM/GBSA or MM/PBSA binding free energy calculation
  • ADMET, drug-likeness and toxicity profiling
  • Lead optimization using interaction and stability analysis