In Silico Molecular Modeling and Docking in Drug Development

In silico molecular modeling and docking play a crucial role in modern drug discovery and development by enabling the prediction and analysis of molecular interactions between drug candidates and their biological targets. This computational drug design course covers both the theoretical foundations and practical applications of these techniques, focusing on molecular dynamics simulations, binding site identification, virtual screening, and structure-based drug design (SBDD).

Participants will learn to use various molecular docking software and biological databases to perform ligand-receptor docking studies, structure validation, and binding affinity estimation. Emphasis is placed on using tools like PyRx, ArgusLab, and ChemSketch to simulate real-world drug discovery pipelines.

Through hands-on sessions and projects, participants will gain experience in lead optimization, QSAR modeling, and energy minimization techniques—making them proficient in computational workflows commonly used in pharmaceutical R&D and biotechnology innovation.

The program aims to equip participants with comprehensive knowledge and practical experience in in silico drug design methods including ligand-based drug design (LBDD), homology modeling, and molecular interaction studies. It prepares learners to accelerate the hit-to-lead process and perform AI-driven drug candidate optimization using virtual screening strategies.

• Understand the principles of molecular modeling and docking.
• Master computational tools for drug discovery (e.g., PyRx, Modeller, ArgusLab).
• Perform binding site prediction, molecular docking, and virtual screening.
• Analyze and interpret docking results, pharmacophore modeling, and drug-likeness.
• Apply in silico techniques to real-world preclinical drug development projects.

• Students pursuing or holding degrees in Bioinformatics, Biotechnology, Pathology, Chemistry, Computer Science, or Biomedical Engineering.
• PhD Scholars and Researchers working in computational biology, digital pathology, AI in healthcare, or biomedical image analysis.
• Industry Professionals in pharma R&D, diagnostics, medical imaging, and bioinformatics.
• Aspiring learners interested in AI, machine learning, molecular docking, and drug design algorithms.

• Use computational drug discovery platforms for virtual screening and lead optimization
• Perform molecular docking and QSAR modeling
• Interpret binding energies, docking poses, and molecular descriptors
• Contribute to data-driven decision making in pharmaceutical research