Computer Aided Drug Design (Practical)

List of Experiments / Activities

1. Computer Aided Drug Design (Practical) Elective Credit Hours Per Week (L-T-P) Max. Hours L T P
2. 1-- --
3. 30Maximum Marks SE ESE
4. General Instruction on Computational Tools Open-source computational tools are preferred for teaching and learning purposes. However, institutions may select appropriate software platforms or online resources based on availability, institutional policy, and technical feasibility. The choice of software should enable students to perform the required computational tasks effectively. The specific software used may vary, but the learning outcomes and computational procedures described below must be achieved using suitable tools.
5. 1Target Identification and Preparation Retrieve protein sequence or structural information from publicly available biological databases. Prepare the target protein by removing unwanted molecules, correcting structural issues, and adding necessary atoms or charges using appropriate molecular visualization or preparation tools. Identify potential binding or active sites using computational methods that analyze structural pockets and cavities.
6. 2Ligand Design and Optimization Design or sketch candidate molecules using suitable molecular drawing tools. Convert structures into three-dimensional form and optimize their geometry using computational chemistry methods. Generate multiple conformations of the molecule and perform energy minimization to obtain stable structures.
7. 3Molecular Docking Perform docking simulations using computational docking platforms to predict the interaction between ligands and the target protein. Examine binding orientations and molecular interactions using visualization tools. Rank compounds based on predicted binding affinity and interaction quality.
8. 4Pharmacophore Modeling Identify essential molecular features responsible for biological activity such as hydrogen bond donors/acceptors, hydrophobic regions, and aromatic centers. Construct pharmacophore models representing these key features. Use these models to screen compound libraries for molecules that match the required feature pattern.
9. 5QSAR Modeling Calculate molecular descriptors that represent structural and physicochemical properties of compounds. Develop predictive statistical or machine learning models that relate these descriptors to biological activity. Validate models using appropriate validation techniques such as cross-validation and external datasets.
10. 6ADMET Prediction Evaluate pharmacokinetic and toxicity properties computationally, including: o Absorption o Distribution o Metabolism o Excretion o Toxicity Use predictive computational models and online resources to assess drug-likeness and safety profiles.
11. 7Molecular Dynamics Simulations Conduct simulations to study the dynamic behavior of the protein ligand complex over time. Analyze parameters such as structural stability, atomic fluctuations, hydrogen bonding, and interaction persistence. Visualize simulation trajectories to understand conformational changes and binding stability.