Aim
In Silico Molecular Modeling and Docking in Drug Development introduces computational tools for drug discovery. Learn molecular modeling, docking techniques, ligand-receptor interactions, structure-based drug design, and virtual screening for identifying lead compounds, optimizing drug candidates, and predicting pharmacokinetics and toxicity.
Program Objectives
- Computational Basics: Introduction to molecular modeling, energy minimization, and force fields.
- Docking Concepts: Ligand-receptor binding, scoring functions, and docking algorithms.
- Virtual Screening: High-throughput screening and database searching techniques.
- Structure-Based Drug Design: Target identification, lead optimization, and affinity enhancement.
- Binding Affinity: Binding energy calculations, SAR (Structure-Activity Relationship).
- Pharmacokinetics and Toxicity: Predicting ADME (Absorption, Distribution, Metabolism, Excretion) and toxicity.
- Visualization: 3D visualization of docking results and molecular interactions.
- Capstone: Design and dock a small molecule to a biological target.
Program Structure
Module 1: Introduction to Molecular Modeling
- Overview of computational chemistry and molecular modeling.
- Force fields: types, potentials, and energy minimization.
- Understanding molecular structures and their representations.
- Introduction to molecular dynamics simulations (concepts).
Module 2: Docking Theory and Algorithms
- Ligand-receptor interactions and docking theory basics.
- Docking algorithms: rigid-body vs flexible docking (overview).
- Scoring functions: empirical, knowledge-based, and force-field scoring.
- Evaluating docking results and binding affinity calculations.
Module 3: Virtual Screening and Databases
- Database preparation and compound libraries for virtual screening.
- High-throughput screening using docking tools (overview).
- Using molecular docking software: Autodock, Glide, and GOLD (overview).
- Lead compound identification and ranking based on docking scores.
Module 4: Structure-Based Drug Design
- Target identification: receptor-ligand complex, active site recognition.
- Lead optimization: functional group modifications, potency enhancements.
- Computational tools for structure-based drug design.
- Binding affinity and molecular dynamics simulations for optimization.
Module 5: Binding Affinity and SAR
- Calculating binding energy using molecular mechanics.
- Structure-Activity Relationship (SAR) in drug design.
- Binding affinity prediction models: QM/MM, FEP (Free Energy Perturbation).
- Improving binding efficiency through computational optimization.
Module 6: Pharmacokinetics and Toxicity Prediction
- Introduction to ADMET properties: absorption, distribution, metabolism, excretion, toxicity.
- Predicting bioavailability and permeability using computational tools.
- In silico toxicity prediction models: skin irritation, mutagenicity, carcinogenicity.
- Integrating pharmacokinetics data into drug design for better outcomes.
Module 7: Visualization and Analysis
- 3D visualization of docking results using molecular visualization software (PyMOL, Chimera, etc.).
- Interpreting molecular interactions and binding poses.
- Analyzing docking results: key residues, hydrogen bonds, hydrophobic interactions.
- Generating publication-ready figures and reports.
Module 8: Case Studies and Real-World Applications
- Case studies: successful docking in drug discovery (real-life examples).
- Applications in cancer, infectious diseases, and neurological disorders (overview).
- Exploring current trends: AI in drug discovery and deep learning models for docking.
- Challenges and limitations of in silico drug development.
Final Project
- Design a small molecule and dock it to a biological target of choice.
- Deliverables: molecule design, docking results, binding analysis, and final report.
- Submit: a project report summarizing your results and future development directions.
Participant Eligibility
- Students and professionals in Pharmaceutical Sciences, Chemistry, Bioinformatics, Biotechnology
- Researchers working on drug discovery or computational biology
- Basic knowledge of chemistry and molecular biology required
Program Outcomes
- Understand computational molecular modeling and docking theory.
- Perform virtual screening and lead compound identification.
- Design, dock, and optimize small molecules for drug development.
- Analyze binding affinity, pharmacokinetics, and toxicity predictions.
Program Deliverables
- e-LMS Access: lessons, datasets, templates, and case studies.
- Toolkit: docking software guides, SAR analysis sheet, report templates.
- Capstone Support: feedback on design and docking results.
- Assessment: certification after project submission.
- e-Certification and e-Marksheet: digital credentials on completion.
Future Career Prospects
- Computational Chemist (Entry-level)
- Drug Discovery Scientist
- Bioinformatics Analyst (Drug Development)
- Pharmaceutical R&D Associate
Job Opportunities
- Pharmaceutical Companies: drug discovery, computational biology teams.
- Biotech Startups: virtual screening, lead optimization roles.
- Contract Research Organizations: drug development services, computational chemistry support.
- Academia/Research Institutes: molecular modeling and drug discovery research.
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