30-Hour Certification Course on Computational Pharmaceutics

Duration: 30 Hours (15 Sessions × 2 Hours)
Mode: Online (Live + Recorded)
Eligibility: B.Pharm, M.Pharm, Pharm.D, Biotechnology, Life Sciences, Pharmaceutical Sciences Students & Professionals

Module 1: Introduction to Computational Pharmaceutics (2 Hours)

• Introduction to Computational Pharmaceutics
• Digital Transformation in Pharmaceutical Industry
• Drug Development Process
• Applications of Computational Tools
• Current Industry Trends

Module 2: Drug Discovery Fundamentals (2 Hours)

• Drug Discovery Pipeline
• Target Identification & Validation
• Lead Identification
• Lead Optimization
• Role of Computational Approaches

Module 3: Computer-Aided Drug Design (CADD) (2 Hours)

• Introduction to CADD
• Structure-Based Drug Design
• Ligand-Based Drug Design
• Pharmacophore Modeling
• Molecular Similarity Concepts

Module 4: Molecular Modeling Basics (2 Hours)

• Protein Structure
• Ligand Structure
• Protein-Ligand Interaction
• Molecular Visualization
• Introduction to PyMOL & Chimera

Module 5: Molecular Docking (2 Hours)

• Principles of Molecular Docking
• Docking Workflow
• Binding Energy
• Active Site Prediction
• Hands-on Docking Demonstration using AutoDock Vina

Module 6: Virtual Screening (2 Hours)

• High Throughput Virtual Screening
• Compound Libraries
• Drug Repurposing
• Hit Selection
• Screening Workflow

Module 7: Molecular Dynamics Simulation (2 Hours)

• Introduction to Molecular Dynamics
• Simulation Workflow
• Force Fields
• Stability Analysis
• RMSD, RMSF Concepts

Module 8: ADMET Prediction (2 Hours)

• Drug Absorption
• Distribution
• Metabolism
• Excretion
• Toxicity Prediction
• SwissADME & pkCSM Demonstration

Module 9: QSAR Modeling (2 Hours)

• Introduction to QSAR
• Molecular Descriptors
• Model Development
• Validation Techniques
• Applications in Drug Discovery

Module 10: Artificial Intelligence in Pharmaceutics (2 Hours)

• AI in Drug Discovery
• Machine Learning Basics
• Predictive Modeling
• Deep Learning Applications
• Future Scope

Module 11: Pharmaceutical Formulation Modeling (2 Hours)

• Computational Formulation Design
• Excipient Selection
• Optimization Techniques
• Design of Experiments (DoE)
• Case Studies

Module 12: PBPK Modeling & Simulation (2 Hours)

• Physiologically Based Pharmacokinetic Models
• Drug Absorption Simulation
• Dose Optimization
• Regulatory Applications
• Introduction to GastroPlus & Simcyp

Module 13: Regulatory Aspects & Data Management (2 Hours)

• Regulatory Requirements
• FDA & EMA Guidelines
• Data Integrity
• Electronic Documentation
• Good Computational Practices

Module 14: Industry Case Studies & Mini Project (2 Hours)

• Real-Time Pharmaceutical Case Studies
• Drug Design Case Example
• Computational Workflow
• Project Planning
• Presentation Preparation

Module 15: Project Presentation & Final Assessment (2 Hours)

• Mini Project Presentation
• Online Assessment
• Viva Discussion
• Career Opportunities in Computational Pharmaceutics
• Resume Building & Interview Guidance

Practical Tools Covered

• AutoDock Vina
• PyMOL
• UCSF Chimera
• SwissADME
• pkCSM
• PubChem
• Protein Data Bank (PDB)
• DrugBank
• Open Babel
• MolView
• BIOVIA Discovery Studio (Viewer)

Learning Outcomes

After completing this course, participants will be able to:

• Understand the fundamentals of Computational Pharmaceutics.
• Perform basic molecular docking and virtual screening.
• Predict ADMET properties using online computational tools.
• Interpret molecular interactions and drug-target binding.
• Apply AI and computational methods in pharmaceutical research.
• Understand PBPK modeling and formulation optimization.
• Gain practical exposure to widely used pharmaceutical software.
• Build a foundation for careers in computational drug discovery, pharmaceutical R&D, bioinformatics, and AI-driven pharmaceutics.

Certification

Participants who successfully complete the training and pass the final assessment will receive:

• 30-Hour Course Completion Certificate
• Skill-Based Training Certificate
• Project Completion Certificate (for qualifying participants)

This curriculum is suitable for universities, colleges, training institutes, and industry upskilling programs, with a strong emphasis on both theoretical concepts and practical computational tools.