Certificate Program in Pharmacoinformatics: Drug Discovery, AI & Computational Drug Design

Duration: 30 Hours
Mode: Online (Live + Recorded Sessions)
Level: Beginner to Intermediate
Eligibility: B.Pharm, M.Pharm, Pharm.D, Biotechnology, Life Sciences, Chemistry, Bioinformatics, Medical Sciences Students & Professionals

Course Overview

Pharmacoinformatics integrates pharmaceutical sciences with bioinformatics, cheminformatics, computational biology, artificial intelligence, and drug discovery technologies. This course provides practical exposure to modern computational tools used in drug discovery, molecular modeling, virtual screening, QSAR, molecular docking, ADMET prediction, and AI-assisted pharmaceutical research.

Learning Outcomes

After completing this course, participants will be able to:

• Understand Pharmacoinformatics and Computational Drug Discovery
• Work with biological and chemical databases
• Perform protein and ligand preparation
• Conduct molecular docking studies
• Analyze protein-ligand interactions
• Understand QSAR and pharmacophore modeling
• Predict ADMET properties
• Apply AI and Machine Learning in drug discovery
• Perform virtual screening
• Design computational drug discovery workflows
• Interpret docking and computational results
• Develop mini drug discovery projects

Course Curriculum (30 Hours)

Module 1: Introduction to Pharmacoinformatics (2 Hours)

• Introduction to Pharmacoinformatics
• Drug Discovery Pipeline
• Role of Computational Biology
• Traditional vs Computer-Aided Drug Design (CADD)
• Current Industry Applications
• Future Scope and Career Opportunities

Practical

• Overview of computational drug discovery workflow

Module 2: Biological & Chemical Databases (3 Hours)

Biological Databases

• Protein Data Bank (PDB)
• UniProt
• NCBI
• KEGG
• DrugBank

Chemical Databases

• PubChem
• ChEMBL
• ChemSpider
• ZINC Database

Hands-on

• Protein retrieval
• Ligand downloading
• Structure visualization

Module 3: Molecular Biology & Protein Structure (3 Hours)

• Protein Structure
• Amino Acids
• Protein Folding
• Active Site Identification
• Binding Pocket Analysis
• Protein Visualization

Practical

• PyMOL
• Discovery Studio Visualizer

Module 4: Molecular Docking (5 Hours)

Theory

• Molecular Docking Principles
• Docking Algorithms
• Scoring Functions
• Docking Workflow

Practical

• Protein Preparation
• Ligand Preparation
• Grid Generation
• AutoDock Vina
• Docking Analysis
• Interaction Analysis

Software

• AutoDock Vina
• PyRx
• Discovery Studio

Module 5: Molecular Visualization (2 Hours)

• Protein-Ligand Interaction
• Hydrogen Bond Analysis
• Hydrophobic Interaction
• Binding Energy Interpretation
• 2D & 3D Interaction Maps

Hands-on

• PyMOL
• Discovery Studio

Module 6: Virtual Screening (3 Hours)

• Structure-Based Screening
• Ligand-Based Screening
• Library Preparation
• Lead Identification
• Hit Selection
• Ranking Molecules

Practical

• PyRx Virtual Screening
• Screening Small Molecule Libraries

Module 7: QSAR & Pharmacophore Modeling (3 Hours)

QSAR

• Descriptor Generation
• Molecular Properties
• Model Development
• Validation

Pharmacophore

• Pharmacophore Features
• Lead Optimization
• Drug Design

Practical

• SwissADME
• Online QSAR Tools

Module 8: ADMET Prediction (3 Hours)

• Drug-likeness
• Lipinski Rule
• Pharmacokinetics
• Toxicity Prediction
• Bioavailability
• Solubility
• BBB Prediction

Hands-on

• SwissADME
• pkCSM
• ProTox

Module 9: AI & Machine Learning in Pharmacoinformatics (3 Hours)

• Artificial Intelligence Basics
• Machine Learning Concepts
• AI in Drug Discovery
• Deep Learning Applications
• Generative AI in Drug Design
• AlphaFold Overview
• AI-Based Lead Optimization
• Emerging Trends

Hands-on

• AI-powered drug discovery platforms (demonstration)
• ChatGPT for literature mining and research assistance

Module 10: Mini Project & Final Assessment (3 Hours)

Mini Project

Participants will complete a guided computational drug discovery workflow:

• Select a disease target
• Retrieve protein structure
• Download ligands
• Perform docking
• Analyze interactions
• Predict ADMET
• Prepare a short project report

Final Assessment

• Online MCQ Examination
• Practical Evaluation
• Project Presentation
• Feedback Session

Software & Tools Covered

• AutoDock Vina
• PyRx
• PyMOL
• Discovery Studio Visualizer
• SwissADME
• pkCSM
• ProTox-II
• PubChem
• DrugBank
• Protein Data Bank (PDB)
• UniProt
• ChEMBL
• ChemSpider
• ZINC Database
• NCBI
• KEGG

Projects Included

• Molecular Docking of Drug Candidates
• Protein-Ligand Interaction Analysis
• ADMET Prediction of Lead Molecules
• Virtual Screening Workflow
• AI-Assisted Drug Discovery Case Study

Assessment Criteria

Certification

Participants who successfully complete the course requirements will receive a 30-Hour Certificate in Pharmacoinformatics, recognizing their training in computational drug discovery, molecular docking, virtual screening, ADMET prediction, and AI applications in pharmaceutical research.

Career Opportunities

After completing this program, learners can pursue roles such as:

• Pharmacoinformatics Associate
• Computational Drug Discovery Research Assistant
• Molecular Modeling Executive
• Bioinformatics Analyst
• Cheminformatics Scientist
• Drug Discovery Associate
• Clinical Research Informatics Professional
• Pharmaceutical Data Analyst
• AI in Drug Discovery Associate
• Research Intern in Computational Biology and Pharmaceutical Sciences

This curriculum is aligned with current pharmaceutical industry trends and provides a balance of theoretical concepts and hands-on computational training suitable for a 30-hour online certification program.