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Overview Prerequisites Skills You’ll Gain Self Study Materials Included Tools What You’ll Learn Course Modules FAQs

What’s Included?

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Prerequisites

    • Basic Biology Knowledge – Understand fundamental human biology concepts.
    • Pharmaceutical Fundamentals – Familiarity with drug development and approval processes.
    • AI & ML Basics – Grasp core principles of artificial intelligence.
    • Data Analytics Skills – Ability to interpret and analyze datasets.
    • Ethical Awareness – Understand ethics in AI-driven healthcare applications.

Skills You’ll Gain

  • AI-Assisted Drug Discovery
  • Clinical Trial Optimization
  • Medical and Genomic Data Analytics
  • Predictive Modeling for Treatment Outcomes
  • Real-World Evidence Analysis
  • Patient Stratification and Risk Scoring
  • Biomarker and Target Identification
  • Drug Safety and Pharmacovigilance Insights
  • NLP for Clinical and Scientific Texts
  • Ethical and Regulatory-Aware AI in Pharma

Self Study Materials Included

Videos

Engaging visual content to enhance understanding and learning experience.

Podcasts

Insightful audio sessions featuring expert discussions and real-world cases.

E-Books

Comprehensive digital guides offering in-depth knowledge and learning support.

Audiobooks

Listen and learn anytime with convenient audio-based knowledge sharing.

Module Wise Quizzes

Interactive assessments to reinforce learning and test conceptual clarity.

Additional Resources

Supplementary references and list of tools to deepen knowledge and practical application.

Tools You’ll Master

Python

Python

TensorFlow

TensorFlow

PyTorch

PyTorch

Scikit-learn

Scikit-learn

Pandas

Pandas

NumPy

NumPy

SQL

SQL

Jupyter Notebooks

Jupyter Notebooks

MLflow

MLflow

DataBricks

DataBricks

RDKit

RDKit

DeepChem

DeepChem

Biopython

Biopython

Hugging Face Transformers for Biomedical NLP

Hugging Face Transformers for Biomedical NLP

spaCy / Clinical NLP Toolkits

spaCy / Clinical NLP Toolkits

Apache Spark for Healthcare Data

Apache Spark for Healthcare Data

Power BI / Tableau for Clinical Dashboards

Power BI / Tableau for Clinical Dashboards

What You’ll Learn

AI Across the Pharma Value Chain:

Understand how AI and machine learning are applied from discovery to clinical trials and post-market surveillance.

Data-Driven Drug Development:

Learn to analyze clinical, genomic, and real-world data using AI to support evidence-based drug development and decision-making.

Predictive Modeling & Patient Stratification:

Build and evaluate models for treatment outcomes, risk scoring, and optimizing trial design and recruitment.

NLP for Pharma & Healthcare Texts:

Apply NLP to extract insights from scientific literature, clinical notes, and regulatory documents.

Ethics, Regulation & Compliance:

Explore ethical, regulatory, and compliance considerations to ensure responsible and trustworthy AI use in pharma.

Course Modules

Module 1: AI Foundations for Pharma
  1. 1.1 AI and Machine Learning Basics
  2. 1.2 AI Algorithms and Models
  3. 1.3 Use Case: Predictive Modeling for Adverse Drug Reactions and Drug-Drug Interactions Using Historical Patient Datasets
  4. 1.4 Hands-on: Build Predictive Models Using No-Code Tool (Teachable Machine)
Module 2: AI in Drug Discovery and Development
  1. 2.1 AI in Molecular Drug Design
  2. 2.2 AI in Drug Repurposing
  3. 2.3 Use Case: AI-Driven Drug Repurposing Successes (COVID-19 Therapeutics)
  4. 2.4 Hands-On: Practical AI-Driven Molecular Design and Drug Repurposing Using Orange Data Mining Tool
  5. 2.5 Hands-On 2: Exploring Disease-Drug Associations with EpiGraphDB
Module 3: Clinical Trials Optimization with AI
  1. 3.1 AI-Enhanced Patient Recruitment
  2. 3.2 Clinical Data Management and Monitoring
  3. 3.3 Use Case: Pfizer’s AI-Driven Analytics for Optimizing Clinical Trials
  4. 3.4 Hands-on: Implementing Clinical Data Analytics Using No-Code Platforms (KNIME)
Module 4: Precision Medicine and Genomics
  1. 4.1 Personalized Treatment Strategies
  2. 4.2 Biomarker Discovery
  3. 4.3 Case Study: AI-Assisted Biomarker Discovery and Validation in Cancer Treatments
  4. 4.4 Hands-on: Hands-On Genomic Analysis – Exploring AI-Driven Genomic Interpretation Using CBioPortal
Module 5: Regulatory and Ethical AI in Pharma
  1. 5.1 Ethical Considerations and AI Governance
  2. 5.2 AI Compliance and Regulatory Frameworks
  3. 5.3 Case Study: Analyzing Ethical and Regulatory Challenges Encountered in Major AI-Driven Pharma Initiatives
  4. 5.4 Hands-on: Developing AI Governance Strategies Based on Ethical Frameworks
  5. 5.5 Hands-on: Literature Mining with LitVar 2.0
Module 6: Implementing AI in Pharma Projects
  1. 6.1 AI Project Management
  2. 6.2 Evaluating AI Tools and ROI
  3. 6.3 Hands-On: Practical AI Project Management Using Airtable for Tracking, Collaboration, and Management
Module 7: Future Trends and Sustainability in Pharma AI
  1. 7.1 Emerging AI Technologies in Pharma
  2. 7.2 AI for Sustainable Healthcare
  3. 7.3 Case Study: Analysis of Sustainability Initiatives Driven by AI in Pharmaceutical Industry Leaders
  4. 7.4 Hands-on: Scenario Planning and Predictive Analytics Using Dashboards for Future-Focused Decision Making
Module 8: Capstone Project
  1. 8.1 Capstone Project 1: Predictive Modeling for Adverse Drug Reactions in Polypharmacy
  2. 8.2 Capstone Project 2: AI-Enhanced Clinical Trial Recruitment and Retention
  3. 8.3 Capstone Project 3: AI-Powered Drug Design for Rare Diseases
  4. 8.4 Capstone Project Evaluation Scheme

Frequently Asked Questions

Yes, you’ll work with real-world pharma and healthcare use cases—like drug discovery data, clinical trial scenarios, and patient outcome modeling—so you can apply AI techniques directly in pharmaceutical and life sciences environments.

This course is specifically tailored to the pharmaceutical domain, focusing on AI for drug discovery, clinical data analysis, real-world evidence, and regulatory-aware applications, rather than generic AI programs.

You’ll work on projects such as AI-assisted target and molecule ranking, patient risk stratification, trial optimization scenarios, pharmacovigilance signal detection, and a capstone project centered on an AI-powered pharma or healthcare solution.

The course blends core theory with hands-on labs, guided notebooks, and end-to-end projects using real or simulated pharma datasets, ensuring you build practical, implementation-ready skills instead of just conceptual understanding.

You’ll gain specialized AI-in-pharma skills that align with roles like AI Pharma Data Scientist, Clinical AI Specialist, Drug Discovery ML Engineer, and other emerging positions at pharma companies, biotechs, CROs, and healthtech firms.