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Integrating Artificial Intelligence in Experimental Pharmacology: Bridging in Silico Prediction with in Vivo Validation

• Clinical Success: Real-time monitoring of the first AI-designed drug candidates currently navigating human clinical trials. • Smart Toxicity (ADMET): Utilizing machine learning to predict drug safety and metabolic profiles, reducing the need for early animal testing. • Precision Animal Models: Implementing AI-driven computer vision to achieve 100% objective tracking of rodent behavior (e.g., gait and grooming). • Generative AI: Revolutionizing pharmacological experiments through synthetic data and molecular design. • Diverse Methodologies: Applying various AI-enhanced methods to evaluate and validate pharmacological activities.

Duration

1 Week

Skill Level

Beginner

Starts From:

2026-07-20

Category

Science And Technology

Overview

Traditional experimental pharmacology has long relied on resource-intensive in vivo and in vitro models to evaluate drug safety and efficacy. However, the integration of artificial intelligence (AI) is revolutionizing this workflow by transforming the laboratory from a primary discovery site to a targeted validation center. This abstract explores the synergy between computational intelligence and physical experimentation in modern pharmacological research.
The application of machine learning (ML) and deep learning (DL) algorithms now allows researchers to predict pharmacological outcomes—such as LD50 values, binding affinities, and ADMET profiles—before a single animal is handled. By utilizing tools like Quantitative Structure-Activity Relationship (QSAR) modeling and AI-driven molecular docking, pharmacologists can narrow down thousands of lead compounds to a select few high-probability candidates. This "AI-first" approach directly supports the 3Rs principle (Replacement, Reduction, and Refinement) by significantly reducing the number of laboratory animals required for screening.


Outcomes


1. Understand the fundamental concepts of artificial intelligence (AI) and its applications in experimental pharmacology, including machine learning and data-driven drug discovery.
2. Explain the principles and methodologies of in silico techniques such as molecular docking, QSAR modeling, and virtual screening in predicting pharmacological activity.
3. Demonstrate the ability to utilize AI-based tools and software for drug target identification, lead optimization, and toxicity prediction.
4. Analyze and interpret computational results and correlate them with in vivo experimental data for validation of pharmacological effects.

5. Evaluate ethical considerations, limitations, and regulatory aspects associated with the use of AI in pharmacological research.
6. Apply interdisciplinary approaches combining pharmacology, bioinformatics, and AI for innovative research problem-solving.


Modules

  • 1: Introduction to AI in Pharmacology
    Duration: 3–4 hours
    Content: Basics of Artificial Intelligence and Machine Learning
    Role of AI in drug discovery and development
    Overview of experimental pharmacology
    Importance of integrating AI with biological studies.
    Learning Outcome: Participants will understand the fundamentals of AI and its relevance in pharmacology.

    2: In Silico Techniques in Drug Discovery
    Duration: 5–6 hours
    Content: Molecular docking and simulation
    QSAR (Quantitative Structure–Activity Relationship)
    Virtual screening techniques
    ADMET prediction (absorption, distribution, metabolism, excretion, and toxicity)
    Databases and tools (e.g., AutoDock, PubChem, SwissADME)

    Practical Session: Hands-on molecular docking demonstration
    Learning Outcome: Participants will gain skills in computational drug prediction methods.
    3: AI Tools and Applications
    Duration: 4–5 hours
    Content: Machine learning models in pharmacology
    Deep learning applications in drug discovery
    AI for target identification and biomarker discovery
    Case studies of AI-driven drug development

    Practical Session: Introduction to AI-based platforms and software.
    Learning Outcome: Participants will be able to apply AI tools in pharmacological research.

     



Our Trainers

Dr. Seema Sharma
Dr. Seema Sharma Associate Professor Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore,MP
Dr. Ajay Singh Kushwah
Dr. Ajay Singh Kushwah  Professor & HOD  Department of Pharmacology Amar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy Bela, Ropar, Punjab,India
Mr. Gautam Gehani
Mr. Gautam Gehani  Assistant Professor Department of Pharmacology Mittal Institute of Pharmacy Bhopal
Dr. Avinash Singh Mandloi
Dr. Avinash Singh Mandloi Professor Department of Pharmacology VNS College of Pharmacy,Bhopal
Dr. Shamsher Singh
Dr. Shamsher Singh Professor & HOD Department of Pharmacology ISF College of Pharmacy,Moga Punjab
Dr. Lokesh Verma
Associate Professor at SUB  Sir has completed his PhD. In Neuropharmacology  Total Teaching experience - 12years  Total Publication - 10+ Organized National & International Conferences, seminar and workshop.

Contact

Name: Ms. Divya Prajapati

Mobile No: 8225906836

Email: [email protected]


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This Premium course is included in plans

₹ 1000/-

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