Drug Discovery using AI & Machine Learning Session

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Hi Interns ,
Welcome to the Internship Task Portal
Here you will get all the intimations regarding your tasks , sessions , assignments and any updated briefing .
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🧬 Drug Discovery using AI & Machine Learning – Internship Update

The Drug Discovery using AI & Machine Learning Internship is designed to help students build strong foundations in modern computational pharmacology while applying advanced machine learning concepts to real-world therapeutic and clinical pipelines. This program focuses on combining theoretical chemical-biological knowledge with a practical understanding of advanced computational technologies such as molecular modeling, virtual screening, ADMET prediction, deep learning for generative chemistry, and algorithmic target identification.

Throughout the internship, participants will:

  • Learn the fundamentals of traditional and AI-accelerated drug discovery funnels.

  • Understand molecular representations, chemical structure formatting, and structural informatics.

  • Explore virtual compound screening, molecular docking simulations, and quantitative structure-activity relationship (QSAR) modeling.

  • Study applications of deep learning networks and generative models in designing novel, drug-like therapeutic molecules.

  • Gain exposure to production-ready informatics workflows, chemical databases, and modern pharmaceutical research benchmarks.

  • Build a capstone mini-project or case study based on real-world predictive analytics or molecular generation challenges.

Each task includes:

  • 📘 Session-based learning

  • 📝 Notes preparation

  • 💻 Hands-on practical assignments

  • 🔬 Case study analysis

  • 📤 Submission via Google Forms

By the end of the internship, students will gain practical exposure in computational chemistry and artificial intelligence while developing skills useful for careers in computer-aided drug design (CADD), machine learning engineering, pharmaceutical industries, biomedical research, and healthcare innovation.

📥 Internship Task Submission Form

After successfully completing all six tasks in Portal 02, interns are required to submit their work through the Internship Task Submission Form.

Before submitting, ensure you have completed:

  • ✅ Handwritten/Digital Notes for all tasks
  • ✅ Practice & Research Activities
  • ✅ Assignments
  • ✅ Screenshots (if applicable)
  • ✅ Any supporting documents or project files
📌 Task Submission Link:
https://forms.gle/vGJe2BQzQYFiikH26

TASK I: Drug Discovery – A Beginner’s Guide to In-Silico Drug Discovery

🎯 Objective:

Understand the complete drug discovery pipeline and learn how Artificial Intelligence (AI) and Machine Learning (ML) are transforming modern drug discovery.

📚 Sessions:

📝 Task:

Write notes on:

  • What is Drug Discovery?
  • Difference between Traditional Drug Discovery and In-Silico Drug Discovery.
  • Various stages of the drug discovery pipeline.
  • Role of Artificial Intelligence in modern drug discovery.
  • Advantages of AI over conventional drug development.
  • Applications of AI in target identification, virtual screening and lead optimization.

🔬 Practice / Research:

  • Research one successful AI-assisted drug discovery case study.
  • Explain why AI significantly reduces drug development time and cost.
  • Research one pharmaceutical company using AI for drug discovery.

📌 Assignment:

Write a report (250–300 words) comparing Traditional Drug Discovery with AI-Powered Drug Discovery. Include advantages, limitations and future opportunities of AI in pharmaceutical research.

TASK II: Understanding the Protein Structure

🎯 Objective:

Learn the fundamentals of protein structures and understand why proteins are important drug targets.

📚 Sessions:

📝 Task:

Write notes on:

  • What are proteins?
  • Levels of protein structure (Primary, Secondary, Tertiary and Quaternary).
  • Importance of proteins in biological systems.
  • Active sites and binding pockets.
  • Why proteins are selected as drug targets.
  • Introduction to AlphaFold AI.

🔬 Practice / Research:

  • Research AlphaFold and explain how AI predicts protein structures.
  • Select one disease-related protein and explain its biological function.

📌 Assignment:

Prepare a report explaining how protein structures are used in AI-assisted drug discovery. Include one protein example and explain its therapeutic importance.

TASK III: Downloading the Appropriate Protein Structure from PDB

🎯 Objective:

Learn how to obtain high-quality protein structures from the Protein Data Bank (PDB).

📚 Sessions:

  • Session 3 – How to Download the Appropriate Protein Structure from PDB
  • Session Link: (Click here to access)

📝 Task:

Write notes on:

  • What is the Protein Data Bank (PDB)?
  • What is a PDB ID?
  • Different experimental methods (X-Ray, NMR and Cryo-EM).
  • Selecting the best protein structure.
  • Protein quality parameters.

🔬 Practice / Research:

  • Download one protein from the Protein Data Bank.
  • Record its PDB ID, organism, resolution and biological function.

📌 Assignment:

Create a report including:

  • Protein Name
  • PDB ID
  • Organism
  • Disease Association
  • Biological Function
  • Reason for selecting the protein

TASK IV: Chemical Compound Database

🎯 Objective:

Understand how chemical databases support AI-driven drug discovery.

📚 Sessions:

📝 Task:

Write notes on:

  • Introduction to chemical databases.
  • PubChem database.
  • ChEMBL database.
  • ZINC database.
  • DrugBank overview.
  • SMILES notation and molecular properties.

🔬 Practice / Research:

  • Search Aspirin and one additional FDA-approved drug in PubChem.
  • Record Molecular Formula, Molecular Weight, CID and Canonical SMILES.

📌 Assignment:

Prepare a comparison table of two chemical compounds and explain how AI models utilize these databases during virtual screening.

TASK V: Understanding PDB Format and Structure Conversion

🎯 Objective:

Learn how proteins are prepared before molecular docking.

📚 Sessions:

📝 Task:

Write notes on:

  • Structure of a PDB file.
  • Difference between PDB and PDBQT.
  • Removing water molecules.
  • Adding hydrogen atoms.
  • Energy minimization.
  • Protein preparation workflow.

🔬 Practice / Research:

  • Open a downloaded protein in PyMOL or Discovery Studio Visualizer.
  • Identify protein chains, ligands and active sites.

📌 Assignment:

Create a workflow diagram illustrating the complete protein preparation process before molecular docking.

TASK VI: Basic Concepts of Molecular Docking

🎯 Objective:

Understand molecular docking principles and their integration with Artificial Intelligence.

📚 Sessions:

📝 Task:

Write notes on:

  • What is Molecular Docking?
  • Lock-and-Key Theory.
  • Induced Fit Theory.
  • Binding Affinity.
  • Docking Score.
  • Molecular interactions.
  • Role of AI in molecular docking.

🔬 Practice / Research:

  • Research AutoDock Vina.
  • Research PyRx.
  • Explain the complete docking workflow.

📌 Assignment:

Write a report (300–400 words) describing the complete molecular docking workflow and explain how AI improves docking accuracy, virtual screening and lead identification.

 

 

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