• Home
  • /
  • In Silico Molecular Modeling and Docking in Drug Development
Biotechnology · Moderate · Live online

In Silico Molecular Modeling and Docking in Drug Development

Register NowExplore Details

1 MonthCertificate

About This Course

In silico molecular modeling and docking play a crucial role in modern drug discovery and development by enabling the prediction and analysis of molecular interactions between drug candidates and their biological targets. This computational drug design course covers both the theoretical foundations and practical applications of these techniques, focusing on molecular dynamics simulations, binding site identification, virtual screening, and structure-based drug design (SBDD).

Participants will learn to use various molecular docking software and biological databases to perform ligand-receptor docking studies, structure validation, and binding affinity estimation. Emphasis is placed on using tools like PyRx, ArgusLab, and ChemSketch to simulate real-world drug discovery pipelines.

Through hands-on sessions and projects, participants will gain experience in lead optimization, QSAR modeling, and energy minimization techniques—making them proficient in computational workflows commonly used in pharmaceutical R&D and biotechnology innovation.

Aim

The program aims to equip participants with comprehensive knowledge and practical experience in in silico drug design methods including ligand-based drug design (LBDD), homology modeling, and molecular interaction studies. It prepares learners to accelerate the hit-to-lead process and perform AI-driven drug candidate optimization using virtual screening strategies.

Next: Day-by-day →

Program Objectives

  • Understand the principles of molecular modeling and docking.
  • Master computational tools for drug discovery (e.g., PyRx, Modeller, ArgusLab).
  • Perform binding site prediction, molecular docking, and virtual screening.
  • Analyze and interpret docking results, pharmacophore modeling, and drug-likeness.
  • Apply in silico techniques to real-world preclinical drug development projects.

Program Structure

Week 1: Preparation of Protein Structure

  • Preparation of compound library
  • Generating the 2D & 3D structures using Cheminformatics tools
  • Energy and molecular property calculation
  • Force field-based energy minimization
  • Quantum Mechanics-based property evaluation
  • Tools: ISIS Draw/ChemSketch, Argus Lab, Mopac

Week 2: Protein Active Site Prediction

  • Understanding protein-ligand interactions
  • Overview of 3D structure file formats (PDB, MOL2)
  • Use of biological and chemical databases (PubChem, DrugBank, Zinc)
  • Active site prediction via online and AI-assisted tools
  • Tools: PDB/PDBSum, Active Site Prediction Tool

Week 3: Comparative Modeling / Homology Modeling

  • Predicting 3D structure from known templates
  • Ab initio protein structure prediction
  • Use of machine learning tools in structural biology
  • Tools: Modeller, I-TASSER, and other online prediction platforms

Week 4: Structure Validation

  • Evaluating structural integrity
  • Ramachandran Plot analysis
  • Tools: SAVES server, PROCHECK

Week 5: Docking and Interaction Studies

  • Structure-Based Drug Design (SBDD) techniques
  • Ligand-Based Drug Design (LBDD) strategies
  • Virtual screening pipelines and binding affinity prediction
  • Tools: PyRx, ArgusLab, Autodock

Who Should Enrol?

  • Students pursuing or holding degrees in Bioinformatics, Biotechnology, Pathology, Chemistry, Computer Science, or Biomedical Engineering.
  • PhD Scholars and Researchers working in computational biology, digital pathology, AI in healthcare, or biomedical image analysis.
  • Industry Professionals in pharma R&D, diagnostics, medical imaging, and bioinformatics.
  • Aspiring learners interested in AI, machine learning, molecular docking, and drug design algorithms.

Program Outcomes

  • Use computational drug discovery platforms for virtual screening and lead optimization
  • Perform molecular docking and QSAR modeling
  • Interpret binding energies, docking poses, and molecular descriptors
  • Contribute to data-driven decision making in pharmaceutical research
Next: Fees →

Fee Structure

Standard: ₹14,998 | $258

Discounted: ₹7499 | $129

We accept 20+ global currencies. View list →

Next: FAQ →

Frequently asked questions

Will I get the recording?

Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).

What if I miss a live session?

Watch the recording in your dashboard and send your questions to the mentor through the support desk.

Do I get a certificate?

Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.

Which fee category applies to me?

Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.

How can I pay?

By card, UPI or PayPal on the payment page; international bank transfer is also available there.

What is the refund policy?

See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy

Need Help?

We’re here for you!


(+91) 120-4781-217
WhatsApp +91 99581 61117

Registration open

More upcoming workshops

Live, mentor-led and certified. Dates and times in IST.

See all workshops
FREEDOM TO LEARN • 10% OFF All Courses & Workshops • Use Code: NANOINDIA10 • ⏳ Offer Ends In: Loading... • Learn Today. Lead Tomorrow. • Explore Programs →
FREEDOM TO LEARN • 10% OFF All Courses & Workshops • Use Code: NANOINDIA10 • ⏳ Offer Ends In: Loading... • Learn Today. Lead Tomorrow. • Explore Programs →
Support