5-Days Bootcamp AI & Cheminformatics in Drug Discovery

5-Days Bootcamp AI & Cheminforma-
tics in Drug Discovery

Understand the modern drug discovery pipeline
Handle chemical data (SMILES, SDF, descriptors)
Perform virtual screening & docking
Build QSAR models and predict ADMET properties
Apply AI-driven generative models for novel molecule design
Create an end-to-end workflow for lead compound identification

.

Educator

Shivani Singh

Difficulty

Beginner to Expert

Mode

Live Session

Language

English

AI & Cheminformatics in Drug Discovery

Mode – Live Session

Certificate Provided

5 Days

Time 7:00 PM IST

Start Date 25 Sept

Last date 24 Sept

Bootcamp Outcomes

By the end of this bootcamp, you will:
Develop your own cheminformatics pipeline
Screen, dock, and analyze candidate molecules
Apply machine learning & AI to predict molecular properties
Present a complete mini drug discovery project

Day-1

Introduction to Cheminformatics with RDKit

Topic: The drug discovery pipeline and chemical data handling

- Overview of the modern drug discovery process, from target identification to lead optimization.
- Introduction to chemical data and molecular representations (SMILES, SDF), and physicochemical descriptors.

Hands-on:

- Install Python, Miniconda, and libraries (RDKit, Pandas, NumPy, Matplotlib, Jupyter Notebook).
- Learn how to use Jupyter Notebook and standard Python data science libraries.
- Read and write chemical files with RDKit.
- Calculate molecular properties (molecular weight, logP, polar surface area).
- Implement Lipinski’s Rule of Five to filter drug-like molecules.

Project Task: Select a target protein and download a relevant compound dataset from PubChem or ChEMBL.

Day-2

Virtual Screening and Compound Visualization

Topic 1: Exploring Chemical Space

- Techniques for screening large chemical libraries (similarity searching, clustering).

Hands-on:

- Generate molecular fingerprints (e.g., Morgan fingerprints).
- Perform similarity searches to identify drug-like molecules.

Topic 2: Molecular Visualization

Visualizing small molecules and proteins to understand 3D structure and interactions.

Hands-on:

Install and use visualization tools (PyMOL or UCSF Chimera).

- Generate 3D conformers for molecules using RDKit.
- Visualize molecules from the Protein Data Bank (PDB) in PyMOL.

Project Task: Use RDKit to generate 3D conformers for compounds identified on Day 1.

Day-3

Molecular Docking with Open-Source Tools

Topic 1: Introduction to Protein-Ligand Docking

- Principles of structure-based drug design and docking theory.

Hands-on:

- Prepare a protein target and a ligand for docking.
- Use AutoDock Vina to predict ligand binding poses.

Topic 2: Analyzing Docking Results & Workflows

- Interpreting docking scores and binding poses.
- Incorporating docking into automated workflows.

Hands-on:

- Analyze docking poses and scores in PyMOL or UCSF Chimera.
- Set up a basic cheminformatics pipeline in KNIME.

Project Task: Perform docking of selected compounds against the target protein.

Day-4

Machine Learning in Drug Design

Topic 1: QSAR Modeling with scikit-learn

- Introduction to Quantitative Structure-Activity Relationships (QSAR).
- Using ML models to predict molecular properties.

Hands-on:

- Generate molecular descriptors with RDKit.
- Train a machine learning model (Random Forest Regressor) in scikit-learn.

Topic 2: Predicting ADMET Properties

- Importance of ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) in drug discovery.

Hands-on:

- Predict ADMET properties with ADMETLab.
- Integrate ADMET prediction into QSAR workflow.

Project Task: Build a QSAR model and predict ADMET properties for docking hits to identify top candidates.

Day-5

AI-Driven Compound Generation & Wrap-Up

Topic 1: Generative AI for Molecules

- Introduction to deep learning for drug-like molecule generation (SMILES-based models).

Hands-on:

- Use a pre-trained generative model for molecule design.
- Filter generated molecules for drug-likeness using RDKit.

Topic 2: Putting It All Together

- Review of the full drug discovery workflow and future of AI in the field.

Hands-on:

- - Combine all steps into a comprehensive Jupyter Notebook.
  - Present project results: identified lead compounds and selection rationale.

Educator – Shivani Singh

Shivani Singh is a PhD scholar in Computational Biology & Bioinformatics at Sharda University, currently working as a Bioinformatics Educator. She specializes in protein structure prediction, molecular docking, and AI-driven drug repurposing. With an MSc in Bioinformatics, she has conducted hands-on workshops in molecular docking, MD simulations, and bioinformatics pipelines. Her teaching combines research experience with practical skills in structural bioinformatics, cheminformatics, and machine learning for life sciences.