Download RDKit: Cheminformatics & Drug Discovery in Python 2025
RDKit: Cheminformatics & Drug Discovery in Python course download, Learn RDKit via systematic introduction & real projects for drug design applications, machine learning modeling, etc. In this course, you will learn the RDKit toolkit in two ways: first by systematically exploring the toolkit’s common modules and functionalities, and second by working on meaningful real-life projects. The content is explained step by step with details in Jupyter Notebook, which is a user-friendly code editor.
In the Drawing Molecules section, you will learn how to draw molecules, the different methods for drawing, how to customize drawing options, how to highlight atoms & bonds, and when to use each drawing method. In the Projects section, you will learn how to combine different RDKit concepts to perform real and meaningful projects and workflows in cheminformatics and drug discovery. You will also learn how to integrate RDKit with other Python packages—for example, how to build machine learning models with RDKit and scikit-learn for virtual screening, and how to use RDKit with the Pandas package for advanced data analysis. The projects will also demonstrate how to use RDKit’s algorithms, such as MCS (Maximum Common Substructure) analysis, 3D conformer generation, and similarity analysis. The projects will also cover more advanced topics, such as fragment-based drug design with RDKit, which involves handling and connecting fragments conditionally.
What you’ll learn
- Master the RDKit package in Python for cheminformatics & drug design tasks. Understand the modules & main concepts of the toolkit to become proficient with it.
- Learn essential RDKit features including reading, writing, manipulating and drawing molecules. Also, calculating fingerprints and descriptors.
- Use advanced RDKit algorithms for similarity analysis, MCS (Maximum Common Substructure) analysis, and 3D conformer generation.
- Integrate RDKit with scikit-learn to develop machine learning models (regression & classification) and use them in virtual screening.
- Plan and execute RDKit-based scripts and projects for practical drug discovery workflows.
- Perform Fragment-Based Drug Design using RDKit by handling and connecting chemical fragments conditionally.
- Combine RDKit with Pandas for advanced chemical data analysis and manipulation.
Course concepts
- Section 1: Introduction
- Lecture 1 Course Structure
- Lecture 2 RDKit Overview
- Lecture 3 Installation
- Section 2: Reading & Writing Molecules
- Lecture 4 Reading Molecules [SDF Files]
- Lecture 5 Molecule Sanitization Process
- Lecture 6 Reading Molecules [SMILES Formats]
- Lecture 7 Writing Molecules [SDF File]
- Section 3: Molecules in RDKit
- Lecture 8 Molecules Objects
- Lecture 9 Atoms Objects
- Lecture 10 Bonds Objects
- Lecture 11 Conformers Objects
- Section 4: Molecular Operations
- Lecture 12 Adding & Removing Hydrogens
- Lecture 13 Modifying Molecule Structure
- Lecture 14 Substructure Matching
- Section 5: Molecular Descriptors & Fingerprints
- Lecture 15 Calculating Molecular Descriptors
- Lecture 16 Calculating Fingerprints
- Section 6: Drawing Molecules
- Lecture 17 Drawing Molecules [Overview & Drawing Options]
- Lecture 18 Drawing with Highlighting Atoms & Bonds
- Lecture 19 Drawing Multiple Molecules
- Lecture 20 Drawing Molecules by Using Functions
- Section 7: Projects
- Lecture 21 Performing Substructure Matching & Drawing Result
- Lecture 22 Computing Similarity to a Reference Molecule & Managing Result
- Lecture 23 Generating & Identifying Lowest Energy Conformer
- Lecture 24 Maximum Common Substructure [Part 1 – Performing MCS]
- Lecture 25 Maximum Common Substructure [Part 2 – Exploring Options]
- Lecture 26 Developing a Regression Machine Learning Model [RDKit + Scikit-Learn]
- Lecture 27 Applying Machine Learning Model for Virtual Screening
- Lecture 28 Developing a Classification Machine Learning Model [RDKit + Scikit-Learn]
- Lecture 29 Integrating with Pandas Package for Data Analysis
- Lecture 30 Connecting Molecular Fragments Conditionally
Who this course is for:
- Anyone interested in learning RDKit for Python.
- Cheminformatics/drug discovery practitioners who wants to apply or implement computational methods.
- Researchers building machine learning models for chemical data.
Course details
- Video quality: MP4 | Video: h264, 1280 × 720
- Audio quality: Audio: AAC, 44.1 KHz, 2 Ch
- Last updated 06/2025
- Video duration: 7h 9m
- Number of lessons: 07 sections, 30 lectures
- Language: Language: English
- Compressed file size: 2.69 GB
