Rdkit: Cheminformatics & Drug Discovery In Python

Learn RDKit via systematic introduction & real projects for drug design applications, machine learning modeling, etc.

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.

Requirements

Basic knowledge of chemistry, drug design, cheminformatics, or any related field.

Very basic understanding of Python or any programming language.

Description

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 Reading & Writing Molecules section, the process of reading different formats and writing them will be explained, in addition to important RDKit concepts such as molecular sanitization.In the Molecules section, the Molecule object in RDKit will be explained alongside related objects (Atom, Bond, and Conformer). This section will make you familiar with how RDKit represents and handles molecules.In the Molecule Operations section, the common operations on molecules will be explained, including adding & removing hydrogens, programmatically modifying molecules, and performing substructure matching.In the Descriptors & Fingerprints section, you will learn how to use RDKit to calculate molecular descriptors and fingerprints, the different methods for calculation, and the available types of fingerprints.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.

Overview

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

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.