Drug Design and Discovery: Using Machine Learning and Deep Learning

About Course

This course introduces the foundations of machine learning and deep learning for drug design and discovery, with a focus on practical use in pharmaceutical research. It is designed for students and researchers who want to understand how these models work, how to interpret them, and how to apply them independently in their own projects.

The course is organized into three main chapters:

Chapter 1: Preprocessing (for beginners)

Chapter 2: Basics of machine learning and its use in drug discovery

Chapter 3: Deep learning methods and their applications in modern pharmaceutical research

Alongside conceptual lessons, the course includes hands-on Google Colab notebooks and explanation videos that demonstrate how the methods work in real workflows.

By the end of this course, learners will be able to:
Understand the core ideas behind machine learning and deep learning
Recognize how these methods are used in drug design and discovery
Work with the logic, data, and workflows behind common model types
Use Google Colab notebooks to explore practical examples
Follow explanation videos to understand each step of the implementation
Build the confidence to study, adapt, and apply models independently for research

Course Content

Preprocessing and Machine Learning Session 0: Introduction
Introduction

Session 0: Introduction

Preprocessing Session 01: Prerequisites
Preprocessing

Preprocessing Session 02: Variables, IO, Data Types, Strings and Operators
Preprocessing

Preprocessing Session 03: Control Flow, For Loop and While Loop
Preprocessing

Preprocessing Session 04: Functions, Anonymous Functions and Exception Handling
Preprocessing

Preprocessing Session 05: Reading & Writing in Pandas, Indexing, Selecting & Assigning and Summary Functions & Map
Preprocessing

Preprocessing Session 06: Part 1: Grouping, Aggregation, Merging, Combining, Data Types and Missing Values
Preprocessing

Preprocessing Session 07: Dataset Introduction, Outlier Detection and Handling and Missing Data Imputation
Preprocessing

Preprocessing Session 08: Introduction to EDA, Primary Steps, Univariate Analysis and Bivariate Analysis
Preprocessing

Preprocessing Session 09: Introduction to NumPy and Arrays, Array Operations & Indexing and Statistical Analysis with NumPy
Preprocessing

Machine Learning Session 01: Drug Discovery Pipeline
Basics of machine learning and its use in drug discovery

Machine Learning Session 02: Molecular Representations
Basics of machine learning and its use in drug discovery

Machine Learning Session 03: Major Datasets and Major Issues in Data Curation
Basics of machine learning and its use in drug discovery

Machine Learning Session 04: ML Basics
Basics of machine learning and its use in drug discovery

Machine Learning Session 05: Classical ML Algorithms
Basics of machine learning and its use in drug discovery

Deep Learning Session 06: Deep Learning
Deep learning methods and their applications in modern pharmaceutical research

Deep Learning Session 07: Applied ADMET Modeling
Deep learning methods and their applications in modern pharmaceutical research

Deep Learning Session 08: Binding Affinity Prediction
Deep learning methods and their applications in modern pharmaceutical research

Deep Learning Session 09: Generative Models
Deep learning methods and their applications in modern pharmaceutical research

Recap Session 10: Machine Learning in Drug Discovery
Recap

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About Course

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Course Content

Preprocessing and Machine Learning Session 0: Introduction Introduction

Session 0: Introduction

Preprocessing Session 01: Prerequisites Preprocessing

Session 01: Prerequisites

Preprocessing Session 02: Variables, IO, Data Types, Strings and Operators Preprocessing

Session 02: Part 1 – Variables

Session 02: Part 2 – Input/Output

Session 02: Part 3 – Data Types

Session 02: Part 4 – Strings

Session 02: Part 5 – Operators

Preprocessing Session 03: Control Flow, For Loop and While Loop Preprocessing

Session 03: Part 1 – Control Flow (if / else)

Session 03: Part 2 – For Loop

Session 03: Part 3 – While Loop

Preprocessing Session 04: Functions, Anonymous Functions and Exception Handling Preprocessing

Session 04: Part 1 – Functions

Session 04: Part 2 – Anonymous Functions (lambda)

Session 04: Part 3 – Exception Handling

Preprocessing Session 05: Reading & Writing in Pandas, Indexing, Selecting & Assigning and Summary Functions & Map Preprocessing

Session 05: Part 1 – Reading & Writing in Pandas

Session 05: Part 2 – Indexing, Selecting & Assigning

Session 05: Part 3 – Summary Functions & Map

Preprocessing Session 06: Part 1: Grouping, Aggregation, Merging, Combining, Data Types and Missing Values Preprocessing

Session 06: Part 1 – Grouping & Aggregation

Session 06: Part 2 – Merging & Combining

Session 06: Part 3 – Data Types & Missing Values

Preprocessing Session 07: Dataset Introduction, Outlier Detection and Handling and Missing Data Imputation Preprocessing

Session 07: Part 1 – Dataset Introduction

Session 07: Part 2 – Outlier Detection & Handling

Session 07: Part 3 – Missing Data Imputation

Preprocessing Session 08: Introduction to EDA, Primary Steps, Univariate Analysis and Bivariate Analysis Preprocessing

Session 08: Part 1 – Introduction to EDA

Session 08: Part 2 – Primary Steps (review of data preprocessing)

Session 08: Part 3 – Univariate Analysis

Session 08: Part 4 – Bivariate Analysis

Preprocessing Session 09: Introduction to NumPy and Arrays, Array Operations & Indexing and Statistical Analysis with NumPy Preprocessing

Session 09: Part 1 – Introduction to NumPy & Arrays

Session 09: Part 2 – Array Operations & Indexing

Session 09: Part 3 – Statistical Analysis with NumPy

Machine Learning Session 01: Drug Discovery Pipeline Basics of machine learning and its use in drug discovery

Session 01 – Drug Discovery Pipeline

Machine Learning Session 02: Molecular Representations Basics of machine learning and its use in drug discovery

Session 02 – Molecular Representations

Notebook 2 Explained

Machine Learning Session 03: Major Datasets and Major Issues in Data Curation Basics of machine learning and its use in drug discovery

Session 03 – Part 1 – Major Datasets

Session 03 – Part 2 – Major Issues in Data Curation

Notebook 3 Explained

Machine Learning Session 04: ML Basics Basics of machine learning and its use in drug discovery

Session 04 – ML Basics

Machine Learning Session 05: Classical ML Algorithms Basics of machine learning and its use in drug discovery

Session 05 – Classical ML Algorithms

Notebook 4-5 Explained

Deep Learning Session 06: Deep Learning Deep learning methods and their applications in modern pharmaceutical research

Session 06 – Deep Learning

Notebook 6 Explained

Deep Learning Session 07: Applied ADMET Modeling Deep learning methods and their applications in modern pharmaceutical research

Session 07 – Applied ADMET Modeling

Notebook 7 Explained

Deep Learning Session 08: Binding Affinity Prediction Deep learning methods and their applications in modern pharmaceutical research

Session 08 – Binding Affinity Prediction

Deep Learning Session 09: Generative Models Deep learning methods and their applications in modern pharmaceutical research

Session 09 – Generative Models

Recap Session 10: Machine Learning in Drug Discovery Recap

Session 10 – Drug Discovery Recap