Course Information

Artificial intelligence (AI), including machine learning and deep learning, is rapidly transforming modern drug design and discovery. This course provides a practical, beginner-friendly introduction to AI in pharmaceutical research, progressing from foundational concepts to advanced applications. Participants will learn Python programming with a strong emphasis on real-world use cases currently applied by researchers and industry professionals in drug discovery and development.

Designed for pharmacists, chemists, students, and life science professionals, this course teaches you how machine learning and deep learning models are developed, interpreted, and applied in modern drug discovery workflows; no prior programming experience required.

 

What You Will Learn

By the end of this artificial intelligence course, you will be able to:

• Understand the core principles of machine learning and deep learning
• Recognize how AI models are used in drug design and pharmaceutical research
• Understand the logic, structure, and workflows behind common machine learning models
• Preprocess and work with research data for model development
• Use Google Colab notebooks to run and explore practical examples independently
• Follow guided implementation videos to understand each step of the workflow
• Develop the confidence to adapt and apply machine learning approaches in your own research

 

Course Structure

This course is organized into three chapters, progressing from foundational concepts to advanced applications:

• Chapter 1: Data Preprocessing and Fundamental Concepts: A beginner-friendly introduction to data handling and the essential concepts needed before building any machine learning model. No coding background is assumed.
• Chapter 2: Machine Learning Applications in Drug Discovery: Learn how core machine learning algorithms are applied to real pharmaceutical research problems, including model training, evaluation, and interpretation.
• Chapter 3: Deep Learning Methods in Pharmaceutical Research: Explore deep learning architectures and their use in modern drug design workflows, including hands-on implementation examples in Google Colab.

 

Hands-On Learning Experience

This course goes beyond theory. Each chapter includes:

• Google Colab notebooks with ready-to-run code for practical exploration
• Step-by-step video walkthroughs explaining every part of the workflow
• Real research workflows from pharmaceutical and biomedical settings

Learners gain direct exposure to the tools and methods used in real drug discovery pipelines.

 

Who This Course Is For

• Pharmacists and pharmacy students
• Chemists and chemistry students
• Pharmaceutical and biomedical researchers
• Life science students interested in AI-driven drug discovery
• Beginners with no programming background who want to enter the field

 

Requirements

• Internet access and a web browser
• A Google account for Google Colab (free)
• No prior programming knowledge is required; all concepts are explained step by step

 

About the Instructor

This course is taught by Mahsa Sheikholeslami, a pharmacist and researcher in artificial intelligence applications for drug design and discovery.

 

Start Learning AI for Drug Design Today

Whether you are a pharmacist, chemist, or life science researcher, this course gives you the practical foundation to understand and apply machine learning and deep learning in drug design and discovery. Join learners from across the pharmaceutical and biomedical fields who are building their AI skills for the future of drug research.

Course Structure

Session 0 – Introduction

Preprocessing:

• Session 01: Prerequisites
• Session 02: Variables, Input/Output, Data Types, Strings and Operators
• Session 03: Control Flow (if / else), For loop and While loop
• Session 04: Functions, Anonymous Functions (lambda) and Exception Handling
• Session 05: Reading & Writing in Pandas, Indexing, Selecting & Assigning and Summary Functions & Map
• Session 06: Grouping & Aggregation, Merging & Combining, and Data Types & Missing Values
• Session 07: Dataset Introduction, Outlier Detection & Handling, Missing Data Imputation and Data Type Preprocessing
• Session 08: Introduction to EDA, Primary Steps (review of data preprocessing), Univariate Analysis and Bivariate Analysis
• Session 09: Introduction to NumPy & Arrays, Array Operations & Indexing, and Statistical Analysis with NumPy

Machine Learning

• Session 01: Drug Discovery Pipeline
• Session 02: Molecular Representations with Notebook 2 Explained
• Session 03: Major Datasets and Major Issues in Data Curation with Notebook 3 Explained
• Session 04: ML Basics
• Session 05: Classical ML Algorithms and Notebook 4-5 Explained

Deep Learning

• Session 06: Deep Learning and Notebook 6 Explained
• Session 07: Applied ADMET Modeling and Notebook 7 Explained
• Session 08: Binding Affinity Prediction
• Session 09: Generative Models
• Session 10: Drug Discovery Recap