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Basics of AI and ML in Drug Discovery
Rating: 4.7 out of 5(12 ratings)
43 students

Basics of AI and ML in Drug Discovery

AI and ML in Drug Discovery: Applications in Target Identification, Drug Design, Clinical Development, and Future Trends
Created bySinjini Bala
Last updated 10/2025
English
English [Auto],

What you'll learn

  • Understand the fundamental concepts of artificial intelligence and machine learning and their specific applications in drug discovery.
  • Gain insights into how AI accelerates target identification, drug design, and clinical trials.
  • Evaluate ethical, regulatory, and data privacy challenges in applying AI to biomedical research.
  • Explore real-world case studies and future directions, including AI in rare diseases and integration with emerging technologies like quantum computing.

Course content

2 sections • 12 lectures • 1h 35m total length

  • Introduction to AI and Machine Learning in Drug Discovery9:59

    1. Evolution of drug discovery and the role of AI

    2. Overview of machine learning and deep learning concepts

    3. Importance of AI in modern pharmaceutical research

    4. Benefits and challenges of integrating AI into drug discovery

  • Fundamentals of Drug Discovery Process9:16

    1. Traditional drug discovery workflow

    2. Target identification and validation

    3. Hit identification and lead optimization

    4. Preclinical and clinical trial phases

  • Basics of Artificial Intelligence8:25

    1. Core principles of AI

    2. Types of AI: narrow, general, and superintelligence

    3. Symbolic AI vs. data-driven AI

    4. Relevance of AI in life sciences

  • Fundamentals of Machine Learning7:46

    1. Supervised learning methods

    2. Unsupervised learning methods

    3. Reinforcement learning basics

    4. Performance evaluation metrics

  • Data in Drug Discovery7:57

    1. Biological and chemical datasets

    2. Genomics, proteomics, and transcriptomics data

    3. Chemical compound libraries and molecular descriptors

    4. Data challenges: quality, integration, and bias

  • Computational Approaches in Drug Discovery7:30

    1. Molecular docking and virtual screening

    2. Quantitative Structure–Activity Relationship (QSAR) modeling

    3. Molecular dynamics simulations

    4. Systems biology and network pharmacology

  • Deep Learning in Drug Discovery7:08

    1. Neural networks and backpropagation

    2. Convolutional neural networks for molecular imaging

    3. Recurrent neural networks for sequence data

    4. Generative models for molecule design

  • Applications of AI in Target Discovery7:36

    1. Gene expression analysis with AI

    2. Protein structure prediction (AlphaFold and beyond)

    3. Biomarker discovery using machine learning

    4. AI-driven disease modeling

  • Applications of AI in Drug Screening and Design7:29

    1. Virtual high-throughput screening

    2. AI-based molecular property prediction

    3. Drug repurposing with machine learning

    4. Generative AI for novel drug design

  • AI in Preclinical and Clinical Development7:31

    1. Predicting ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity)

    2. AI for optimizing clinical trial design

    3. Patient stratification and personalized medicine

    4. Predictive models for treatment outcomes

  • Ethical, Regulatory, and Practical Considerations7:22

    1. Ethical challenges in AI-driven drug discovery

    2. Data privacy and security in biomedical AI

    3. Regulatory perspectives and FDA/EMA guidelines

    4. Reproducibility and transparency in AI models

  • Future Directions and Case Studies7:31

    1. Recent breakthroughs and success stories

    2. AI in rare disease drug discovery

    3. Integration of quantum computing and AI

    4. Future prospects of AI in pharmaceutical R&D

Requirements

  • For this course, there are no strict prerequisites. It is designed to be beginner-friendly and accessible to anyone interested in the intersection of artificial intelligence and drug discovery. A basic understanding of biology, biotechnology, or pharmaceuticals can be helpful, but it is not mandatory. Similarly, no prior coding or advanced mathematics skills are required, as concepts will be explained in a clear and simplified way. Learners only need curiosity and an eagerness to explore how AI is transforming the future of healthcare and pharmaceutical research.

Description

Welcome to Basics of AI and Machine Learning in Drug Discovery! If you’ve ever wondered how new medicines are discovered and how technology is transforming this process, you’re in the right place. This course is designed to guide you step by step through the exciting world of AI and Machine Learning (ML) in drug research, without assuming any advanced background.

We’ll start by exploring how drugs are discovered, what AI and ML are, and why they’re becoming essential tools in modern pharmaceutical research. You’ll learn how computers can analyze huge amounts of data, from genes and proteins to chemical compounds, to identify promising drug candidates faster and more efficiently. Through real-world examples, you’ll see how AI helps predict how drugs behave in the body, discover new targets, and even design entirely new molecules.

You’ll also explore practical applications like virtual screening, protein structure prediction, drug repurposing, and personalized medicine. Along the way, we’ll discuss important topics like data quality, ethics, regulatory considerations, and the challenges of making AI work safely and effectively in healthcare.

By the end of this course, you’ll have a clear understanding of how AI and ML are transforming drug discovery, and you’ll gain confidence in navigating this cutting-edge field. Whether you’re a student, a professional, or simply curious about the future of medicine, this course will give you the insights and tools to see how technology is shaping the drugs of tomorrow.

Who this course is for:

  • This course is designed for anyone eager to understand how artificial intelligence is transforming the field of drug discovery and development. It will be especially valuable for students in biotechnology, life sciences, pharmacy, or computer science who want to build a strong foundation in the applications of AI in healthcare. Researchers and professionals in pharmaceuticals, biomedical sciences, or healthcare will also benefit from learning how AI can accelerate drug discovery and improve efficiency in clinical development. At the same time, tech enthusiasts and data scientists who are curious about real-world applications of AI in the biomedical domain will find this course highly relevant. Even beginners with no prior experience in AI or drug discovery are welcome, as the course is structured to introduce concepts in a clear and accessible way. Ultimately, this course is for anyone who wants to explore the exciting intersection of technology and medicine and gain insights into how AI is shaping the future of pharmaceuticals and personalized healthcare.

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