This lab is designed to provide an interactive learning experience for upper division undergraduate students interested in exploring the applications of machine learning (ML) in computational drug discovery.
The lab consists of five main modules, each focusing on different aspects of computational drug discovery using ML techniques. The modules cover the following topics:
- Fundamentals of Computational Drug Discovery
- Drug Property Prediction and Optimization
- Drug-Target Interaction and Molecular Modeling
- Drug Repurposing and Combination Therapy
- Toxicity Prediction and Adverse Effect Analysis
By engaging with the lab, students will:
- Understand the fundamental concepts and techniques used in computational drug discovery.
- Gain hands-on experience with ML models for predicting drug properties, interactions, and toxicity.
- Develop insights into the applications of ML in various stages of the drug discovery pipeline.
- Enhance their critical thinking and problem-solving skills through guided exercises and case studies.
To access the lab, simply navigate to the provided URL in your web browser. The lab is compatible with modern web browsers and can be accessed from various devices.
The lab is organized into the following sections:
- Introduction and Overview: Provides a brief introduction to the lab's purpose, key concepts, and learning objectives.
- Modules: Contains separate sections for each module, with lessons covering theoretical concepts and practical exercises.
- Case Studies and Projects: Includes real-world case studies and projects to apply the learned concepts and techniques.
- Additional Resources: Provides links to relevant research papers, tutorials, and external resources for further exploration.
To begin your learning journey, navigate to the desired module using the menu on the lab's homepage.