AI Drug Design Courses and Workshops 

This course provides a comprehensive introduction to the application of artificial intelligence (AI) in
drug design and discovery. Students will explore the key principles of AI, machine learning, and deep
learning as they apply to molecular modeling, virtual screening, and the development of new
therapeutics. The course includes hands-on Python labs, where students will implement AI techniques
to solve real-world drug discovery problems.

This course provides an in-depth exploration of advanced neural network architectures and AI techniques applied to drug discovery. The course emphasizes practical applications, including molecular property prediction, drug-target interaction prediction, de novo drug design, and optimization. The students will gain hands-on experience with cutting-edge tools and technologies, and they will learn to apply AI-driven methods to accelerate the drug discovery process

This course introduces students to calculus concepts with a focus on their applications in AI. Each week, students will participate in Python labs to solidify their understanding of calculus and explore its real-world applications in AI.

This course delves into the use of advanced neural network techniques for integrating multi-modal data sources in drug discovery. Students will explore how to combine diverse data types (e.g., molecular structures, genomic data, clinical data) to enhance drug discovery processes. The course emphasizes practical applications and hands-on experience with state-of-the-art neural network models and integration techniques.

This course focuses on integrating different neural network approaches to address various challenges in drug discovery and design. Students will explore how to combine neural network techniques with other computational methods to enhance drug discovery processes. The course covers molecular modeling, predictive analytics, and advanced deep learning architectures, emphasizing practical applications and hands-on experience.

This course explores the application of neural networks in the field of drug design and drug discovery. Students will learn the fundamental principles of neural networks, understand various neural network architectures, and apply these models to real-world problems in drug discovery, such as predicting molecular properties, optimizing drug candidates, and identifying potential drug-target interactions.

This course provides an in-depth understanding of organic chemistry principles with a focus on their application in drug design. Each week, students will engage in Python labs to explore how AI can be used to predict, model, and design new drug molecules.

This course covers fundamental physics concepts with a focus on their applications in AI. Each week, students will engage in Python labs to apply physics principles and explore their real-world implications in AI.

This course provides a thorough introduction to statistics and probability theory with a focus on applications in Artificial Intelligence (AI) and Machine Learning (ML). Students will learn fundamental concepts and techniques essential for understanding and developing AI models. Each week includes lectures on theoretical concepts accompanied by hands-on Python labs where students will apply these concepts using real-world datasets and popular libraries such as NumPy, pandas, SciPy, and scikit-learn.