Empowering Chemists with AI and ML: Transformative Tools and Techniques

DAY 1

Lecture 1: Introduction

• What is Artificial Intelligence (AI) and Machine Learning (ML)?
• When to apply AI and ML
• AI and ML in a Nutshell, further reading list.
• Importance of Data
• Data Sources and Databases

Lecture 2: AI/ML Workflow

• Introduction to Python packages for ML
• Introduction to Deepnote and Jupyter notebooks
• Numpy, Scipy, Scikit-learn
• Data structure with Pandas and Keras framework
• Visualization of results with Matplotlib
• Introduction to KNIME
• Introduction to AWS and cloud-based AI tools/MATLAB for ML

DAY 2

Lecture 3: Common Algorithms in Machine Learning Part 1

• Introduction to computational learning
• Supervised vs Unsupervised learning
• Introduction to ML Algorithms
• Classification vs Regression Algorithms
• Some common types of ML Algorithms
• Random Forest Algorithm
• Gaussian Process Regressions
• Deep Learning models
• Natural Language Processing models
• Neural Networks

Lecture 4: Common Algorithms in Machine Learning Part 2

• Deep Neural Networks
• Convolutional Neural Networks
• Graph Neural Networks
• Introduction to generative AI models

DAY 3

Lecture 5: Feature Engineering and Descriptor Design

• What is Feature Engineering
• Introduction to Descriptor Design
• Numerical vs Categorical Descriptors
• Introduction to Computational Chemistry
• Introduction to RDKit, Mopac, Gaussian, and Spartan
• Training set

Lecture 6: Case Studies: Applications of AI/ML in Chemistry

Featuring two of the following;

• Catalysis Example (Python boilerplates)
• Mass Spectroscopy Example (Python boilerplates)
• Glycosylation Example (MATLAB and Python boilerplates)
• Application of Generative AI in Chemistry (Python boilerplates)

• Understand the fundamentals of AI/ML and its tailored application to chemistry.
• Explore the diverse applications of AI/ML in different branches of chemistry, such as drug discovery, molecular design, reaction prediction, and spectroscopy analysis.
• Learn about commonly used AI/ML algorithms and tools in chemistry research.
• Implement AI/ML techniques to solve real-world chemistry problems.
• Develop critical thinking skills to evaluate the potential impact of AI/ML on the future of chemistry research and industry.

• Graduate students and researchers in chemistry and related fields seeking to enhance their knowledge and skills in AI/ML.
• Professionals working in pharmaceuticals, materials science, chemical engineering, and other industries interested in leveraging AI/ML for innovation.
• Educators and instructors looking to integrate AI/ML concepts into their chemistry curricula.
• Policymakers and regulators involved in shaping the future of AI/ML applications in chemistry.