MATLAB provides a comprehensive environment for applying artificial intelligence (AI) to chemistry, enabling you to process, analyze, and model molecular and chemical data. You can use built-in machine learning and deep learning toolboxes to explore advanced algorithms, automate workflows, and speed up chemical research and discovery.
With MATLAB and Simulink, you can:
- Develop and train machine learning and deep learning models to predict molecular properties and chemical reactivity
- Apply graph neural networks for molecular structure analysis, node classification, and multi-label graph classification
- Automate data preprocessing, feature extraction, and model evaluation for chemical data sets
- Integrate AI-driven approaches to speed up drug discovery, materials design, and cheminformatics tasks
- Create and share educational resources to teach AI, machine learning, and deep learning concepts in chemistry
Classify Atoms in Molecules Using Graph Convolutional Neural Networks
Classify nodes in molecular graphs using deep learning. Explore step-by-step examples and apply graph convolutional networks in your chemistry research.
Identify Functional Groups Using Graph Attention Neural Networks
Discover how to use advanced graph attention mechanisms for multi-label classification of chemical compounds. Explore step-by-step examples that provide valuable insights into applying deep learning techniques in cheminformatics.
Large Language Models for SMILES-Based Molecule Prediction in MATLAB
Use external pretrained models (for example, from PyTorch®) in MATLAB for masked molecule prediction and chemical property analysis.
Protein Feature Selection and Classification for Cancer Diagnosis
Identify key protein biomarkers and classify profiles for cancer diagnosis using MATLAB. Advance your bioinformatics and proteomics research.
Products
Learn about the products used for AI applications in chemistry.
