Data Science and AI: Predicting Toxicity in Small Molecules using MATLAB
To Know
About this Class
This one-hour online training introduces participants to predictive modeling techniques for evaluating the toxicity of small molecules using MATLAB. Participants will explore the principles of cheminformatics, learn how to preprocess molecular data, and build predictive models using machine learning. The training highlights MATLAB's specialized tools for feature extraction, model development, and performance evaluation tailored to small-molecule datasets. Designed for researchers and data scientists in fields such as drug discovery and toxicology, this session equips attendees with practical skills for leveraging AI in molecular analysis.
By the end of this training, attendees will be able to:
- Understand the role of data science and AI in predicting toxicity of small molecules
- Import and preprocess molecular data, including handling chemical descriptors and cleaning datasets
- Extract relevant features from molecular datasets to enhance model accuracy
- Develop and evaluate machine learning models, such as classification algorithms, for toxicity prediction
- Visualize model results and interpret performance metrics to assess predictive accuracy
- Utilize MATLAB’s built-in resources and toolboxes to further explore cheminformatics and predictive modeling
Basic familiarity with MATLAB and machine learning concepts is helpful but not required.