Generative AI & NLP
Lead Your Bioinformatics R&D with AI Confidence. This is an introductory course in AI specifically for Bioinformatics Professionals and Life Science Researchers, designed to help you understand and apply Machine Learning to drug discovery and genomic data. It covers core algorithms (KNN, Random Forest), Python fundamentals, and specific bioinformatics applications using the CHEMbL dataset. The course empowers you to prototype novel ML models through hands-on activities and a culminating capstone project focused on drug discovery.
Who is this for?
Bioinformatics Specialists, Life Science Researchers, R&D Scientists, and Pharmacologists who need to understand Machine Learning fundamentals to accelerate research, analyze large biological datasets, and guide AI-driven drug discovery projects.
Prerequisites:
No formal Machine Learning or AI prerequisites are listed, as the course focuses on foundational concepts and direct application within the Bioinformatics domain. Basic familiarity with a programming concept is helpful but not mandatory.
What You Will Achieve
- Apply core ML algorithms (KNN, Linear Regression, Random Forest) directly to solve bioinformatics problems.
- Master data-centric concepts for biological data, including data pre-processing and feature extraction from canonical SMILES.
- Gain strategic insight into the role of AI/ML in the Drug Discovery pipeline using industry-relevant datasets like CHEMbL.
- Become fluent in Python fundamentals (data structures, control flow, Pandas, Matplotlib) necessary for data science in R&D.
- Confidently build and evaluate classification and regression models, understanding key metrics to measure AI performance.
- Design, prototype, and present a capstone project where you apply all learned concepts to model drug activity on a specific protein.
Key Topics Covered
This 8-session, 2-hour-per-session curriculum (16 Total Hours) is structured around in-demand Life Science R&D applications:
- AI Fundamentals: Introduction to AI hierarchy, classification vs. regression, sentiment analysis, and metrics to measure AI performance.
- Python for Data Science: Data structures (functions, dictionaries), control flow, Pandas DataFrames, Matplotlib visualization, and an activity to create a basic chatbot.
- Core Machine Learning (ML): KNN, Linear Regression, Random Forest, and MLP (Multi-Layer Perceptron) with hands-on activities to build AIs for house price and bank churn prediction (for conceptual understanding).
- Data Preparation & Modules: Introduction to Python modules, data manipulation, and hyper-parameter tuning plots.
- Bioinformatics Application (Drug Discovery I): Introduction to Drug Discovery, CHEMbL dataset, data pre-processing for a select protein, and Feature Extraction from canonical SMILES.
- Advanced Bioinformatics ML: Introduction to scikit-learn, Dimensionality Reduction Techniques, and an activity to reduce the dimensions of the CHEMbL dataset.
- Capstone Application: Apply the complete prototype model developed to a different protein, followed by a Project Presentation and discussion.
Assessment & Certification
Assessment is based on hands-on coding activities (e.g., “Build an AI using KNN,” “Build an AI using linear regression”) and a final team-based capstone project focused on drug discovery. You will present your capstone project for peer feedback and discussion, emphasizing the communication and applied research aspects of AI.