Data Driven Research: in Biology and chemistry( AI)
Master the integration of Large Language Models and Deep Learning to accelerate molecular discovery and biological insights. Bridge the gap between experimental data and predictive modeling using industry-standard AI-driven research frameworks.
Course Description
The scientific landscape is undergoing a massive shift from traditional observation to Data-Driven Research powered by Artificial Intelligence. This course provides a comprehensive roadmap for applying Machine Learning (ML) and Generative AI across the biological and chemical sciences. You will explore how Neural Networks are used to predict protein folding, simulate complex chemical reactions, and analyze high-throughput multi-omics data. The curriculum emphasizes the use of AI-accelerated workflows to manage the "Big Data" generated by modern lab instruments, turning noise into actionable hypotheses. From Chemoinformatics to Systems Biology, students will learn to deploy Deep Learning models that optimize experimental design and reduce R&D timelines. By blending theoretical foundations with hands-on Python simulations, this course equips you with the skills to navigate the 2026 research ecosystem. Whether you are developing novel catalysts or identifying new therapeutic targets, you will master the predictive analytics necessary to lead at the intersection of life sciences and computational intelligence.
What You'll Learn
The architecture of AI-driven research pipelines in drug discovery and genomics.
How to use Generative Adversarial Networks (GANs) for de novo molecular design.
Techniques for processing high-dimensional biological data using Dimensionality Reduction.
Application of Large Language Models (LLMs) for scientific literature mining and hypothesis generation.
Strategies for training Random Forest and SVM models on chemical property datasets.
Best practices for Data Wrangling and cleaning noisy experimental biological data.
Curriculum
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The architecture of AI-driven research pipelines in drug discovery and genomics.
Lesson -
How to use Generative Adversarial Networks (GANs) for de novo molecular design.
Lesson -
Techniques for processing high-dimensional biological data using Dimensionality Reduction.
Lesson -
Application of Large Language Models (LLMs) for scientific literature mining and hypothesis generation.
Lesson -
Strategies for training Random Forest and SVM models on chemical property datasets.
Lesson -
Best practices for Data Wrangling and cleaning noisy experimental biological data.
Lesson
