Graph Generation for Drug Discovery using Python and Keras

About this course

Are you curious about the world of molecular structures, drug discovery, and generative models? Look no further! This exciting course will take you on a journey through the fascinating field of graph generation and its real-world applications.

In this course, we will start by exploring the basics of molecular representations using SMILES notation and how to convert them into graph structures using the powerful RDKit library. You will learn how to handle and manipulate molecular data efficiently.

Next, we will dive into the realm of generative models, specifically GraphWGAN (Graph Wasserstein Generative Adversarial Network). You will gain an understanding of how GraphWGAN combines the power of generative adversarial networks (GANs) and graph neural networks (GNNs) to create realistic and diverse molecular graphs.

Throughout the course, we will build and train both the generator and discriminator models, learning how they work together to create new molecules that closely resemble real chemical compounds. As we progress, you will discover the art of hyperparameter tuning and optimizing the training process to achieve better results.

But the journey doesn't end there! We will explore various real-world applications of graph generation, particularly in drug discovery and materials science. You will witness how this cutting-edge technology is revolutionizing the pharmaceutical industry, accelerating the process of drug development, and contributing to groundbreaking research.

As we delve into the practical aspects of this course, you will gain hands-on experience working with TensorFlow, Keras, and other essential libraries, honing your skills in machine learning and data manipulation.

By the end of this course, you will be equipped with the knowledge and skills to tackle graph generation tasks independently. You will also have a portfolio of impressive projects

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