Download Graph Generation for Drug Discovery Using Python and Keras Course
Graph Generation for Drug Discovery Using Python and Keras udemy video Course download, Python-based Graph Generation for Molecular Structures using Keras: A Practical Introduction to Neural Network Modeling.
What you’ll learn
- Understand the basics of graph generation and its applications in various fields.
- Learn how to manipulate molecular structures using the RDKit library in Python.
- Gain proficiency in preprocessing chemical data stored in CSV files.
- Develop an understanding of mapping atom symbols and bond types to numerical representations.
- Learn to convert SMILES strings into graph representations.
- Understand the concepts of Generative Adversarial Networks (GANs) and their application in graph generation.
- Implement a Graph Generator using TensorFlow and Keras to generate molecular graphs.
- Create a Discriminator model to evaluate the quality of generated graphs.
- Learn about the Wasserstein GAN framework for improved GAN training stability.
- Gain hands-on experience in training and fine-tuning GAN models for graph generation tasks.
- Understand the importance of GPU acceleration and how to configure it for faster computations.
- Develop the ability to save and load model weights for future use.
- Gain proficiency in generating molecular graphs using the trained GAN model.
- Learn to visualize and analyze the generated molecular structures.
Course content
- Introduction
- Model Generation, Training and Prediction
- Section 1: Introduction
- Lecture 1 Introduction
- Lecture 2 About this Project
- Lecture 3 Why Should we Learn?
- Lecture 4 Applications
- Lecture 5 Python, Keras, and Google Colab
- Section 2: Model Generation, Training and Prediction
- Lecture 6 Setup Working Directory
- Lecture 7 What is qm9.csv file?
- Lecture 8 What is code.ipynb?
- Lecture 9 Launch Code
- Lecture 10 Activate GPU
- Lecture 11 Mount Google Drive
- Lecture 12 Installing two Python libraries
- Lecture 13 Importing several libraries
- Lecture 14 Disabling the logging functionality
- Lecture 15 Loading Dataset
- Lecture 16 Process CSV file
- Lecture 17 Selects a specific SMILES string
- Lecture 18 Convert the SMILES string
- Lecture 19 Mapping atom symbols
- Lecture 20 Mapping bond types
- Lecture 21 Constants
- Lecture 22 Convert a SMILES string to a graph representation
- Lecture 23 Convert graph representation back into RDKit molecule object
- Lecture 24 Graph representation
- Lecture 25 Converting subset of SMILES data to graph tensors
- Lecture 26 Defines a generator model
- Lecture 27 Creates an instance of the GraphGenerator model
- Lecture 28 Defines a custom graph convolutional layer
- Lecture 29 Creates the discriminator model
- Lecture 30 Creates a discriminator model
- Lecture 31 Wasserstein Generative Adversarial Network
- Lecture 32 Sets up a WGAN
- Lecture 33 Training
- Lecture 34 Saving and loading the model weights
- Lecture 35 Sample molecules
- Lecture 36 Generating molecules
- Lecture 37 Displaying molecules
Course details
- Video quality: MP4 | Video: h264, 1280 × 720
- Audio quality: Audio: AAC, 44.1 KHz, 2 Ch
- Video duration: 1h 24m
- Number of lessons: 02 Section and 37 lectures
- Language of instruction: English
- Compressed file size: 370 MB
