bioinformatic

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In this tutorial series, you’ll learn how to use DeepChem to solve interesting and challenging problems in the life sciences. The tutorial acts as a introduction to DeepChem as well as application of DeepChem to a wide array of problems across domains like molecular machine learning, quantum chemistry, bioinformatics and material science. This tutorial series is continually updated with new DeepChem features and models as implemented and is designed to be accessible to beginners.

Learn how to use PyTorch, Monai, and Python for computer vision using machine learning. One practical use-case for artificial intelligence is healthcare imaging. In this course, you will improve your machine learning skills by creating an algorithm for automatic liver segmentation. Link

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TorchDrug is a machine learning platform designed for drug discovery, covering techniques from graph machine learning (graph neural networks, geometric deep learning & knowledge graphs), deep generative models to reinforcement learning. It provides a comprehensive and flexible interface to support rapid prototyping of drug discovery models in PyTorch. Link

It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.