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PaddleHelix is a machine-learning-based bio-computing framework aiming at facilitating the development of the following areas:
- Vaccine design
- Drug discovery
- Precision medicine
- We provide abundant tutorials to navigate the directory and start quickly.
- PaddleHelix is based on PaddlePaddle, a high-performance Parallelized Deep Learning Platform.
- Highly Efficent: We provide LinearRNA - highly efficient toolkit for mRNA vaccine development. LinearFold & LinearParitition achieves O(n) complexity in RNA-folding prediction, which is hundreds of times faster than traditional folding techniques.
- Large-scale Representation Learning and Transfer Learning: Self-supervised learning for molecule representations offers prospects of a breakthrough in tasks with limited annotation, including drug profiling, drug-target interaction, protein-protein interaction, RNA-RNA interaction, protein folding, RNA folding, and molecule design. PaddleHelix implements a variety of representation learning algorithms and state-of-the-art large-scale pre-trained models to help developers to start from "the shoulders of giants" quickly.
- Easy-to-use APIs: PaddleHelix provide frequently used structures and pre-trained models. You can easily use those components to build up your models and systems.