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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

• High Efficiency: We provide LinearRNA, a highly efficient toolkit for RNA structure prediction and analysis. LinearFold & LinearPartition achieve O(n) complexity in RNA-folding prediction, which is hundreds of times faster than traditional folding techniques.

• Large-scale Representation 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 various representation learning algorithms and state-of-the-art large-scale pre-trained models to help developers start from "the shoulders of giants" quickly.

• Rich examples and applications: PaddleHelix provides frequently used components such as networks, datasets, and pre-trained models. Users can easily use those components to build up their models and systems. PaddleHelix also provides multiple applications, such as compound property prediction, drug-target interaction, and so on.

The installation prerequisites and guide can be found here.

• We provide abundant tutorials to help you navigate the repository and start quickly.
• PaddleHelix is based on PaddlePaddle, a high-performance Parallelized Deep Learning Platform.

• Representation Learning - Compounds
• Representation Learning - Proteins
• Drug-Target Interaction
• Molecular Generation
• Drug Drug Synergy
• LinearRNA

• Detailed API reference of PaddleHelix can be found here.

• If you need help in modifying the source code of PaddleHelix, please see our Guide for developers.