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2022.07.29 PaddleHelix released the codes of HelixFold-Single, an MSA-free protein structure prediction pipeline relying on only the primary sequences, which can predict the protein structures within seconds. Please refer to paper and codes for more details. Welcome to PaddleHelix website to try out the structure prediction online service.

2022.07.18 PaddleHelix fully released HelixFold including training and inference pipeline. The complete training time are optimized from 11 days to 7.5 days. Please refer to paper and codes for more details.

2022.07.07 Paper "BatchDTA: implicit batch alignment enhances deep learning-based drug–target affinity estimation" is published in Briefings in Bioinformatics. Please refer to paper and codes for more details.

2022.05.24 Paper "HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer" is published in Bioinformatics. Refer to paper for more information.

2022.02.07 Paper "Geometry-enhanced molecular representation learning for property prediction" is published in Nature Machine Intelligence. Please refer to paper and codes to explore the algorithm.

PaddleHelix is a bio-computing tool, taking advantage of the machine learning approaches, especially deep neural networks, for facilitating the development of the following areas:

• Drug Discovery. Provide 1) Large-scale pre-training models: compounds and proteins; 2) Various applications: molecular property prediction, drug-target affinity prediction, and molecular generation.
• Vaccine Design. Provide RNA design algorithms, including LinearFold and LinearPartition.
• Precision Medicine. Provide application of drug-drug synergy.

PaddleHelix platform provides the AI + biochemistry abilities for the scenarios of drug discovery, vaccine design and precision medicine.

PaddleHelix is a bio-computing repository based on PaddlePaddle, a high-performance Parallelized Deep Learning Platform. The installation prerequisites and guide can be found here.

We provide abundant tutorials to help you navigate the repository and start quickly.

• Drug Discovery
  • Compound Representation Learning and Property Prediction
  • Protein Representation Learning and Property Prediction
  • Predicting Drug-Target Interaction: GraphDTA, MolTrans
  • Molecular Generation
• Vaccine Design
  • Predicting RNA Secondary Structure

We also provide examples that implement various algorithms and show the methods running the algorithms:

• Pretraining
  • Representation Learning - Compounds
  • Representation Learning - Proteins
• Drug Discovery and Precision Medicine
  • Drug-Target Interaction
  • Molecular Generation
  • Drug Drug Synergy
  • Few-shot Molecular Property Prediction
• Vaccine Design
  • LinearRNA
• Protein Structure Prediction
  • HelixFold
  • HelixFold-Single

PaddleHelix team participated in multiple competitions related to bio-computing. The solutions can be found here.

• To develope new functions based on the source code of PaddleHelix, please refer to guide for developers.
• For more details of the APIs, please refer to the documents.

We are looking for machine learning researchers / engineers or bioinformatics / computational chemistry researchers interested in AI-driven drug design. We base in Shenzhen or Shanghai, China. Please send the resumes to [email protected] or [email protected].