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2021.06.17 PaddleHelix team won the 2nd place in the OGB-LCS KDD Cup 2021 PCQM4M-LSC track, predicting DFT-calculated HOMO-LUMO energy gap of molecules. Please refer to the solution for more details.

2021.05.20 PaddleHelix v1.0 released. 1) Update from static framework to dynamic framework; 2) Add new applications: molecular generation and drug-drug synergy.

2021.05.18 Paper "Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity" is accepted by KDD 2021. The code is available at here.

2021.03.15 PaddleHelix team ranks 1st in the ogbg-molhiv and ogbg-molpcba of OGB, predicting the molecular properties.

PaddleHelix is a bio-computing tools, taking advantage of machine learning approach, 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
• Vaccine Design
  • LinearRNA

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 wangfan04@baidu.com or fangxiaomin01@baidu.com.