English | 简体中文
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.
2022.01.07 PaddleHelix released the reproduction of AlphaFold 2 using PaddlePaddle in HelixFold.
2021.11.23 Paper "Multimodal Pre-Training Model for Sequence-based Prediction of Protein-Protein Interaction" is accepted by MLCB 2021. Please refer to paper and code for more details.
2021.10.25 Paper "Docking-based Virtual Screening with Multi-Task Learning" is accepted by BIBM 2021.
2021.09.29 Paper "Property-Aware Relation Networks for Few-shot Molecular Property Prediction" is accepted by NeurIPS 2021 as a Spotlight Paper. Please refer to PAR for more details.
2021.07.29 PaddleHelix released a novel geometry-level molecular pre-training model, taking advantage of the 3D spatial structures of the molecules. Please refer to GEM for more details.
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 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
- Vaccine Design
We also provide examples that implement various algorithms and show the methods running the algorithms:
- Pretraining
- Drug Discovery and Precision Medicine
- Vaccine Design
- Protein Structure Prediction
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.