PaddlePaddle/ PaddleHelix
About the developer
320 Stars 
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Apache License 2.0 
550 Commits 
7 Opened issues 
Description
Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集
Services available
Contributors list
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Latest News
2021.06.17PaddleHelix 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.
2021.05.20PaddleHelix v1.0 released. 1) Update from static framework to dynamic framework; 2) Add new applications: molecular generation and drug-drug synergy.
2021.05.18Paper "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.
2021.03.15PaddleHelix team ranks 1st in the ogbg-molhiv and ogbg-molpcba of OGB, predicting the molecular properties.
Introduction
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.
Resources
Application Platform
PaddleHelix platform provides the AI + biochemistry abilities for the scenarios of drug discovery, vaccine design and precision medicine.
Installation Guide
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.
Tutorials
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
Examples
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
Competition Solutions
PaddleHelix team participated in multiple competitions related to bio-computing. The solutions can be found here.
Guide for Developers
- 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.
Welcome to Join Us
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]