Skip to content

Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

License

Notifications You must be signed in to change notification settings

chupvl/PaddleHelix

 
 

Repository files navigation

English | 简体中文


Version python version support os DOI

Latest News

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 PaddleFold.

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.


Introduction

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.

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.

Examples

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

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].

About

Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 57.4%
  • Jupyter Notebook 25.3%
  • C++ 16.4%
  • Other 0.9%