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About the developer

PaddlePaddle
320 Stars 58 Forks 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

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English | 简体中文


Version python version support os

Latest News

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 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]

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