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<!doctype html> <html> <head> <meta charset="utf-8"> <meta name="renderer" content="webkit" /> <meta name="format-detection" content="telephone=no"/> <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" /> <meta content="width=device-width, initial-scale=1, maximum-scale=1, minimum-scale=1" name="viewport" /> <title>Baidu Research</title> <link rel="shortcut icon" href="../web/images/favicon.png" type="image/x-icon"> <link rel="stylesheet" href="../web/css/reset.css"> <link rel="stylesheet" href="../web/css/animate.css"> <link rel="stylesheet" href="../web/css/Bootstrap.css"> <link rel="stylesheet" href="../web/css/style.css"> <link rel="stylesheet" href="../web/css/media.css?3333"> <script src="../web/js/jquery.min.js"></script> </head> <body class="n_body" ondragstart="return false">  <div class="n_header"> <div class="container"> <div class="header01"> <div class="logo"> <a class="h_logo" href="/Index" style="background-image: url(../web/images/logo.png);"><img src="../web/images/logo.png" alt="Baidu Research"></a> </div> <div class="nav"> <ul> <li><a href="/Index">Home</a></li> <li><a href="/Publications">Publications</a></li> <li><a href="/Research_Areas?id=55">Research Areas</a> <div class="nav_er"> <ul class="div_dl "> <li><a href="/Research_Areas/index-view?id=55">Data Science and Data Mining</a></li> <li><a href="/Research_Areas/index-view?id=56">Natural Language and Speech</a></li> <li><a href="/Research_Areas/index-view?id=57">Business Intelligence</a></li> <li><a href="/Research_Areas/index-view?id=58">Robotics and Autonomous Driving</a></li> <li><a href="/Research_Areas/index-view?id=59">Computer Vision</a></li> <li><a href="/Research_Areas/index-view?id=60">Machine Learning and Deep Learning</a></li> <li><a href="/Research_Areas/index-view?id=61">Computational Biology and Bioinformatics</a></li> <li><a href="/Research_Areas/index-view?id=62">High Performance Computing</a></li> <li><a href="/Research_Areas/index-view?id=75">Quantum Computing</a></li> </ul> </div> </li> <li><a class="active" href="/Blog">Blog</a></li> <li><a href="/Career">Careers</a></li> <li><a href="/Downloads">Downloads</a></li> <li><a href="/AI_Colloquium">AI Colloquium</a></li> </ul> <div class="nav_btn"><span></span></div> </div> </div> </div> <div class="header03 "> <div class="logo"><a href="/Index"><img src="../web/images/logo.png" alt="Baidu Research"></a></div> <div class="nav"> <ul> <li><a href="/Index">Home</a></li> <li><a href="/Publications">Publications</a></li> <li><a href="/Research_Areas?id=55">Research Areas</a> <div class="nav_er"> <ul class="div_dl "> <li><a href="/Research_Areas/index-view?id=55">Data Science and Data Mining</a></li> <li><a href="/Research_Areas/index-view?id=56">Natural Language and Speech</a></li> <li><a href="/Research_Areas/index-view?id=57">Business Intelligence</a></li> <li><a href="/Research_Areas/index-view?id=58">Robotics and Autonomous Driving</a></li> <li><a href="/Research_Areas/index-view?id=59">Computer Vision</a></li> <li><a href="/Research_Areas/index-view?id=60">Machine Learning and Deep Learning</a></li> <li><a href="/Research_Areas/index-view?id=61">Computational Biology and Bioinformatics</a></li> <li><a href="/Research_Areas/index-view?id=62">High Performance Computing</a></li> <li><a href="/Research_Areas/index-view?id=75">Quantum Computing</a></li> </ul> </div> </li> <li><a href="/Blog">Blog</a></li> <li><a href="/Career">Careers</a></li> <li><a href="/Downloads">Downloads</a></li> <li><a href="/AI_Colloquium">AI Colloquium</a></li> </ul> </div> <div class="nav_btn"><span></span></div> </div> </div> <div class="baidu-page-banner blog-side" style="background: url(/Public/uploads/5ae96c0a7676c.png);"> <div class="container"> <div class="baidu-page-title wow fadeIn">Blog</div> </div> </div> <div class="content-info"> <div class="container-details-er"> <div class="blog-details-title">PaddlePaddle’s Graph Neural Networks Advance Drug Design</div> <div class="blog-details-date"><p>2021-03-25</p><a href="/Blog">Back to list</a></div> <p style="font-size: 16px; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; text-align: justify; color: rgb(0, 0, 0);">Compound screening to find molecules with the desired biological activity is of great importance to drug design. Traditional screening methods require synthesis and the biological testing of large compound collections, which can turn into a costly and time-consuming process with a low success rate. Machine learning, particularly graph neural networks, has the potential to replace traditional methods by enabling AI assisted virtual screening. This will further speed intermediate steps and significantly reduce R&D costs.<br/></span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-size: 16px; font-family: arial, helvetica, sans-serif; color: rgb(0, 0, 0);"> </span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-size: 16px; font-family: arial, helvetica, sans-serif; color: rgb(0, 0, 0);"></span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><a></a></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><a></a><a></a><a style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(12, 12, 12); text-decoration: none;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(12, 12, 12);">We are excited to announce that our AI models, trained with Baidu’s open-source deep learning platform PaddlePaddle, ranked first on a well-recognized benchmark leaderboard for molecular property predictions. All algorithms and pretrained models stem from </span></a><a href="https://github.com/PaddlePaddle/PGL" style="text-decoration: underline; font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">Paddle Graph Learning (PGL)</span></a><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; text-decoration: none;">, <span style="font-family: arial, helvetica, sans-serif; font-size: 16px; text-decoration: none; color: rgb(12, 12, 12);">a graph learning framework, and </span></span><a href="https://github.com/PaddlePaddle/PaddleHelix" style="text-decoration: underline; font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">PaddleHelix</span></a><span style="text-decoration: none; font-size: 15px; font-family: Calibri, sans-serif;">, <span style="text-decoration: none; font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(12, 12, 12);">a machine learning bio-computing framework. </span></span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; text-decoration: none; color: rgb(0, 0, 0);"> </span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">The HIV and PCBA datasets from </span><a href="https://ogb.stanford.edu/docs/leader_graphprop/" style="text-decoration: underline; font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">Open Graph Benchmark (OGB)</span></a><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">, a set of benchmark datasets aiming to facilitate graph learning research, are among the world’s largest benchmarks for molecular property prediction. The HIV task is to predict whether a compound inhibits HIV virus replication or not. The PCBA task is to classify compounds based on their effectiveness against over 100 disease targets. For example, compounds that can increase expression of functional SMN2 protein can alleviate spinal muscle atrophy caused by a mutation of SMN1 protein.</span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-size: 16px; font-family: arial, helvetica, sans-serif; color: rgb(0, 0, 0);"><br/></span></p><p style="text-align:center"><img src="/ueditor/upload/20210324/1616555324671664.png" title="1616555324671664.png" alt="leaderboard.png" width="2783" height="676"/></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">To tackle the challenge, our researchers trained a deep graph neural network to learn molecular chemical representation through a self-supervised learning task. Additionally, they took the representation to train a molecule property prediction classifier. </span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);"> </span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">Molecular representation learning</span></strong></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);"> </span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">In the HIV task, the first step is to learn the molecule chemical representation with the graph neural network. OGB provides graph representations of molecules where nodes are atoms, and edges are chemical bonds alongside atom features. However, we found that these features could not represent the chemical information of molecules without considering domain knowledge.</span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);"> </span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">Our researchers trained a graph neural network to learn molecular representation by integrating its chemical properties, such as fingerprints, the presence of absence of particular chemical substructures. This novel representation learning method plays a critical role in achieving the SOTA results on the OGB leaderboard for HIV.</span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);"> </span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">Graph learning technology</span></strong></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);"> </span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">In the PCBA task, our researchers managed to improve the performance by integrating GINE plus and the APPNP algorithm based on PGL without adding extra model parameters. APPNP algorithm can utilize the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank.</span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);"> </span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">What is PGL and PaddleHelix?</span></strong></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);"> </span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">PGL is an efficient and flexible graph learning framework based on PaddlePaddle, which was open sourced in 2019. Its latest version 2.0 supports dynamic (computational) graphs and large-scale graphs. Developers can use PGL to efficiently build graph neural networks for industrial applications, ranging from recommendation systems to search engines, finance, maps, security risk control, and biomedicine.</span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);"> </span></p><p style="font-size: medium; font-family: 宋体; white-space: normal; text-align: justify;"><span style="font-family: arial, helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0);">PaddleHelix is a machine learning bio-computing framework built upon PaddlePaddle, aiming to facilitate the development of vaccine design, drug discovery, and precision medicine.Open sourced in December, 2020, PaddleHelix currently provides pretrained models including representation learning for compounds and proteins, LinearRNA, drug-target interaction, and ADMET modeling. With PaddleHelix, we are aiming to provide an industry-facing bio-computing ecosystem and services in the future.</span></p><p><br/></p> <div class="pager"> <a href="/Blog/index-view?id=156"> <i class="glyphicon glyphicon-menu-up"></i>Previous One：Baidu Releases PaddlePaddle 2.0 with New Features Including Dynamic Graphs, Reorganized APIs and Heterogeneous Parameter Server </a> <a href="/Blog/index-view?id=154"> <i class="glyphicon glyphicon-menu-down"></i>Next One：Call For Papers! 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