Molecular property prediction, crucial for early drug candidate screening and optimization, has seen advancements with deep learning-based methods. While deep learning-based methods have advanced considerably, they often fall short in fully leveraging 3D spatial information. Specifically, current molecular encoding techniques tend to inadequately extract spatial information, leading to ambiguous representations where a single one might represent multiple distinct molecules. Moreover, existing molecular modeling methods focus predominantly on the most stable 3D conformations, neglecting other viable conformations present in reality. To address these issues, we propose 3D-Mol, a novel approach designed for more accurate spatial structure representation. It deconstructs molecules into three hierarchical graphs to better extract geometric information. Additionally, 3D-Mol leverages contrastive learning for pretraining on 20 million unlabeled data, treating their conformations with identical topological structures as weighted positive pairs and contrasting ones as negatives, based on the similarity of their 3D conformation descriptors and fingerprints. We compare 3D-Mol with various state-of-the-art baselines on 7 benchmarks and demonstrate our outstanding performance.
Molecular Graph Contrastive Learning with Line Graph
School of Statistics and Mathematics, Central University of Finance and Economics（中央财经大学统计与数学学院）；School of Journalism, Communication University of China（中国传媒大学新闻学院）
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Institute of Genomics, Chinese Academy of Sciences（中国科学院北京基因组研究所）；Peking University（北京大学）
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University（浙江大学药学院药物信息学研究所）
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Computing, Macquarie University（麦考瑞大学计算学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院）
Local-Global Multimodal Contrastive Learning for Molecular Property Prediction
School of Mathematical Sciences（数学科学学院）；University of Electronic Science and Technology of China（电子科技大学）；Department of Computer Science and Engineer（计算机科学与工程系）；Oakland University（奥克兰大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）；College of Management Science（管理科学学院）；Chengdu University of Technology（成都理工大学）
DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
Institute for AI Industry Research（人工智能产业研究院）；Tsinghua University（清华大学）；Peking University（北京大学）；Institute of Automation（自动化研究所）；Chinese Academy of Science（中国科学院）
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval
Valence Labs；University of Toronto（多伦多大学）；Vector Institute（矢量研究所）；University of British Columbia（不列颠哥伦比亚大学）；Université de Montréal（蒙特利尔大学）；Mila- Quebec AI Institute（米拉-魁北克人工智能研究所）
Valid Property-Enhanced Contrastive Learning for Targeted Optimization & Resampling for Novel Drug Design
Department of Computer Science, University of North Carolina at Chapel Hill（北卡罗来纳大学 Chapel Hill 分校计算机科学系）；Radiation Medicine Centre, Bhabha Atomic Research Centre（博哈原子研究中心放射医学中心）；Homi Bhabha National Institute（霍米·巴布纳国家研究所）；IITB-Monash Research Academy, IIT Bombay（印度理工学院 Bombay 的 IITB-Monash 研究学院）；Department of Chemistry, Indian Institute of Technology Bombay（印度理工学院 Bombay 化学系）
Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning
School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学计算机科学与工程学院）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属同济医院）
Global Concept Explanations for Graphs by Contrastive Learning
Institute of Theoretical Informatics, Karlsruhe Insitute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany Institute of Nanotechnology, Karlsruhe Insitute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry
Institute of Cyberspace Security Zhejiang University of Technology Hangzhou, China vencent\；Institute of Cyberspace Security Zhejiang University of Technology Hangzhou, China
Beijing National Research Center for Information Science and Technology(BNRist)（北京信息科学与技术国家研究中心）；Department of Computer Science and Technology Tsinghua University（清华大学计算机科学与技术系）
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Standigm；Samsung Electronics（三星电子）；Samsung Advanced Institute of Technology (SAIT)（三星先进技术研究院）；AITRICS
Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph
National University of Singapore（新加坡国立大学）；University of Washington（华盛顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）
ChemCLIP: Bridging Organic and Inorganic Anticancer Compounds Through Contrastive Learning
Li Ka Shing Faculty of Medicine, The University of Hong Kong（香港大学李嘉诚医学院）；Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong（香港大学李嘉诚医学院诊断放射学系）；Department of Chemistry, The University of Hong Kong（香港大学化学系）
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
National University of Singapore（新加坡国立大学）；RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究所先进智能项目中心）；The University of Auckland（奥克兰大学）；The University of Melbourne（墨尔本大学）；The University of Tokyo（东京大学）
LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction
Baidu Inc.（百度公司）
在这份报告中，我们（SuperHaving团队）提出了我们对KDD Cup 2021-PCQM 4 M-LSC的解决方案，这是一个预测分子HOMO-LUMO间隙的大规模量子化学数据集。我们的解决方案Lite几何增强分子表征学习（LiteGEM）在深度图神经网络和各种自监督学习任务的帮助下，在测试集上实现了0.1204的平均绝对误差（MAE）。该框架的代码可以在https://github.com/PaddlePaddle/PaddleHelix/tree/dev/competition/kddcup2021-PCQM4M-LSC/中找到。
Medication Recommendation via Dual Molecular Modalities and Multi-Step Enhancement
School of Information Science and Engineering, Yanshan University（燕山大学信息科学与工程学院）；School of Information Science and Engineering, Xinjiang College of Science & Technology（新疆科技学院信息科学与工程学院）
