Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. To properly represent molecules, graph contrastive learning is a promising paradigm as it utilizes self-supervision signals and has no requirements for human annotations. However, prior works fail to incorporate fundamental domain knowledge into graph semantics and thus ignore the correlations between atoms that have common attributes but are not directly connected by bonds. To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning. KCL framework consists of three modules. The first module, knowledge-guided graph augmentation, augments the original molecular graph based on the Chemical Element KG. The second module, knowledge-aware graph representation, extracts molecular representations with a common graph encoder for the original molecular graph and a Knowledge-aware Message Passing Neural Network (KMPNN) to encode complex information in the augmented molecular graph. The final module is a contrastive objective, where we maximize agreement between these two views of molecular graphs. Extensive experiments demonstrated that KCL obtained superior performances against state-of-the-art baselines on eight molecular datasets. Visualization experiments properly interpret what KCL has learned from atoms and attributes in the augmented molecular graphs. Our codes and data are available at https://github.com/ZJU-Fangyin/KCL.
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）
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）
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）
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）
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）
MolCAP: Molecular Chemical reActivity pretraining and prompted-finetuning enhanced molecular representation learning
School of Software, Shandong University（山东大学软件学院）；Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University（山东大学联合SDU-NTU人工智能研究中心（C-FAIR））
Generating High-Precision Force Fields for Molecular Dynamics Simulations to Study Chemical Reaction Mechanisms using Molecular Configuration Transformer
Peking University（北京大学）；Shenzhen Bay Laboratory（深圳湾实验室）；Changping Laboratory（昌平实验室）
Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning
Nostrum Biodiscovery；Barcelona Supercomputing Center（巴塞罗那超级计算中心）；Faculty of Pharmacy and Food Sciences, University of Barcelona（巴塞罗那大学药学与食品科学系）；Catalan Institution for Research and Advanced Studies (ICREA)（加泰罗尼亚研究与高级研究机构）；Data Science Dpt., Almirall S.A.（Almirall S.A.数据科学部门）
Physical probes expose and alleviate chemical-environment collapse in molecular representations
Hainan Institute, Zhejiang University（浙江大学海南研究院）；Institute of Marine Biology and Pharmacology, Ocean College, Zhejiang University（浙江大学海洋学院海洋生物学与药理研究所）；School of Food and Pharmacy, Zhejiang Ocean University（浙江海洋大学食品学院）；Hangzhou Bio-Sincerity Pharmaceutical Technology Company Limited（杭州生物 sincerity 药业技术有限公司）；School of Pharmaceutical Sciences, Shanghai Jiao Tong University（上海交通大学药学院）
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning
Kakao Corporation（Kakao公司）；LG Chem（LG化学）；Seoul National University（首尔国立大学）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））；Seoul National University of Science and Technology（首尔国立科技大学）
MolSculpt: Sculpting 3D Molecular Geometries from Chemical Syntax
AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, China（人工智能科学（AI4S）优选计划，北京大学深圳研究生院，中国）；Faculty of Materials Science, Shenzhen MSU-BIT University, Shenzhen, China（材料科学学院，深圳MSU-BIT大学，中国）
MolecularIQ: Characterizing Chemical Reasoning Capabilities Through Symbolic Verification on Molecular Graphs
ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University, Linz, Austria（林茨ELLIS单元和LIT人工智能实验室，机器学习研究所，约翰·凯撒大学，林茨，奥地利）；Clinical Research Institute Medical Artificial Intelligence, Johannes Kepler University, Linz, Austria（医学人工智能临床研究研究所，约翰·凯撒大学，林茨，奥地利）
Representational Alignment with Chemical Induced Fit for Molecular Relational Learning
Wuhan University of Technology（武汉理工大学）；Yonsei University（延世大学）；Hubei Key Laboratory of Transportation Internet of Things（湖北省交通运输物联网重点实验室）；Dalian University（大连大学）
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction
Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University（清华大学交叉信息研究院）；Zhejiang Lab（之江实验室）；The Chinese University of Hong Kong（香港中文大学）
KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledge
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, China（人工智能安全国家重点实验室，计算技术研究所，中国科学院，中国）；University of Chinese Academy of Sciences (CAS), China（中国科学院大学（中国科学院））
