Molecular Property Prediction (MPP) plays a pivotal role across diverse domains, spanning drug discovery, material science, and environmental chemistry. Fueled by the exponential growth of chemical data and the evolution of artificial intelligence, recent years have witnessed remarkable strides in MPP. However, the multifaceted nature of molecular data, such as molecular structures, SMILES notation, and molecular images, continues to pose a fundamental challenge in its effective representation. To address this, representation learning techniques are instrumental as they acquire informative and interpretable representations of molecular data. This article explores recent AI/-based approaches in MPP, focusing on both single and multiple modality representation techniques. It provides an overview of various molecule representations and encoding schemes, categorizes MPP methods by their use of modalities, and outlines datasets and tools available for feature generation. The article also analyzes the performance of recent methods and suggests future research directions to advance the field of MPP.
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）
PolyFusionAgent: A Multimodal Foundation Model and Autonomous AI Assistant for Polymer Property Prediction and Inverse Design
Department of Applied Computer Science, The University of Winnipeg（应用计算机科学系，温尼伯大学）；Department of Mechanical Engineering, University of Manitoba（机械工程系，曼尼托巴大学）
MOLAR: Learning Multimodal Molecular Representations from Noisy Labels
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Zhengzhou University（郑州大学）；The Education University of Hong Kong（香港教育大学）；The Chinese University of Hong Kong（香港中文大学）；Weizmann Institute of Science（魏茨曼科学研究所）
Multimodal machine learning with large language embedding model for polymer property prediction
Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States（材料科学与工程系，德雷克塞尔大学）；Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States（化学系，宾夕法尼亚大学）
Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning
Institute of Computing Technology, Chinese Academy of Science（中国科学院计算技术研究所）；Ningbo Institute of Information Technology Application, Chinese Academy of Sciences (CAS)（中国科学院宁波信息技术应用研究院）
Multimodal Foundation Models for Material Property Prediction and Discovery
Massachusetts Institute of Technology（麻省理工学院）；John Hopkins University Applied Physics Laboratory（约翰霍普金斯大学应用物理实验室）；University of Chicago（芝加哥大学）；Technical University of Denmark（丹麦技术大学）
Multimodal Federated Learning in Healthcare: a Review
West Virginia University（西弗吉尼亚大学）；Tribhuvan University（特里布万大学）；University at Albany（奥尔巴尼大学）；University of Pittsburgh（匹兹堡大学）；University of Aberdeen（阿伯丁大学）
ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems
Zhejiang University,\ University - Ant \ Joint Laboratory of Knowledge Graph Hangzhou China；Zhejiang University,\ University - Ant \ Joint Laboratory of Knowledge Graph
Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions
School of Biomedical Engineering, Tsinghua University（清华大学生物医学工程学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University（清华大学临床医学院北京清华长庚医院肝胆胰中心）；School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；College of Electronic Information Engineering, Tongji University（同济大学电子与信息工程学院）
ProtoMol: Enhancing Molecular Property Prediction via Prototype-Guided Multimodal Learning
MBZUAI（穆扎芬人工智能研究所）；University of Zhengzhou（郑州大学）；HKUST（香港科技大学）；University of Aberdeen（爱丁堡大学）；MBZUAI, Weizmann Institute of Science（穆扎芬人工智能研究所、威斯曼科学研究所）
MultiPUFFIN: A Multimodal Domain-Constrained Foundation Model for Molecular Property Prediction of Small Molecules
Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学化学工程系）；Faculty of Industrial Engineering, KU Leuven（鲁文大学工业工程学院）；University of Surrey（萨里大学）
GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction
Department of Computer Science, University of Southern California（南加州大学计算机科学系）；Department of Quantitative and Computational Biology, University of Southern California（南加州大学定量与计算生物学系）；Amazon（亚马逊）；Department of Medical Biochemistry and Biophysics, Science for Life Laboratory, Karolinska Institutet（卡罗林斯卡学院医学生物化学与生物物理系，生命科学实验室）
Zhejiang University（浙江大学）；East China Normal University（华东师范大学）
Deep Learning has implemented a wide range of applications and has become increasingly popular in recent years. The goal of multimodal deep learning (MMDL) is to create models that can process and link information using various modalities. Despite the extensive development made for unimodal learning, it still cannot cover all the aspects of human learning. Multimodal learning helps to understand and analyze better when various senses are engaged in the processing of information. This paper focuses on multiple types of modalities, i.e., image, video, text, audio, body gestures, facial expressio…
From Efficient Multimodal Models to World Models: A Survey
Fudan University（复旦大学）；Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）；Shanghai Engineering Research Center of AI and Robotics（上海人工智能与机器人工程研究中心）；Engineering Research Center of AI and Robotics, Ministry of Education（教育部人工智能与机器人工程研究中心）；Shanghai Key Lab of Intelligent Information Processing（上海智能信息处理重点实验室）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）
MIPS: a Multimodal Infinite Polymer Sequence Pre-training Framework for Polymer Property Prediction
Department of Electronic Engineering, Tsinghua University Beijing China；Zhongguancun Institute of Artificial Intelligence Beijing China；Department of Electronic Engineering \& College of AI, Tsinghua University Beijing National Research Center for Information Science；Department of Electronic Engineering, Tsinghua University；Zhongguancun Institute of Artificial Intelligence
FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data
Nanjing University of Science and Technology（南京理工大学）；National University of Singapore（新加坡国立大学）；Institute of High-Performance Computing, A*STAR（新加坡高性能计算研究院（新加坡科研局下属机构））
