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
Molecular property prediction refers to the task of labeling molecules with some biochemical properties, playing a pivotal role in the drug discovery and design process. Recently, with the advancement of machine learning, deep learning-based molecular property prediction has emerged as a solution to the resource-intensive nature of traditional methods, garnering significant attention. Among them, molecular representation learning is the key factor for molecular property prediction performance. And there are lots of sequence-based, graph-based, and geometry-based methods that have been proposed. However, the majority of existing studies focus solely on one modality for learning molecular representations, failing to comprehensively capture molecular characteristics and information. In this paper, a novel multi-modal representation learning model, which integrates the sequence, graph, and geometry characteristics, is proposed for molecular property prediction, called SGGRL. Specifically, we design a fusion layer to fusion the representation of different modalities. Furthermore, to ensure consistency across modalities, SGGRL is trained to maximize the similarity of representations for the same molecule while minimizing similarity for different molecules. To verify the effectiveness of SGGRL, seven molecular datasets, and several baselines are used for evaluation and comparison. The experimental results demonstrate that SGGRL consistently outperforms the baselines in most cases. This further underscores the capability of SGGRL to comprehensively capture molecular information. Overall, the proposed SGGRL model showcases its potential to revolutionize molecular property prediction by leveraging multi-modal representation learning to extract diverse and comprehensive molecular insights. Our code is released at https://github.com/Vencent-Won/SGGRL.
Multimodal Molecular Pretraining via Modality Blending
Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；Harbin Institute of Technology（哈尔滨工业大学）；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）
Institute of AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
Multi-Modal Molecular Representation Learning via Structure Awareness
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyberspace Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Xidian University（西安电子科技大学）；Renmin University of China（中国人民大学）
Unified Molecule Pre-training with Flexible 2D and 3D Modalities: Single and Paired Modality Integration
Computational Bioscience Research Center, King Abdullah University of Science and Technology（国王阿卜杜勒·阿齐兹科技大学计算生物科学研究中心）；Institute for Infocomm Research, A*STAR（信息与通信研究机构，A*STAR）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算机与信息系统学院）
DMMRL: Disentangled Multi-Modal Representation Learning via Variational Autoencoders for Molecular Property Prediction
Guangxi Key Lab of Human-machine Interaction and Intelligent Decision（广西人机交互与智能决策重点实验室）；Nanning Normal University（南宁师范大学）；College of Big Data and Software Engineering（大数据与软件工程学院）；Zhejiang Wanli University（浙江万里大学）
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
Renmin University of China（中国人民大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The Chinese University of Hong Kong（香港中文大学）
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）
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。
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）
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）
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）
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）
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）
SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression
University of Notre Dame, Dept. of Computer Science；University of Notre Dame, Dept. of Chemistry；University of Notre Dame, Lucy Family Institute for Data \& Society Notre Dame Indiana USA；University of Notre Dame, Lucy Family Institute for Data \& Society
DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models
NLPR, MAIS, Institute of Automation（NLPR、MAIS、自动化研究所）；School of Artificial Intelligence（人工智能学院）；University of Chinese Academy of Sciences（中国科学院大学）；Alibaba Group（阿里巴巴集团）；Hupan Lab（华普实验室）；College of Intelligence and Computing（智能与计算学院）；National University of Singapore（新加坡国立大学）；Cancer Science Institute of Singapore（新加坡癌症科学研究所）
