Multi-Level Fusion Graph Neural Network for Molecule Property Prediction
School of Mathematical Sciences, University of Electronic Science and Technology of China（电子科技大学数学科学学院）；College of Management Science, Chengdu University of Technology（成都理工大学管理科学学院）
Accurate prediction of molecular properties is essential in drug discovery and related fields. However, existing graph neural networks (GNNs) often struggle to simultaneously capture both local and global molecular structures. In this work, we propose a Multi-Level Fusion Graph Neural Network (MLFGNN) that integrates Graph Attention Networks and a novel Graph Transformer to jointly model local and global dependencies. In addition, we incorporate molecular fingerprints as a complementary modality and introduce a mechanism of interaction between attention to adaptively fuse information across representations. Extensive experiments on multiple benchmark datasets demonstrate that MLFGNN consistently outperforms state-of-the-art methods in both classification and regression tasks. Interpretability analysis further reveals that the model effectively captures task-relevant chemical patterns, supporting the usefulness of multi-level and multi-modal fusion in molecular representation learning.
MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization
Georgia Institute of Technology（佐治亚理工学院）；Analytics Center of Excellence, IQVIA（IQVIA卓越分析中心）；North Carolina State University（北卡罗来纳州立大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
Texas A&M University（德州农工大学）
分子性质预测因其广泛的应用而受到越来越多的关注。一个特别感兴趣和重要的任务是在没有3D平衡结构的情况下预测量子化学性质。这实际上是有利的，因为获得3D平衡结构需要极其昂贵的计算。在这项工作中，我们设计了一个深度图神经网络，通过直接从二维分子图学习来预测量子特性。此外，我们提出了一个3D图神经网络，从低成本的构象集，这可以得到一个负担得起的预算与开源工具学习。我们使用我们的方法参加了2021年KDD Cup on OGB大型挑战赛（OGB-LSC），该挑战赛旨在预测分子的HOMO-LUMO能隙。最终的评估结果表明，我们是赢家之一，在holdout测试集上的平均绝对误差为0.1235。我们的实现作为MoleculeX包的一部分提供（https：//github.com/divelab/MoleculeX）。
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules
School of Materials Science and Engineering, Shanghai Jiao Tong University（上海交通大学材料科学与工程学院）；Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University（上海交通大学张江高等研究院）；Materials Genome Initiative Center, Shanghai Jiao Tong University（上海交通大学材料基因组计划中心）
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）
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Pennsylvania State University（宾夕法尼亚州立大学）；Massachusetts Institute of Technology（麻省理工学院）；Stanford University（斯坦福大学）；Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Yale University（耶鲁大学）；HEC Montréal（蒙特利尔高等商学院）；Tencent AI Lab（腾讯人工智能实验室）
Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning
National Institute of Standards and Technology（美国国家标准与技术研究院）；University of Maryland（马里兰大学）
GeLLMO: Generalizing Large Language Models for Multi-property Molecule Optimization
Department of Computer Science and Engineering, The Ohio State University, USA（计算机科学与工程系，俄亥俄州立大学）；Translational Data Analytics Institute, The Ohio State University, USA（转化数据分析研究所，俄亥俄州立大学）；Department of Biomedical Informatics, The Ohio State University, USA（生物医学信息学系，俄亥俄州立大学）；College of Pharmacy, The Ohio State University, USA（药学院，俄亥俄州立大学）
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；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）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））
Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization
Department of Computer Science and Engineering, The Ohio State University, USA（计算机科学与工程系，俄亥俄州立大学）；Translational Data Analytics Institute, The Ohio State University, USA（转化数据分析研究所，俄亥俄州立大学）；Department of Biomedical Informatics, The Ohio State University, USA（生物医学信息学系，俄亥俄州立大学）；College of Pharmacy, The Ohio State University, USA（药学院，俄亥俄州立大学）
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）
Synergistic Benefits of Joint Molecule Generation and Property Prediction
Institute of AI for Health, Helmholtz Zentrum Munchen（人工智能与健康研究所，海德堡中心）；Technical University of Munich（慕尼黑技术大学）；School of Computation, Information and Technology（计算、信息与技术学院）；Faculty of Mathematics, Informatics and Mechanics, University of Warsaw（数学、信息学与力学学院，华沙大学）；Ardigen SA（Ardigen公司）；Eindhoven University of Technology（埃因霍温理工大学）
Department of Chemical Engineering, University of Michigan（美国密歇根大学化学工程系）；Michigan Institute for Data & AI in Society, University of Michigan（美国密歇根大学数据与社会人工智能研究所）；Department of Statistics, University of Michigan（美国密歇根大学统计系）
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；School of Biomedical Science, Hunan University（湖南大学生物医学科学学院）
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；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）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction
Institute of Cyberspace Security, Zhejiang University of Technology（浙江工业大学网络空间安全研究院）；Collaborative Innovation Center of Yangtze RiverDelta Region Green Pharmaceuticals（长三角绿色制药协同创新中心）
