On the Scalability of GNNs for Molecular Graphs

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On the Scalability of GNNs for Molecular Graphs

17 April 2024

Maciej Sypetkowski

Frederik Wenkel

Farimah Poursafaei

Dominique Beaini

ArXiv (abs)PDF HTML HuggingFace (1 upvotes)

Papers citing "On the Scalability of GNNs for Molecular Graphs"

16 / 16 papers shown

Title
A Matter of Representation: Towards Graph-Based Abstract Code Generation Nyx Iskandar Hisham Bedri Andy Tsen 72 0 0 15 Oct 2025
Scaling Laws and Symmetry, Evidence from Neural Force Fields Khang Ngo Siamak Ravanbakhsh 48 0 0 10 Oct 2025
MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder Xingtong Yu Chang Zhou Xinming Zhang Yuan Fang AI4CE 80 0 0 08 Oct 2025
EARL: Efficient Agentic Reinforcement Learning Systems for Large Language Models Zheyue Tan Mustapha Abdullahi Tuo Shi Huining Yuan Zelai Xu Chao Yu Boxun Li Bo Zhao OffRL 85 0 0 07 Oct 2025
Molecular Machine Learning in Chemical Process Design Jan G. Rittig Manuel Dahmen Martin Grohe Philippe Schwaller Alexander Mitsos 205 1 0 28 Aug 2025
Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures Vijay Prakash Dwivedi Charilaos I. Kanatsoulis Shenyang Huang Jure Leskovec GNN 3DV 181 5 0 19 Jun 2025
Descriptor-based Foundation Models for Molecular Property Prediction Jackson Burns Akshat Zalte William Green 105 3 0 18 Jun 2025
Backward Oversmoothing: why is it hard to train deep Graph Neural Networks? Nicolas Keriven 158 0 0 22 May 2025
Virtual Cells: Predict, Explain, Discover Emmanuel Noutahi Jason Hartford Prudencio Tossou Shawn T. Whitfield Alisandra K. Denton ... Emmanuel Bengio Dominique Beaini Christopher Gibson Daniel Cohen Berton Earnshaw 339 3 0 20 May 2025
CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning Mingyu Lu Ethan Weinberger Chanwoo Kim Su-In Lee 247 1 0 16 May 2025
Relational Graph Transformer Vijay Prakash Dwivedi Sri Jaladi Yangyi Shen Federico López Charilaos I. Kanatsoulis Rishi Puri Matthias Fey Jure Leskovec 316 4 0 16 May 2025
Scaling Laws of Graph Neural Networks for Atomistic Materials ModelingDesign Automation Conference (DAC), 2025 Chaojian Li Zhifan Ye Massimiliano Lupo Pasini Jong Youl Choi Cheng Wan Y. Lin Dali Wang 215 2 0 10 Apr 2025
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks Maya Bechler-Speicher Ben Finkelshtein Fabrizio Frasca Luis Muller Jan Tönshoff ... Michael M. Bronstein Mathias Niepert Bryan Perozzi Mikhail Galkin Christopher Morris OOD 495 28 0 21 Feb 2025
Efficient Biological Data Acquisition through Inference Set DesignInternational Conference on Learning Representations (ICLR), 2024 Ihor Neporozhnii Julien Roy Emmanuel Bengio Jason Hartford 231 2 0 25 Oct 2024
Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?LOG IN (LOG IN), 2024 Qian Ma Haitao Mao Jingzhe Liu Zhehua Zhang Chunlin Feng Yu Song Yihan Shao Yao Ma 257 3 0 20 Aug 2024
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark Jan Tönshoff Martin Ritzert Eran Rosenbluth Martin Grohe 313 72 0 01 Sep 2023