Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
HAGO-Net: Hierarchical Geometric Message Passing for Molecular Representation Learning
Authors: Hongbin Pei, Taile Chen, Chen A, Huiqi Deng, Jing Tao, Pinghui Wang, Xiaohong Guan
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed models are validated by extensive comparisons on four challenging benchmarks. Notably, the models exhibited state-of-the-art performance in molecular chirality identification and property prediction, achieving state-of-the-art performance on five properties of QM9 dataset. The models also achieved competitive results on molecular dynamics prediction task. |
| Researcher Affiliation | Academia | Hongbin Pei1, Taile Chen1, Chen A1, Huiqi Deng2, Jing Tao1*, Pinghui Wang1, Xiaohong Guan1 1MOE KLINNS Lab, Xi an Jiaotong University, China 2Shanghai Jiao Tong University, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the proposed HAGO-MPS with definitions and mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Model configurations in all experiments are detailed in our Git Hub repository (Git-repo 2023). |
| Open Datasets | Yes | We empirically validate and analyze HAGO-Net and DHAGO-Net on four challenging molecular datasets, including MD17, QM9, GEOM-QM9, and Pub Chem3D. |
| Dataset Splits | Yes | We adopt the same dataset split in (Liu et al. 2022), i.e., 110,000 molecules for training, 10,000 molecules for validation, and the rest for testing. |
| Hardware Specification | Yes | All experiments are conducted on eight NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | Model configurations in all experiments are detailed in our Git Hub repository (Git-repo 2023), but specific hyperparameters or detailed training settings are not provided within the main text of the paper. |