Association Pattern-enhanced Molecular Representation Learning
Authors: Lingxiang Jia, Yuchen Ying, Tian Qiu, Shaolun Yao, Liang Xue, Jie Lei, Jie Song, Mingli Song, Zunlei Feng
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on 11 benchmark molecular property prediction tasks across 8 advanced molecular foundation models demonstrate significant superiority of the proposed method, with performance improvements of up to approximately 20%. |
| Researcher Affiliation | Academia | Lingxiang Jia1,2, Yuchen Ying1,2, Tian Qiu1,2, Shaolun Yao1,2, Liang Xue3*, Jie Lei4, Jie Song1,2, Mingli Song1,2, Zunlei Feng1,2* 1State Key Laboratory of Blockchain and Data Security, Zhejiang University 2Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security 3Computing Science and Artificial Intelligence College, Suzhou City University 4College of Computer Science, Zhejiang University of Technology |
| Pseudocode | Yes | Algorithm 1: Pattern-Enhanced Finetuning for MFMs |
| Open Source Code | Yes | Code & Appendix https://github.com/hry98kki/APMP |
| Open Datasets | Yes | To comprehensively evaluate the proposed method on downstream tasks, we conduct experiments on 11 benchmark datasets from Molecule Net (Wu et al. 2018) |
| Dataset Splits | Yes | Moreover, we randomly split all the datasets with a ratio for train/validation/test as 8:1:1, and use the best of validation results to select the best model evaluated on the test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments in the main text. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, PyTorch 1.9) needed to replicate the experiment in the main text. |
| Experiment Setup | No | The paper mentions using three different seeds for evaluation and an 8:1:1 train/validation/test split, but it lacks specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) in the main text. It refers to Appendix C.2 for more finetuning details, but these are not provided in the main text. |