Molformer: Motif-Based Transformer on 3D Heterogeneous Molecular Graphs
Authors: Fang Wu, Dragomir Radev, Stan Z. Li
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate Molformer across a broad range of domains, including quantum chemistry, physiology, and biophysics. Extensive experiments show that Molformer outperforms or achieves the comparable performance of several state-of-the-art baselines. |
| Researcher Affiliation | Academia | Fang Wu1,3, Dragomir Radev2, Stan Z. Li1* 1 School of Engineering, Westlake University 2 Department of Computer Science, Yale University 3 Institute of AI Industry Research, Tsinghua University |
| Pseudocode | Yes | Algorithm 1: Attentive Farthest Point Sampling |
| Open Source Code | Yes | The code is available at https://github.com/smiles724/Molformer. |
| Open Datasets | Yes | For QM9, we use the exact train/validation/test split as Townshend et al. (2020). For PDBbind, 90% of the data is used for training and the rest is divided equally between validation and test like Chen et al. (2019). For others, we adopt the scaffold splitting method with a ratio of 8:1:1 for train/validation/test as Rong et al. (2020). |
| Dataset Splits | Yes | For QM9, we use the exact train/validation/test split as Townshend et al. (2020). For PDBbind, 90% of the data is used for training and the rest is divided equally between validation and test like Chen et al. (2019). For others, we adopt the scaffold splitting method with a ratio of 8:1:1 for train/validation/test as Rong et al. (2020). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) required to replicate the experiment. |
| Experiment Setup | No | The paper states, "More implementing details can be found in Appendix," implying that specific experimental setup details like hyperparameters are not present in the main text. |