Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
Authors: weitao Du, Jiujiu Chen, Xuecang Zhang, Zhi-Ming Ma, Shengchao Liu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, Molecule JAE proves its effectiveness by reaching state-of-the-art performance on 15 out of 20 tasks by comparing it with 12 competitive baselines. |
| Researcher Affiliation | Collaboration | 1 Department of Mathematics, Chinese Academy of Sciences 2 Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China 3 Huawei Technologies Ltd 4 Department of Computer Science and Operations Research, Université de Montréal |
| Pseudocode | No | The paper describes its methodology through textual explanations and diagrams but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The code is available on this website. (No URL is provided in the document.) |
| Open Datasets | Yes | For pretraining, we use PCQM4Mv2 [55]. and QM9 [59] is a dataset of 134K molecules... and MD17 [46] is a dataset on molecular dynamics simulation. |
| Dataset Splits | Yes | We take 110K for training, 10K for validation, and 11K for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or processor types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using specific models/architectures like Sch Net and Mi Di transformer, but it does not specify any software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Table 6: Hyperparameter specifications for Molecule JAE of property prediction. epochs {50, 100} learning rate {5e-4, 1e-4} β {[0.1, 10]} number of steps {1000} λ1 {1} λ2 {0, 0.01, 1} and Table 7: Hyperparameter specifications for Molecule JAE of molecule generation. epochs {3000, 10000} learning rate {2e-4, 3e-4} number of layers 12 number of diffusion steps {500} diffusion noise schedule cosine mlp hidden dimensions {X: 256, E: 128, y: 128, pos: 64} λtrain {X: 0.4, E: 2, y: 0, pos: 3, charges: 1} |