Training-free Multi-objective Diffusion Model for 3D Molecule Generation
Authors: Xu Han, Caihua Shan, Yifei Shen, Can Xu, Han Yang, Xiang Li, Dongsheng Li
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments on both single-objective and multi-objective 3D molecule generation, focusing on quantum properties, and compared our approach with the trained or fine-tuned diffusion models. |
| Researcher Affiliation | Collaboration | 1Tufts University, 2Microsoft Research Asia 3East China Normal University, 4Microsoft Research AI4Science |
| Pseudocode | Yes | The pseudo-code and hyperparameters are provided in Appendix A.1 and A.3, and the code will be published later. |
| Open Source Code | No | The pseudo-code and hyperparameters are provided in Appendix A.1 and A.3, and the code will be published later. |
| Open Datasets | Yes | We perform conditional molecule generation on QM9 (Ramakrishnan et al., 2014), a dataset of over 130K molecules and 6 corresponding quantum properties. |
| Dataset Splits | Yes | Following previous research, we split the dataset into training, valid, and test sets, each including 100K, 18K, and 13K samples respectively. |
| Hardware Specification | No | The paper mentions hardware used for training baselines ('A100 GPU') but does not specify the hardware used for running their own training-free method's experiments (e.g., inference, evaluation). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The pseudo-code and hyperparameters are provided in Appendix A.1 and A.3. In Appendix A.3, Table 3 lists 'Hyperparameters for two conditions sampling' including 'Guide from', 'w1', 'w2', 'r', and 'MC sample size'. |