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..
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 | Venue PDF | 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'. |