Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation
Authors: Haoran Liu, Youzhi Luo, Tianxiao Li, James Caverlee, Martin Renqiang Min
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate our model s effectiveness on property-guided and context-guided molecule generation, both for de-novo 3D molecule design and structure-based drug discovery against protein targets. |
| Researcher Affiliation | Collaboration | 1Texas A&M University, College Station, TX 77840; 2NEC Laboratories America, Princeton, NJ 08540 |
| Pseudocode | Yes | A detailed algorithm for this decoding process is in Appendix 9. |
| Open Source Code | No | Supplementary materials are available at this link1. 1https://arxiv.org/abs/2412.15086 |
| Open Datasets | Yes | We conduct experiments mainly on two benchmark datasets for 3D molecule generation: GEOM-Drugs (Axelrod and Gomez-Bombarelli 2022) and Cross Docked2020 (Francoeur et al. 2020). |
| Dataset Splits | No | Our model is trained using a subset of 50,000 molecular structures from each dataset, specifically selecting those with the lowest energy conformations for each molecule. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types with speeds, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions 'RDKit (Landrum 2016)' but does not provide a comprehensive list of key software components with their specific version numbers needed to replicate the experiments. |
| Experiment Setup | No | The paper states 'Implementation details are provided in Appendix 11.' but the main text does not contain specific hyperparameter values, training configurations, or system-level settings for the experiments. |