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 [1].
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
Authors: Han Huang, Leilei Sun, Bowen Du, Weifeng Lv
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on diverse datasets validate the effectiveness of our framework. Particularly, the proposed method still generates high-quality molecular graphs in a limited number of steps. In this section, we display the experimental results of the proposed discrete graph structure assisted diffusion framework on multiple datasets. |
| Researcher Affiliation | Academia | Han Huang, Leilei Sun, Bowen Du*, Weifeng Lv State Key Laboratory of Software Development Environment, Beihang University, China EMAIL |
| Pseudocode | Yes | Algorithm 1: Optimizing CDGS Algorithm 2: Sampling from CDGS with the Euler Maruyama method |
| Open Source Code | Yes | We provide more experiment details in Appendix, and we release the code at https://github. com/GRAPH-0/CDGS. |
| Open Datasets | Yes | We train and evaluate models on two molecule datasets, ZINC250k (Irwin et al. 2012) and QM9 (Ramakrishnan et al. 2014). |
| Dataset Splits | No | We use 8 : 2 as the split ratio for train/test. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory, or cloud instance types used for running experiments. |
| Software Dependencies | No | The paper mentions software like RDKit but does not provide specific version numbers for any key software components or libraries required for replication. |
| Experiment Setup | Yes | We pretrain the time-dependent predictor on perturbed graphs of the ZINC250k dataset for 200 epochs. Each initial molecular graph is encoded into latent codes at the middle time tΞΎ = 0.3 through the forward-time ODE solver. After 50 gradient ascent steps, all latent codes are decoded back to molecules with another gradient-guided reverse-time ODE solver. |