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..
Graph Diffusion Transformers for Multi-Conditional Molecular Generation
Authors: Gang Liu, Jiaxin Xu, Tengfei Luo, Meng Jiang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively validate Graph Di T for multi-conditional polymer and small molecule generation. Results demonstrate the superiority of Graph Di T across nine metrics from distribution learning to condition control for molecular properties. In experiments, we evaluate model performance on one polymer and three small molecule datasets. |
| Researcher Affiliation | Academia | Gang Liu, Jiaxin Xu, Tengfei Luo, Meng Jiang University of Notre Dame EMAIL |
| Pseudocode | No | Not found. The paper contains architectural diagrams and descriptions but no explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is provided in the supplementary materials. Data and code will be on Github after publication. |
| Open Datasets | Yes | We have one polymer dataset [40] for materials, featuring three numerical gas permeability conditions: O2Perm, CO2Perm, and N2Perm. For drug design, we create three class-balanced datasets from Molecule Net [46]: HIV, BBBP, and BACE |
| Dataset Splits | Yes | We randomly split the dataset into training, validation, and testing (reference) sets in a 6:2:2 ratio. |
| Hardware Specification | Yes | All experiments can be run on a single A6000 GPU card. |
| Software Dependencies | No | Not found. The paper does not specify software dependencies with version numbers for reproducibility (e.g., Python, PyTorch, or other libraries with their specific versions). |
| Experiment Setup | No | Not found. The paper describes architectural and encoding choices but does not provide specific numerical hyperparameters (e.g., learning rate, batch size, number of epochs) or system-level training settings. |