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].
Floating Anchor Diffusion Model for Multi-motif Scaffolding
Authors: Ke Liu, Weian Mao, Shuaike Shen, Xiaoran Jiao, Zheng Sun, Hao Chen, Chunhua Shen
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments, Dataset. Two datasets are utilized in this work..., Evaluation metrics., Ablation study is conducted to evaluate the effectiveness of TSP and noise scale., Finally, we compare our approaches with the conditional generation and inpainting methods... |
| Researcher Affiliation | Collaboration | 1Zhejiang University, China 2The University of Adelaide, Australia 3Swansea University, UK 4Ant Group. |
| Pseudocode | No | Not found. |
| Open Source Code | Yes | Code is available at https://github.com/aim-uofa/FADiff. |
| Open Datasets | Yes | Two datasets are utilized in this work, including the virtual motif dataset (VM dataset) from the PDB database (Berman et al., 2000) for training and the evaluation multi-motif scaffolding benchmark MS Benchmark that we collected from the PROSITE database(Sigrist et al., 2012). |
| Dataset Splits | No | Not found. The paper mentions “training” and “evaluation” sets but does not specify explicit numerical splits (percentages or counts) for train, validation, and test sets. |
| Hardware Specification | Yes | All our experiments are conducted on a computing cluster with 8 GPUs of NVIDIA Ge Force RTX 4090 24GB and CPUs of AMD EPYC 7763 64-Core of 3.52GHz. All the inferences are conducted on a single GPU of NVIDIA Ge Force RTX 4090 24GB. |
| Software Dependencies | No | Not found. The paper refers to other models/frameworks (Frame Diff, VFN-Diff) but does not list specific software dependencies (e.g., libraries, programming languages) with their version numbers. |
| Experiment Setup | Yes | We train FADiff for 90,000 steps with a coordinate scale of 0.1 and 0.02 based on the pre-trained VFN-Diff (Mao et al., 2024).Euler-Maruyama discretization with 500 steps implemented as a geodesic random walk is adopted in this work... |