GeSS - DrugOOD

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Date: 2024-12-13

Name: GeSS - DrugOOD

Domain: Biology & Medicine

Focus: Benchmark suite evaluating geometric deep learning models under real-world distribution shifts

Task Types: Classification

Metrics: Accuracy, RMSE, OOD robustness delta

Models: GCN, EGNN, DimeNet++

AI/ML Motif: Classification

Resources

Benchmark: Visit

Keywords

geometric deep learning distribution shift OOD robustness scientific applications

Citation

• Deyu Zou, Shikun Liu, Siqi Miao, Victor Fung, Shiyu Chang, and Pan Li. Gess: benchmarking geometric deep learning under scientific applications with distribution shifts. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, editors, Advances in Neural Information Processing Systems, volume 37, 92499–92528. Curran Associates, Inc., 2024. URL: https://proceedings.neurips.cc/paper_files/paper/2024/file/a8063075b00168dc39bc81683619f1a8-Paper-Datasets_and_Benchmarks_Track.pdf.

@inproceedings{neurips2024_a8063075,
  author = {Zou, Deyu and Liu, Shikun and Miao, Siqi and Fung, Victor and Chang, Shiyu and Li, Pan},
  booktitle = {Advances in Neural Information Processing Systems},
  editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
  pages = {92499--92528},
  publisher = {Curran Associates, Inc.},
  title = {GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts},
  url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/a8063075b00168dc39bc81683619f1a8-Paper-Datasets_and_Benchmarks_Track.pdf},
  volume = {37},
  year = {2024}
}

Ratings

CategoryRating

Software

3.00

Reference code expected post-conference; current public software availability limited. Benchmark infrastructure partially described but not fully released yet.

Specification

5.00

Benchmark clearly defines OOD robustness scenarios with classification and regression tasks in scientific domains, though no explicit hardware constraints are given.

Dataset

5.00

Curated datasets of 3D crystal structures and material properties are included and publicly available for reproducible research.

Metrics

5.00

Uses well-established metrics such as MAE and structural validity for materials modeling, plus accuracy and OOD robustness deltas.

Reference Solution

4.00

Two reference models (SODNet, DiffCSP-SC) are reported with results, code expected to be released soon.

Documentation

4.00

Paper and poster provide solid explanation of benchmarks and scientific motivation; more extensive user documentation forthcoming.

Average rating: 4.33/5

Radar plot

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