Anti-cancer drug discoveries have been serendipitous, we sought to present the Open Molecular Graph Learning Benchmark, named CandidateDrug4Cancer, a challenging and realistic benchmark dataset to facilitate scalable, robust, and reproducible graph machine learning research for anti-cancer drug discovery. CandidateDrug4Cancer dataset encompasses multiple most-mentioned 29 targets for cancer, covering 54869 cancer-related drug molecules which are ranged from pre-clinical, clinical and FDA-approved. Besides building the datasets, we also perform benchmark experiments with effective Drug Target Interaction (DTI) prediction baselines using descriptors and expressive graph neural networks. Experimental results suggest that CandidateDrug4Cancer presents significant challenges for learning molecular graphs and targets in practical application, indicating opportunities for future researches on developing candidate drugs for treating cancers.
@article{arxiv.2203.00836,
title = {CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer},
author = {Xianbin Ye and Ziliang Li and Fei Ma and Zongbi Yi and Pengyong Li and Jun Wang and Peng Gao and Yixuan Qiao and Guotong Xie},
journal= {arXiv preprint arXiv:2203.00836},
year = {2022}
}
Comments
Accepted by Workshop on Graph Learning Benchmarks, The Web Conference 2021