  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.
