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Anti-cancer drug discoveries have been serendipitous, we sought to presentthe Open Molecular Graph Learning Benchmark, named CandidateDrug4Cancer, achallenging and realistic benchmark dataset to facilitate scalable, robust, andreproducible graph machine learning research for anti-cancer drug discovery.CandidateDrug4Cancer dataset encompasses multiple most-mentioned 29 targets forcancer, covering 54869 cancer-related drug molecules which are ranged frompre-clinical, clinical and FDA-approved. Besides building the datasets, we alsoperform benchmark experiments with effective Drug Target Interaction (DTI)prediction baselines using descriptors and expressive graph neural networks.Experimental results suggest that CandidateDrug4Cancer presents significantchallenges for learning molecular graphs and targets in practical application,indicating opportunities for future researches on developing candidate drugsfor treating cancers.

CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer