论文标题
候选人：癌症药物发现的开放分子图学习基准
CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer
论文作者
论文摘要
抗癌药物的发现是偶然的，我们试图介绍开放的分子图学习基准，称为CantidatedRug4cancer，这是一个具有挑战性且逼真的基准数据集，以促进可扩展，稳健和可重复的图形机器学习抗癌药物研究。候选4CANCER数据集涵盖了多个最多的癌症靶标，涵盖了54869个与癌症相关的药物分子，其范围从临床前，临床和FDA批准的范围内。除了构建数据集外，我们还使用描述符和表达性图神经网络进行了有效的药物靶标相互作用（DTI）预测基准的基准实验。实验结果表明，候选物4Cancer在实际应用中对学习分子图和目标提出了重大挑战，这表明了将来研究候选药物治疗癌症的机会。
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.
