论文标题
DEEPPURPOSE：用于药物目标互动预测的深度学习库
DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction
论文作者
论文摘要
准确预测药物目标相互作用（DTI）对于药物发现至关重要。最近，深度学习模型显示了DTI预测的有希望的性能。但是，对于进入生物医学领域的计算机科学家和DL经验有限的生物信息学家，这些模型都可能很难使用。我们提出了Deeppurpose，这是一个用于DTI预测的全面且易于使用的深度学习库。通过实现15种化合物和蛋白质编码器以及50多个神经体系结构，并提供许多其他有用的功能，VEPURPORES支持培训定制的DTI预测模型。我们演示了在几个基准数据集上进行化耗的最先进的性能。
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use deep learning library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets.
