Jiang Zhengxiang, Li Pengyong
School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.
School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710126, China.
Bioinformatics. 2024 Nov 28;40(12). doi: 10.1093/bioinformatics/btae688.
Accurate drug response prediction is critical to advancing precision medicine and drug discovery. Recent advances in deep learning (DL) have shown promise in predicting drug response; however, the lack of convenient tools to support such modeling limits their widespread application. To address this, we introduce DeepDR, the first DL library specifically developed for drug response prediction. DeepDR simplifies the process by automating drug and cell featurization, model construction, training, and inference, all achievable with brief programming. The library incorporates three types of drug features along with nine drug encoders, four types of cell features along with nine cell encoders, and two fusion modules, enabling the implementation of up to 135 DL models for drug response prediction. We also explored benchmarking performance with DeepDR, and the optimal models are available on a user-friendly visual interface.
DeepDR can be installed from PyPI (https://pypi.org/project/deepdr). The source code and experimental data are available on GitHub (https://github.com/user15632/DeepDR).
准确的药物反应预测对于推进精准医学和药物发现至关重要。深度学习（DL）的最新进展在预测药物反应方面显示出了前景；然而，缺乏支持此类建模的便捷工具限制了它们的广泛应用。为了解决这个问题，我们引入了DeepDR，这是第一个专门为药物反应预测而开发的深度学习库。DeepDR通过自动进行药物和细胞特征提取、模型构建、训练和推理来简化流程，所有这些都可以通过简短的编程实现。该库包含三种类型的药物特征以及九种药物编码器、四种类型的细胞特征以及九种细胞编码器和两个融合模块，能够实现多达135种用于药物反应预测的深度学习模型。我们还使用DeepDR探索了基准性能，并且最佳模型可在用户友好的可视化界面上获取。
DeepDR可以从PyPI（https://pypi.org/project/deepdr）安装。源代码和实验数据可在GitHub（https://github.com/user15632/DeepDR）上获取。
