Robust generalization of drug-target affinity (DTA) prediction models is a notoriously difficult problem in computational drug discovery. In this article, we present pydebiaseddta: a computational software for improving the generalizability of DTA prediction models to novel ligands and/or proteins. pydebiaseddta serves as the practical implementation of the DebiasedDTA training framework, which advocates modifying the training distribution to mitigate the effect of spurious correlations in the training data set that leads to substantially degraded performance for novel ligands and proteins. Written in Python programming language, pydebiaseddta combines a user-friendly streamlined interface with a feature-rich and highly modifiable architecture. With this article we introduce our software, showcase its main functionalities, and describe practical ways for new users to engage with it.

中文翻译：

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用于训练稳健的药物靶点亲和力预测模型的计算软件：pydebiaseddta。

药物靶点亲和力（DTA）预测模型的鲁棒泛化是计算药物发现中众所周知的难题。在本文中，我们介绍了 pydebiaseddta：一种计算软件，用于提高 DTA 预测模型对新型配体和/或蛋白质的通用性。pydebiaseddta 是 DebiasedDTA 训练框架的实际实现，该框架主张修改训练分布以减轻训练数据集中虚假相关性的影响，这种影响会导致新型配体和蛋白质的性能大幅下降。pydebiaseddta 采用 Python 编程语言编写，将用户友好的简化界面与功能丰富且高度可修改的架构相结合。在本文中，我们介绍我们的软件，展示其主要功能，并描述新用户使用它的实用方法。