Esteban-Medina Marina, de la Oliva Roque Víctor Manuel, Herráiz-Gil Sara, Peña-Chilet María, Dopazo Joaquín, Loucera Carlos
Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain.
Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain.
Comput Struct Biotechnol J. 2024 Mar 1;23:1129-1143. doi: 10.1016/j.csbj.2024.02.027. eCollection 2024 Dec.
We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.
我们介绍了drexml,这是一种用于合理的数据驱动药物再利用的命令行工具和Python包。该包采用机器学习和机制性信号转导建模来识别能够调节特定疾病的药物靶点。此外,它还采用可解释性工具将潜在的药物靶点置于疾病的功能格局中。该方法在范可尼贫血和家族性黑色素瘤这两种迫切需要解决方案的不同罕见疾病中得到了验证。在范可尼贫血病例中,该模型成功预测了先前经验证的再利用药物,而在家族性黑色素瘤病例中,它识别出了一组有前景的药物以供进一步研究。