@article {810,
	title = {drexml: A command line tool and Python package for drug repurposing.},
	journal = {Comput Struct Biotechnol J},
	volume = {23},
	year = {2024},
	month = {2024 Dec},
	pages = {1129-1143},
	abstract = {<p>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.</p>},
	issn = {2001-0370},
	doi = {10.1016/j.csbj.2024.02.027},
	author = {Esteban-Medina, Marina and de la Oliva Roque, V{\'\i}ctor Manuel and Herr{\'a}iz-Gil, Sara and Pe{\~n}a-Chilet, Maria and Dopazo, Joaquin and Loucera, Carlos}
}
@article {751,
	title = {Discovering potential interactions between rare diseases and COVID-19 by combining mechanistic models of viral infection with statistical modeling.},
	journal = {Hum Mol Genet},
	year = {2022},
	month = {2022 Jan 12},
	abstract = {<p>Recent studies have demonstrated a relevant role of the host genetics in the COVID-19 prognosis. Most of the 7000 rare diseases described to date have a genetic component, typically highly penetrant. However, this vast spectrum of genetic variability remains yet unexplored with respect to possible interactions with COVID-19. Here, a mathematical mechanistic model of the COVID-19 molecular disease mechanism has been used to detect potential interactions between rare disease genes and the COVID-19 infection process and downstream consequences. Out of the 2518 disease genes analyzed, causative of 3854 rare diseases, a total of 254 genes have a direct effect on the COVID-19 molecular disease mechanism and 207 have an indirect effect revealed by a significant strong correlation. This remarkable potential of interaction occurs for more than 300 rare diseases. Mechanistic modeling of COVID-19 disease map has allowed a holistic systematic analysis of the potential interactions between the loss of function in known rare disease genes and the pathological consequences of COVID-19 infection. The results identify links between disease genes and COVID-19 hallmarks and demonstrate the usefulness of the proposed approach for future preventive measures in some rare diseases.</p>},
	issn = {1460-2083},
	doi = {10.1093/hmg/ddac007},
	author = {L{\'o}pez-S{\'a}nchez, Macarena and Loucera, Carlos and Pe{\~n}a-Chilet, Maria and Dopazo, Joaquin}
}
@article {713,
	title = {Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection.},
	journal = {Signal Transduct Target Ther},
	volume = {5},
	year = {2020},
	month = {2020 12 11},
	pages = {290},
	keywords = {Computational Chemistry, COVID-19, drug repositioning, Humans, Machine Learning, Molecular Docking Simulation, Molecular Targeted Therapy, Proteins, SARS-CoV-2, Signal Transduction},
	issn = {2059-3635},
	doi = {10.1038/s41392-020-00417-y},
	author = {Loucera, Carlos and Esteban-Medina, Marina and Rian, Kinza and Falco, Matias M and Dopazo, Joaquin and Pe{\~n}a-Chilet, Maria}
}
