Drug repurposing, which involves using already approved drugs for new clinical targets, represents a cost-effective alternative to the development of new drugs. In this study, we introduce an innovative computational strategy, which uses Non-negative Matrix Tri-Factorization (NMTF) to generate vector embeddings of given sizes for drugs and drug targets; vector embeddings are then employed to generate predictions for drug repurposing using conventional classifiers, like random forest, logistic regression, and multi-layer perceptron. Our approach leverages the NMTF method within a new approach to classification, named two-tower architecture, which is effective in solving complex tasks, such as the optimal prediction of targets for already approved drugs. This approach produces robust models, with AUROC reaching 0.90, which outperform traditional NMTF. We evaluate our method in the context of Parkinson's Disease; within the newly revealed drug-target predictions, we highlight compounds that exhibit potential in mitigating neurodegeneration, thereby revealing a potentially useful drug in relationships with a well-identified target.

Leveraging Non-negative Matrix Tri-Factorization and Knowledge-Based Embeddings for Drug Repurposing: an Application to Parkinson's Disease

Messa, Letizia;Testa, Carolina;Raimondi, Manuela Teresa;Ceri, Stefano;Pinoli, Pietro
2024-01-01

Abstract

Drug repurposing, which involves using already approved drugs for new clinical targets, represents a cost-effective alternative to the development of new drugs. In this study, we introduce an innovative computational strategy, which uses Non-negative Matrix Tri-Factorization (NMTF) to generate vector embeddings of given sizes for drugs and drug targets; vector embeddings are then employed to generate predictions for drug repurposing using conventional classifiers, like random forest, logistic regression, and multi-layer perceptron. Our approach leverages the NMTF method within a new approach to classification, named two-tower architecture, which is effective in solving complex tasks, such as the optimal prediction of targets for already approved drugs. This approach produces robust models, with AUROC reaching 0.90, which outperform traditional NMTF. We evaluate our method in the context of Parkinson's Disease; within the newly revealed drug-target predictions, we highlight compounds that exhibit potential in mitigating neurodegeneration, thereby revealing a potentially useful drug in relationships with a well-identified target.
2024
ICBBE'23: Proceedings of the 2023 10th International Conference on Biomedical and Bioinformatics Engineering
Non-negative matrix factorization, Learning latent representations, Computational biology.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1261776
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