MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Riccardo Concu (Autor) FCUP; Natalia N D S Cordeiro (Autor) FCUP; Perez-Perez, M (Autor) Outra; Fdez-Riverola, F (Autor) Outra

Revista

Título: Molecules

Vol. 28 Nº 3

Página Final: 1182

ISSN: 1420-3049

Editora: MDPI

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Outras Informações

ID Authenticus: P-00X-YK7

DOI: 10.3390/molecules28031182

Abstract (EN): Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12