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

• Type: Article in International Scientific Journal
• Date: 2023

Authors

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

Journal

Title: Molecules

Vol. 28 No. 3

Final page: 1182

ISSN: 1420-3049

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12