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

Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for both predicting new active drugs as well as 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. For such main aim, this paper presents an MTML-QSAR models based on the use of topological drugs’ features, along 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 at http://sing-group.org/mozart and could be downloaded as free open-source software at https://github.com/mpperez3/MOZART.