Virtual Screening And Discovery Of Matrix Metalloproteinase-12 Inhibitors By Swarm Intelligence Optimization Algorithm-Based Machine Learning

Matrix metalloproteinase-12 (MMP-12) is an attractive therapeutic target for drug design and discovery for many human conditions. In this study, six swarm intelligence optimization algorithms were applied to optimize the parameters of the model generated using the LibSVM toolkit in MATLAB to identify potential MMP-12 inhibitors (MMP-12is); six types of optimized support vector machine (SVM) models were established. The highest prediction accuracy obtained was 98.89 %, which was equivalent to the effect of the optimal "RF+opt" model. All six models passed the Y-randomization test and showed excellent performance with reliable results. Virtual screening identified 371 molecules with a predictive probability score greater than 0.9. The optimized SVM models, in addition to "RF+opt" and "SVM2" models, were combined to establish a consistency evaluation system. Our results revealed six non-toxic potential MMP-12is. This process provides a strong theoretical basis for the design, synthesis, and development of novel drugs targeting MMP-12.