Virtual Screening and Discovery of Matrix Metalloproteinase‐12 Inhibitors by Swarm Intelligence Optimization Algorithm‐Based Machine Learning

Abstract

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 nontoxic potential MMP-12is. This process provides a strong theoretical basis for the design, synthesis, and development of novel drugs targeting MMP-12.

Pilot-Scale Anaerobic Treatment of Printing and Dyeing Wastewater and Performance Prediction Based on Support Vector Regression

“…The two algorithms do not display significant difference in their performance when using different kernel functions. (25) is smaller than that used by the ν-SVR (34), consistent with its fewer iteration times. This indicates that when the data amount is huge, the ε-SVR using fewer support vectors may obtain a faster model training and prediction speed.…”

“…In this study, Matlab R2018a software (MathWorks Inc., USA) was used to compile and configure the downloaded and decompressed LibSVM library [25]. According to the grey correlation analysis results of Qi et al [21], the COD removal rate was closely related to the flow rate, OLR, water temperature and influent SS.…”