Development of a Web-Enabled SVR-Based Machine Learning Platform and its Application on Modeling Transgene Expression Activity of Aminoglycoside-Derived Polycations

Objective: Support Vector Regression (SVR) has become increasingly popular in cheminformatics modeling. As a result, SVR-based machine learning algorithms, including Fuzzy-SVR and Least Square-SVR (LS-SVR) have been developed and applied in various research areas. However, at present, few downloadable packages or public-domain software are available for these algorithms. To address this need, we developed the Support vector regression-based Online Learning Equipment (SOLE) web tool (available at http://reccr.chem.rpi.edu/SOLE/index.html) as an online learning system to support predictive cheminformatics and materials informatics studies. Keywords: Software, QSAR, QSPR, machine learning, regression, support vector machine.