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. Results: In this work, we employed the SOLE system to model transgene expression efficacy of polymers obtained from aminoglycoside antibiotics, which allowed the results of several modeling approaches to be easily compared. All models had test set r² of 0.96-0.98 and test set R² of 0.79-0.84. Y-scrambling test showed the models were stable and not over-fitted. Conclusion: SOLE has a user-friendly interface and includes routine elements of performing QSAR/QSPR studies that can be applied in various research areas. It utilizes rational and sophisticated feature selection, model selection and model evaluation processes.