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
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.