Zhuo Zhen, Thrimoorthy Potta, Nicholas A. Lanzillo, Kaushal Rege and Curt M. Breneman Pages 41 - 55 ( 15 )
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.
Software, QSAR, QSPR, machine learning, regression, support vector machine.
Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, Chemical Engineering, Arizona State University, Tempe, AZ 85287-6106, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, Chemical Engineering, Arizona State University, Tempe, AZ 85287-6106, School of Science, Rensselaer Polytechnic Institute, 1C05 Jonsson-Rowland Science Center, 110 Eighth Street, Troy, NY 12180