Applying Transfer Learning to QSAR Regression Models
Aiming at avoiding high costs in the production and analysis of new drug candidates, databases containing molecular information have been generated, and thus, computational models can be constructed from these data. The quantitative study of structure-activity relationships (QSAR) involves building predictive models that relate chemical descriptors for a compound set and biological activity with respect to one or more targets in the human body. Datasets manipulated by researchers in QSAR analyses are generally characterized by a small number of instances, which can affect the accuracy of the resulting models. In this context, transfer learning techniques that take information from other QSAR models to the same biological target would be desirable, reducing efforts and costs for evaluating new chemical compounds. This article presents a novel transfer learning method that can be applied to build QSAR regression models by Support Vectors Regression (SVR). The SVR-Adapted method for Transfer Learning (ATL) was compared with standard SVR method regarding values of mean squared error. From experimental studies, the performance of both methods was evaluated for different proportions of the original training set. The obtained results show that transfer learning is capable to exploit knowledge from models built from other datasets, which is effective primarily for small target training datasets.
KeywordsTransfer learning Support-vector regression Cheminformatics QSAR models Medicinal chemistry
The authors thank to the Brazilian funding agencies CAPES and FAPESP, and to IBM for financial support.
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