Appropriate Evaluation Measurements for Regression Models

Abstract

In recent years, accelerating the speed of finding seed compounds and reducing the cost of pharmaceutical research has become a necessity. The contribution of in silico drug discovery methods, which predict candidates as new drugs using physicochemical features and substructure fingerprints of compounds, is thus expected. Selecting the seed compounds without conducting experiments could enable us to reduce the time and cost required for drug development. However, estimating the characteristics of compounds in our body using a simple linear model alone is unsatisfactory because effects and distribution of compounds are determined by the environment in our body and their interactions with other molecules. Compared to simple models, more complex models have been prepared to estimate compound characteristics with high predictive accuracy. Thus, it is increasingly important to correctly evaluate the predictive performance when selecting the models appropriate for research purposes. The determinant coefficient, famous as R², is one of the most famous statistical measures for evaluating regression models. However, this measure cannot be used to evaluate nonlinear models. In this paper, the difficulty of using the determinant coefficient is explained and the proper statistical measures were suggested under the following two conditions: mean squared error (MSE) for cross-validation, and MSE along with correlation coefficients for the observed and predicted values of test data. As understanding statistical measures and using them appropriately is necessary, the suggested measures will support the effective selection of promising seed compounds and accelerate drug discovery.