Optimally Weighted Ensembles in Model-Based Regression for Drug Discovery

In drug discovery, classification is a well established in silico method based on machine learning algorithms. However, since the activity value on a target protein is described as a continuous value, a regressional approach is worth being considered as well. The results of the regression can then be turned into classification results with a given threshold value. To further improve the results, a new method called optimally weighted ensembles that uses a combination of more than one model to build a better performing ensemble of models, is applied here. This method is used in the context of drug discovery for the first time. Naturally, it is crucial to choose suitable models for the specific problem, if any prior knowledge is available. Statistical significance of the approach is verified using a second dataset with a different target. In this work, we show to what extent the obtained classification results compare to previous, highly optimized, single model results as presented in previous work. Furthermore, the results are compared to ensembles where none of the contributing models were optimized beforehand. All case studies are performed using the publicly available database ChEMBL¹.