Distributed heterogeneous ensemble learning on Apache Spark for ligand-based virtual screening

Abstract

Virtual screening is one of the most common computer-aided drug design techniques that apply computational tools and methods on large libraries of molecules to extract the drugs. Ensemble learning is a recent paradigm launched to improve machine learning results in terms of predictive performance and robustness. It has been successfully applied in ligand-based virtual screening (LBVS) approaches. Applying ensemble learning on huge molecular libraries is computationally expensive. Hence, the distribution and parallelisation of the task have become a significant step by using sophisticated frameworks such as Apache Spark. In this paper, we propose a new approach HEnsL_DLBVS, for heterogeneous ensemble learning, distributed on Spark to improve the large-scale LBVS results. To handle the problem of imbalanced big training datasets, we propose a novel hybrid technique. We generate new training datasets to evaluate the approach. Experimental results confirm the effectiveness of our approach with satisfactory accuracy and its superiority over homogeneous models.