  Quantitative structure-activity relationship (QSAR) is a computer modeling
technique for identifying relationships between the structural properties of
chemical compounds and biological activity. QSAR modeling is necessary for drug
discovery, but it has many limitations. Ensemble-based machine learning
approaches have been used to overcome limitations and generate reliable
predictions. Ensemble learning creates a set of diverse models and combines
them. In our comparative analysis, each ensemble algorithm was paired with each
of the basic algorithms, but the basic algorithms were also investigated
separately. In this configuration, 57 algorithms were developed and compared on
4 different datasets. Thus, a technique for complex ensemble method is proposed
that builds diversified models and integrates them. The proposed individual
models did not show impressive results as a unified model, but it was
considered the most important predictor when combined. We assessed whether
ensembles always give better results than individual algorithms. The Python
code written to get experimental results in this article has been uploaded to
Github (https://github.com/rifqat/Comparative-Analysis).
