We trained 6 RF models with possible combinations of the feature classes, namely SQM, PLEC, and descr. From descr, we excluded the 3D ligand shape descriptors because they could be computed for only 60% of the molecules. The rest did not go through due to the failure of RDKit to kekulize some of their atoms (see Table ). For comparison, in Table and Table , and Figures and, we also include SQM-ML, which is the final RF model after feature engineering. RF(PLEC, descr), RF(PLEC), and RF(descr) are the null models that help to reject the null hypothesis that utilization of SQM terms as input features does not offer significant improvement, or otherwise that PLEC and descr are sufficient to score the given compounds for bioactivity. Finally, we also report the performance of the baseline SFs, namely PM6/COSMO (stated as "SQM") and Glide SP.