To compensate for the above-described deficiency, we attempted to estimate the ∆H and T ∆S int via an alternative approach, that of ML using selected ligand topology, physicochemical, and P-L interaction surface descriptors. Among them prevailed 7, the number of single and aromatic bonds, which are associated with the vibrational entropy that according to 39 has the largest contribution to T ∆S int , the MW and AMW, which are associated with pretty much all entropy components, the ring_flexibility and function_group_count that are linked to the conformational entropy 39 , and finally, the aqueous solubility (deepFl_logP), which is a known determinant of ligand binding. Descriptors directly related to the protein's entropic change upon ligand binding are missing from this pilot study due to the fact that in the proposed scoring protocol the receptor structure changes only mildly. Apart from PM6/COSMO, as a baseline SF we also used Glide SP, which was resilient to optimization by descriptors or interaction fingerprints (data not shown). This implies that this SF is already fine-tuned and hence is a good baseline for comparison with our new hybrid SQM-ML SF. Moreover, Glide is the most popular commercial docking software and, according to the literature and our experience, the most effective, which makes it a tough opponent for SQM-ML.