The initial SQM methods have been corrected to account for non-covalent interactions . SQM and SQM-based SFs have been utilized to provide a more accurate prediction of the ligand binding mode , as well as affinity classification, binding pose identification, and virtual screening (VS). Pavel Hobza's group has previously developed SQM/COSMO SFs at various SQM levels, with the aim to treat larger sets of ligands/docking poses with superior ranking and sampling power than standard SFs . These SFs have been successful in discriminating active and inactive compounds , identifying native docking-ligand poses , ranking compounds based on affinity , and in some cases of VS . Similar studies have been conducted by Ortiz-Mahecha et al. who used an SF based on FMO-DFTB3 potentials including solvation effects to discriminate between binders and non-binders of human leukocyte antigens with good results. Rocha-Santos et al. used PM7 to correctly classify ricin toxin A (RTA) inhibitors .