For illustration purposes, a plot of experimental Ki values is given in Figure .A; the predictive value provided by a simple, MLR-based model (to be denoted M1 from this point onwards) -employing calculated binding energies as a single predictive variable -is accordingly demonstrated in Figure .B. Both residual errors and regression statistics (Table ) testify that binding energies simply do not possess enough information for reproducing the general trend created by experimentally-measured Ki values -as M1 recovers only 50% of the sum of squares (SSQ) for the latter. In other words, the variation in Ki values is not trivially explained by means of the corresponding binding energies. In addition, residual errors as large as ~0.5 log(Ki) -having clear implications on the model's predictive valuecan be observed for five of the predicted inhibition constants. These findings clearly fit our expectations regarding the possibility of reducing binding affinities to calculated binding energies -as pointed out in the introduction (see also Figure ). ) as well as by the similarity between the resulting predicted curve and that of (A). The distribution of errors is generally narrower than that of M1 (as also indicated by SSQresidue for each of the models). Such improvements suggest that information representing particular NCIs taking place in ligand binding may be used to better predict experimental results -that is, compared to information exclusively drawn from binding energies. That being said, such outcome may partly be attributed to the fact that more informative variables are fitted to approximate the experimental Ki curve (the fitting process, however, cannot exclusively be held responsible for our models' predictive capabilities, as demonstrated in Appendix B). Let us now consider a third model (M3) -incorporating both binding energies and LED data employed in M1 and M2, respectively. As shown in Table and Figure .D, this model clearly has striking predictive power -as it recovers no-less-than 96% of the SSQ for the experimentally-measured Ki values. Residual errors are also much smaller, and their distribution narrower, compared to M1-2: the single-largest error amounts to 0.476 log(Ki), which almost matches the very smallest, qualitatively-significant errors found for the previous models. What this all means is that the totality of information corresponding to both overall binding strength and specific NCIs for each of the ligands may be used for reproducing the experimentally-measured Ki curve in a semi-quantitative manner.