It should be noted that it might just happen that some randomly-generated numbers will prove to reflect predictive information after such fitting process (a fact often incorporated into machine learning techniques ) -but that would clearly not teach us anything about the causal relevance of our own calculated data to experimentally-measured Ki values. Needless to say, the DLPNO-CCSD(T)/SVP level of theory used above is, by no means, a random number generator: it offers a well-defined path towards obtaining physically-meaningful energetic quantities. Thus, the fact that the latter provide us with desirable predictive power (which cannot trivially be obtained through optimization of regression coefficients, as done for models A1-3) may also suggest some causal relationship between our predictors and the considered PL binding affinities.