“…[23,30,33,46,73] MLR is applied to build linear relationships between independent variables (SMF descriptors: X i , i =1, 2,…) and a dependent variable (here target property Y = logK): Y = c 0 + Σc i X i , where every descriptor value (SMF count x ij , j = 1, 2,…, n; here n is the number of ligands) is associated with observed property value (y j , j = 1, 2,…, n), c i is descriptor contribution, and c 0 is the independent term which is omitted in a part of models. The Singular Value Decomposition method is used to fit contributions c i and to minimize the sum of squared residuals which are squared differences between the property values calculated by the model (y j , calc ) and observed values (y j , exp ) in the training set.…”