Optimization of correlation weights of local graph invariants is an approach to model molecular properties and/or activities of chemical or/and biological interest. The essence of the approach may be described by means of three main steps: first, a descriptor which is a function of the weights of local graph invariants must be defined by the suitable choice among the different possibilities from the pool of molecular descriptors; second, correlation weights values which produce as large as possible correlation coefficient value between the selected property values and the descriptor data under consideration are calculated by Monte Carlo optimization procedure (the correlation coefficient is used as the quality objective function); third, a relationship such as property = C0 + C1 descriptor has to be calculated and validated with structures of some training set resorting to the standard least square method. We obtain quite satisfactory results using this calculation procedure to model the aqueous solubility of alcohols whose statistical characteristics are: n = 30, r = 0.9843, s = 0.176, F = 870 (Training Set); n = 33, r = 0.9965, s = 0.0902, F = 4456 (Test Set); n = 63, r = 0.9931, s = 0.121, F = 4379 (complete set of alcohol molecules).