This study presents the first QSAR model for Galectin-3 glycomimetic inhibitors based on docked structures to the carbohydrate recognition domain (CRD). Quantitative numerical methods such as PLS (Partial Least Squares) and ANN (Artificial Neural Networks) have been used and compared on QSAR models to establish correlations between molecular properties and binding affinity values (Kd). Training and validation of QSAR predictive models was performed on a master dataset consisting of 136 compounds. The molecular structures and binding affinities (Kd) (136 compounds) were obtained from the literature. To address the issue of dimensionality reduction, molecular descriptors were selected with PLS contingency approach, ANN, PCA (Principal Component Analysis) and GA (Genetic Algorithms) for the best predictive Galectin-3 binding affinity (Kd). Final sets comprising 56, 31 and 35 descriptors were obtained with PLS, PCA and ANN, respectively. The objective of this prototype QSAR model is to serve as a first guideline for the design of novel and potent Gal-3 selective inhibitors with emphasis on modification at both C-3 and at O-3 positions .