To explore the structural diversity within the dataset, we first performed substructure matching against common chloride binding motifs and found that the five most common chloride binding motifs are thiourea, urea, squaramide, indole, and tambjamine moieties (Figure ) We also noted that one transporter may have multiple binding motifs. In addition, we computed the pairwise Tanimoto index to evaluate the coverage of chemical space within our datasets. Tanimoto index ranges from 0, meaning the molecules have no structural similarity, to 1, meaning the two molecules are identical. Our training and test sets have a remarkable degree of structural diversity and share low similarity (Figure ). This structural diversity ensures the generalizability of our model to novel chemical structures, making it a reliable predictor of transport activities for molecules outside the training set. For feature representation, we employed Mordred descriptors as input features. 1,613 2D Mordred descriptors were Initially considered. To select the most relevant features, we conducted correlation analysis, individually testing each feature's correlation with the target property. We used Spearman correlation, which is suitable for both continuous and ordinal variables and captures linear and non-linear associations. Features with correlation coefficients of 0.2 or higher were retained. To further assess the significance of these features, we performed a two-tailed p-test using the Spearman correlation coefficient as the test statistic.