Vps34 is the only isoform of the PI3K family in fungi, making this protein an attractive target to develop new treatments against pathogenic fungi. The high structural similarity between the active sites of the human and fungal Vps34 makes repurposing of human Vps34 inhibitors an appealing strategy. Nonetheless, while some of the cross-reactive inhibitors might have the potential to treat fungal infections, a safer approach to prevent undesired side effects would be to identify molecules that specifically inhibit the fungal Vps34. This study presents the parameterization of four LIE models for estimating the binding free energy of Vps34-inhibitor complexes. Two models are parameterized using a multiparametric linear regression leaving one or more free parameters, while the other two are based on the LIE-D model. All of the models show good predictive capacity (R2 > 0.7, r > 0.85) and a low mean absolute error (MAE < 0.71 kcal/mol). The current study highlights the advantages of LIE-D-derived models when predicting the weight of the different contributions to the binding free energy. It is expected that this study will provide researchers with a valuable tool to identify new Vps34 inhibitors for relevant applications such as cancer treatment and the development of new antimicrobial agents.