Our training strategy consists of several stages aimed at enhancing the robustness and performance of our model. In the first stage, the model is trained on the ANI-1 database, one of the largest database of Density Functional Theory (DFT) computations for small organic molecules. However, due to the relatively low ab-initio accuracy of the ANI-1 database, we fine-tuned the model on two more chemically accurate databases: SPICE 66 and DES370K. The SPICE dataset is used to ensure the accuracy on intramolecular interactions of biomolecular systems. SPICE is a collection of DFT data mainly built to train MLP for simulating drug molecules and proteins. The computations are performed at the ωB97M-D3(BJ) functional with the def2-TZVPPD basis set. In contrast, the DES370K dataset is used to ensures the accuracy of intermolecular interactions. In DES370K, the reference interaction energies for these systems are computed using the highly accurate coupled-cluster singles and doubles with perturbative triples (CCSD(T)) level of theory with a complete basis set (CBS). The complexes in this database represent most of the molecular interactions that could occur in chemistry, including electrostatic-dominated (hydrogen bonding), dispersion-dominated, and mixed (electrostatic/dispersion) interactions.