Building on groundbreaking methods like the high-dimensional Behler-Parrinello Neural Network (BPNN) and Gaussian Approximation Potentials (GAP) , a diverse range of methodologies have been developed using kernel and deep learning methods to parameterize ML potentials (MLPs) that meet EAST requirements for performing biomolecular simulations, see Figure . This outstanding progress has also been possible due to the increased availability of extensive QM datasets of small and large molecular complexes for training MLPs (e.g., QM7-X , ANI , QM9 , MD17/22 , DES , GEMS , QMugs , Aquamarine , SPICE ). In this sense, kernel-based approaches such as GAP and Gradient Domain Machine Learning (GDML) have been successfully demonstrated to produce reliable MLPs for small molecules with reduced training sets and at QM accuracy. The incorporation of spatial and temporal physical symmetries into the GDML method, known as the symmetrized GDML model , along with the optimization of the training algorithm, enables the prediction of the potential energy surface (PES) of molecular systems containing up to a few hundred atoms, such as carbohydrates, nucleic acids, and supramolecules . Although powerful and data-efficient, kernel-based approaches are less flexible and can only be used to develop MLPs for single systems. An alternative that has advanced rapidly in recent years is the use of neural networks (NNs) for parameterizing MLPs. Accordingly, SchNet and ANI models were initially used to reconstruct the PES of small molecules at a high level of theory. The limitations in scalability and generalizability arising from the molecular representation and architecture design employed in both models motivated the development of more physically and chemically inspired NN approaches. For instance, the performance of the AIMNet model (Atoms-in-Molecules Neural Network) relies on multitask learning to simultaneously predict molecular and atomic QM properties, including energies, atomic charges, and volumes. A similar methodology underpins PhysNet , which refines energy predictions by accounting for the physical models of electrostatics and van der Waals interactions (DFT-D3) during the training procedure. This approach allows PhysNet to capture long-range interactions accurately, bridging gaps left by earlier models and extending the applicability of NN-based potentials to a broader range of molecular systems (e.g., polyalanine peptide, SN 2 reactions).