We tested the published weights trained with this data plus the models trained including also internal data. The models are described below. utilized for binding affinity prediction, with kDEEP being a prominent example. However, it is computationally intensive and non-equivariant, requiring grid rotations to achieve invariant predictions for different grid orientations. We implemented the version published by Moon et al. This architecture is identical to kDEEP in all aspects except for the atom features, which include atom type, period, group, degree of atom, hybridization, formal charge, and aromaticity.