Due to their computational efficiency, 2D fingerprints are typically usedin similarity-based high-content screening. The interaction of a ligandwith its target protein, however, relies on its physicochemical interactionsin 3D space. Thus, ligands with different 2D scaffolds can bind to thesame protein if these ligands share similar interaction patterns. Molecularfields can represent those interaction profiles. For efficiency, the extrema ofthose molecular fields, named field points, are used to quantify the ligandsimilarity in 3D. The calculation of field points involves the evaluation of theinteraction energy between the ligand and a small probe shifted on a fine gridrepresenting the molecular surface. These calculations are computationallyprohibitive for large datasets of ligands, making field point representationsof molecules intractable for high-content screening. Here, we overcome thisroadblock by one-shot prediction of field points using generative neuralnetworks based on the molecular structure alone. Field points are predictedby training an SE(3)-Transformer, an equivariant, attention-based graphneural network architecture, on a large set of ligands with field point data.Initial data demonstrates the feasibility of this approach to precisely generatenegative, positive and hydrophobic field points within 1 Å of the groundtruth for a diverse set of drug-like molecules.