Nonbonding molecular interactions, such as hydrogen bonding, hydrophobic contacts, ionic interactions, etc., are at the heart of many biological processes, and their appropriate treatment is essential for the successful application of numerous computational drug design methods. This paper introduces GRADE, a novel interaction fingerprint (IFP) descriptor that quantifies these interactions using floating point values derived from GRAIL scores, encoding both the presence and quality of interactions. GRADE is available in two versions: a basic 35-element variant and an extended 177-element variant. Three case studies demonstrate GRADE's utility: (1) dimensionality reduction for visualizing the chemical space of protein-ligand complexes using Uniform Manifold Approximation and Projection (UMAP), showing competitive performance with complex descriptors
(2) binding affinity prediction, where GRADE achieved reasonable accuracy with minimal machine learning optimization
and (3) three-dimensional-quantitative structure-activity relationship (3D-QSAR) modeling for a specific protein target, where GRADE enhanced the performance of Morgan Fingerprints.