1Predicting structure-dependent functionalities of biomolecules is crucial for accelerating 2 a wide variety of applications in drug-screening, biosensing, disease-diagnosis, and 3 therapy. Although the commonly used structural "fingerprints" work for biomolecules in 4 traditional informatics implementations, they remain impractical in a wide range of 5 machine learning approaches where the model is restricted to make data-driven 6 decisions. Although peptides, proteins, and oligonucleotides have sequence-related 7 propensities, representing them as sequences of letters, e.g., in bioinformatics studies, 8 causes a loss of most of their structure-related functionalities. Biomolecules lacking 9 sequence, such as polysaccharides, lipids, and their peptide conjugates, cannot be 10 screened with models using the letter-based fingerprints. Here we introduce a new 11 fingerprint derived from valence shell electron pair repulsion structures for small peptides 12 that enables construction of structural feature-maps for a given biomolecule, regardless 13 of the sequence or conformation. The feature-map introduced here uses a simple 14 encoding derived from the molecular graph -atoms, bonds, distances, bond angles, etc., 15 that make up each of the amino acids in the sequence, allowing a Residual Neural 16 network model to take greater advantage of information in molecular structure. We make 17 use of the short peptides binding to Major-Histocompatibility-Class-I protein alleles that 18 are encoded in terms of their extended structures to predict allele-specific binding-19 affinities of test-peptides. Predictions are consistent, without appreciable loss in accuracy 20 between models for different length sequences, marking an improvement over the current 21 models. Biological processes are heterogeneous interactions, which justifies encoding all 22 biomolecules universally in terms of structures and relating them to their functionality. The 23 capabilities facilitated by the model expands the paradigm in establishing structure-24 function correlations among small molecules, short and longer sequences including large 25 biomolecules, and genetic conjugates that may include polypeptides, polynucleotides, 26RNAs, lipids, peptidoglycans, peptido-lipids, and other biomolecules that could be 27 implemented in a wide range of medical and nanobiotechnological applications in the 28 future. 29