Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include training a neural network and predicting structural feature sets with the neural network. The operations may include producing predicted structures with the neural network using the structural feature sets, converting the predicted structures into predicted graphs with predicted edges, and comparing predicted graphs to training graphs and predicted edges to training edges to obtain a comparison. The operations may include training a model with the comparison, constructing a graph with the neural network using a node feature set, and reducing missing edges in the graph with the model.

Abstract: A method, computer system, and a computer program product for designing one or more folded structural proteins from at least one raw amino acid sequence is provided. The present invention may include computing one or more character embeddings based on the at least one raw amino acid sequence by utilizing a multi-scale neighborhood-based neural network (MNNN) model. The present invention may then include refining the computed one or more character embeddings with at least one set of sequence neighborhood information. The present invention may further include predicting one or more dihedral angles based on the refined one or more character embeddings.