GraphBNC is a framework that combines graph theory based methods, machine learning and other computational tools for placing protected gold nanoclusters on blood proteins. Machine learning part, artificial neural networks (ANNs) in this case, estimates interactions between the ligand molecules of the nanocluster and amino acid residues, which are used to find favorable site for the nanocluster on the protein. This dataset contains basic source codes needed to run the method. The first part contains methods to encode the nanoclusters and the proteins. The second part has the codes to train and to test ANNs, but pretrained weights are also provided. The rest focuses on the placement of the cluster utilizing Monte Carlo -based simulated annealing. This is a snapshot of the code dataset that has been taken on 05.07.2024. A more detailed description of the data and the address to the GitLab repository for the latest version of the code can be found from the parent dataset of this data publication.

2024