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 the metadata for the parent repository of the codes. Updates and possible corrections are documented in the GitLab project, where the material saved and shared. The GitLab project can be found and downloaded from the following address: https://gitlab.jyu.fi/graphbnc-project-group/graphbnc

2024