Figure 2. GNNs are trained and then optimized.

This example illustrates the GNN's mode of action in computer-assisted peptide design: In the training set, the model is taught the properties of the current peptides (biological activity, stability) and the adopted cells build virtual peptides that are evaluated in the genetic algorithm-based optimization. The peptide optimization process is organized in multiple consecutive cycles. The start population of peptides is based on experts' knowledge concerning the target, e.g. natural ligands, known analogs or compounds that bind to similar targets. The trained GNN is used as a fitness function in a genetic algorithm. Newly generated sequences then have to be synthesized and analyzed in biological assays, before the next GNN training is initiated.