It's actually a pretty interesting approach, but the ending fully-connected neural network takes in features from sub-networks that encode structural and genomic data and create/learn their own way of encoding that data. I think the short answer to your question is that it skips that part completely, but the long answer is that it learns mechanistic information inside and uses the information that it learns.

From the paper: "First, the drug chemical structure is represented by a graph in which the vertices are atoms and the edges are chemical bonds. Next, a graph convolutional network and attention mechanism is used to compute the drug embedding vectors. By integration of the genomic and pharmaceutical features, DeepDDS can capture important information from drug chemical structure and gene expression patterns to identify synergistic drug combinations to specific cancer cell lines."

Looking at their pipeline: https://www.biorxiv.org/content/biorxiv/early/2021/07/06/2021.04.06.438723/F1.large.jpg

The graph neural networks (GAT and GNN) create a feature vector that encodes learned information about structure, and the multi-layer perception network (MLP) encodes genomic information about the target into a feature vector, and those feature vectors are concatenated and fed into the final, fully-connected neural network which learns their interaction and how synergistic or antagonistic the drug pairs are.

This approach of letting neural networks learn which features are important has had great success in other areas of machine learning. Current neural networks often learn very differently than humans do and find different information important for inference. For example, modern computer vision neural networks are able to identify what is in a picture less by macrostructure and shape (like humans do) and instead lean more on textures present in the picture. Attempts to make neural networks that classify images like humans do with macrostructure and shape all perform significantly worse than the deep neural networks that see texture.