To get a full understanding of your problem, one would like to know what approximately the $n$-features are.
Whether, it is about the geometrical structure, protein is described by a graph, where vertices correspond to atoms and edges to bonds within them - I would consider use of
GraphNN, there is some research, that has demonstrated the success of
GraphNN for protein prediction:
In case, your data is some general form tabular data with features of different nature and form - like some continuous features, binary features, categorical features, whatever, there is no notion of proximity between different features.
The efficiency of CNN is based a lot on the ability of them to have a notion of locality, aggregate the information from the neighborhood, and construct the hierarchy of low-level (for several neighboring pixels) features and global (that understand the image or a signal as a whole) features.
Two neighboring pixes from the cat's ear have a notion of proximity and relatedness, whereas the red color and square shape do not have.
For unstructured tabular data I would recommend to start from some
tree ensembling approach:
- Random forest
- Gradient boosting
Seems like what you receive as input is a matrix of pairwise distances $\rho(i, j)$ between all possible amino residues - Protein contact map.
I would think about it as a weighted adjacency matrix. However, there is a rather special structure,
Therefore, there is no need for generic GNN, most likely.
In the literature, the recent research solves this problem with the help of CNN:
Probably, you should tune some hyperparameters, or introduce residual connections, if there are no such at the moment.