Over the past year, I have been working on building a graph-based paratope (antibody binding site) prediction tool – Paragraph. Fortunately, I have had moderate success with this and you can now check out the preprint of this work here.

However, for a long time, I struggled with a highly unstable network, where different random seeds yielded very different results. I believe this instability was largely due to the high class imbalance in my data – only ~10% of all residues in the Fv (variable region of the antibody) belong to the paratope.

I tried many different things in an attempt to stabilise my training, most of which failed. I will share all of these ideas with you though – successful or not – as what works for one person/network is never guaranteed to work for another. I hope that the below may provide some ideas to try out for others facing similar issues. Where possible, I also provide some example hyperparameter values that could act as sensible starting points.