Hi @rkamales - based on the error message, it looks like there are no common UniRef90 IDs between the pre-trained model and your data. Since we did some filtering to remove a fraction of UniRef90 IDs with low prevalence/abundance, this is not surprising. I am not sure if this is an after-effect of your own filtering (if you are doing any) or just a property of your dataset that it does not contain shared features with our pre-trained model. If former, you might want to play with the filtering threshold/criteria to see if that improves the number of shared features. If later, there is not much we can do unless you have paired training data (metabolites and gene families) and you are able to build your own model to get the predictions using melonnpan.train.