Great responses so far. One other thing to consider is the purpose of this model. Will it be used to make inferences on out-of-sample data? If so, you should make sure that the form of data you're training on is representative of the form of data you'll have operationally.

In other words, will the out of sample data also have five replicates like you have for your training data? If not, then you should train using all five replicates, not an average. Otherwise, your out-of-sample data will have variance that has been averaged out by your training process in a way you cannot perform on the new data.

If this model isn't for prediction, you have more flexibility.