**4. Model selection.** Best-performing models should be choosen for screening depending on the task complexity (sequence classification or sequence generation).
**5. Feature selecction.** After model selection, features used in the model should be choosen showing optimal prediction performance, robustness, and interpretability.
**5. Feature selection.** After model selection, features used in the model should be choosen showing optimal prediction performance, robustness, and interpretability.
**6. Evaluation.** Every model should be evaluated beyond performance on train/test datasets. It can be structural analysis of CPP candidates, modelling of interaction with cellular membranes etc.
ACID Design Lab
7 months ago
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GitHub