Architectural good practices for reproducible benchmarking in protein machine learning

This project offers help with implementation and methods for building reproducible benchmark systems in protein machine learning. In computational biology, reliable evaluation is often blocked by datasets that can’t be traced, models that use inconsistent representations, and execution conditions that are poorly defined. To fix these problems, we set up a verifiable benchmarking framework built around five key requirements: * data curation that is deterministic, * artifacts that are stored permanently and version‑controlled, * clear interface contracts, * execution environments that can be reproduced, and * evaluation procedures that are transparent. We also add a decision‑readiness layer that deals with where predictions come from, how uncertainty is calibrated, and how

the model can be explained. The usefulness of these practices is shown in a case study on antimicrobial peptide classification. The study systematically compares different task definitions, ways of defining the negative class, methods for splitting the data, and controls for evolutionary similarity. By following this structured approach, researchers can avoid contamination from pre‑training data and keep protein model comparisons honest. This resource acts as a complete technical guide for creating robust, traceable, and standardized validation pipelines in bioinformatics.

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