The fastest way to ensure your AI protein engineering project produces useless results is to sabotage it from the start with a poorly constructed dataset.
Rule 1: Embrace the Noise. Accuracy is a Guideline, Not a Rule
Skip biological replicates. Rely on single measurements. Average replicates to hide variability. And whatever you do, withhold raw data from your models.
If you actually want results: run biological replicates, report variance, and give models access to the full distribution of measurements. Single-point data without error bars is not data. It’s a guess.
Rule 2: Stick to What You Know. Diversity is Overrated
Include only successful variants. Exclude negative data. Limit mutations to familiar sequences.
If you actually want results: negative data is as informative as positive data. A model that only sees winners cannot learn what failure looks like. Include the full spectrum of functional outcomes, and diversify your sequence space beyond the comfortable neighborhood of known hits.
Rule 3: Keep It Interesting. The Virtue of Inconsistency
Alter protocols between batches. Mix incompatible assays without labeling sources.
If you actually want results: standardize protocols across all experiments. Label every data point with its source assay, batch, and conditions. Batch effects are real, and unlabeled inconsistencies become invisible confounders that corrupt model training.
Rule 4: Trust Your Gut. Process Data Aggressively
Normalize everything. Pool diverse variants into single measurements.
If you actually want results: minimal, transparent processing. Document every transformation applied to raw data. Avoid collapsing distinct measurements into summary statistics unless the model explicitly requires it.
Conclusion: From Misdirection to Meaningful Results
Building a high-quality dataset for AI protein engineering requires deep expertise and rigorous execution. The experimental design, data collection, and curation steps matter as much as the model architecture. Partnering with an experienced team that understands both the biology and the machine learning is the most reliable path to datasets that actually work.