Kernel-Based Evaluation of Conditional Biological Sequence Models
This work addresses the need for better evaluation methods in computational biology, specifically for conditional sequence models, though it appears incremental as it builds on existing kernel-based discrepancy measures.
The authors tackled the problem of evaluating and tuning conditional biological sequence models by proposing kernel-based tools, including the Augmented Conditional Maximum Mean Discrepancy (ACMMD), and demonstrated their utility by rejecting the fit of ProteinMPNN for various protein families and improving its hyperparameter tuning.
We propose a set of kernel-based tools to evaluate the designs and tune the hyperparameters of conditional sequence models, with a focus on problems in computational biology. The backbone of our tools is a new measure of discrepancy between the true conditional distribution and the model's estimate, called the Augmented Conditional Maximum Mean Discrepancy (ACMMD). Provided that the model can be sampled from, the ACMMD can be estimated unbiasedly from data to quantify absolute model fit, integrated within hypothesis tests, and used to evaluate model reliability. We demonstrate the utility of our approach by analyzing a popular protein design model, ProteinMPNN. We are able to reject the hypothesis that ProteinMPNN fits its data for various protein families, and tune the model's temperature hyperparameter to achieve a better fit.