Ashley Babjac | Searcharxiv

arXiv2026

An Active Learning Framework for Data-Efficient, Human-in-the-Loop Enzyme Function Prediction

Generalizable protein function prediction is increasingly constrained by the growing mismatch between exponentially expanding sequences of environmental proteins and the comparatively slow accumulation of experimentally verified functional data. Active learning offers a promising path forward for accelerating biological function prediction, by selecting the most informative proteins to experimentally annotate for data-efficient training, yet its potential remains largely unexplored. We introduce HATTER (Human-in-the-loop Adaptive Toolkit for Transferable Enzyme Representations), a modular framework that integrates multiple active learning strategies with human-in-the-loop experimental annotation to efficiently fine tune function prediction models. We compare active learning training to standard supervised training for biological enzyme function prediction, demonstrating that active learning achieves performance comparable to standard training across diverse protein sequence evaluation datasets while requiring fewer model updates, processing less data, and substantially reducing computational cost.

q-bio.QM↗

arXiv2026

GRIMM: Genetic stRatification for Inference in Molecular Modeling

The vast majority of biological sequences encode unknown functions and bear little resemblance to experimentally characterized proteins, limiting both our understanding of biology and our ability to harness functional potential for the bioeconomy. Predicting enzyme function from sequence remains a central challenge in computational biology, complicated by low sequence diversity and imbalanced label support in publicly available datasets. Models trained on these data can overestimate performance and fail to generalize. To address this, we introduce GRIMM (Genetic stRatification for Inference in Molecular Modeling), a benchmark for enzyme function prediction that employs genetic stratification: sequences are clustered by similarity and clusters are assigned exclusively to training, validation, or test sets. This ensures that sequences from the same cluster do not appear in multiple partitions. GRIMM produces multiple test sets: a closed-set test with the same label distribution as training (Test-1) and an open-set test containing novel labels (Test-2), serving as a realistic out-of-distribution proxy for discovering novel enzyme functions. While demonstrated on enzymes, this approach is generalizable to any sequence-based classification task where inputs can be clustered by similarity. By formalizing a splitting strategy often used implicitly, GRIMM provides a unified and reproducible framework for closed- and open-set evaluation. The method is lightweight, requiring only sequence clustering and label annotations, and can be adapted to different similarity thresholds, data scales, and biological tasks. GRIMM enables more realistic evaluation of functional prediction models on both familiar and unseen classes and establishes a benchmark that more faithfully assesses model performance and generalizability.

q-bio.QM↗