Trustworthy Protein AI: Evaluation, Uncertainty, and Failure Modes

AI in protein science is advancing fast, from structure prediction and protein language models to function annotation, mutational effect prediction, docking, and de novo design. Yet as these models move from benchmarks into real scientific workflows, a central question becomes unavoidable: when should we trust a prediction, and when should we not? High apparent accuracy can hide brittle behavior, data leakage, unclear applicability domains, miscalibrated confidence, and silent failures that only surface in downstream experimental or clinical settings.This Research Topic aims to bring together work that strengthens the reliability, interpretability, and rigorous evaluation of AI methods in protein bioinformatics. We welcome contributions that develop or apply trustworthiness frameworks for protein-focused models, including robust benchmarking, uncertainty quantification, detection of out-of-distribution inputs, and systematic characterization of failure modes. Submissions may address models across the full spectrum of protein tasks, from sequence-to-function and sequence-to-structure to protein–protein interactions, binding and affinity prediction, and generative protein design.A particular focus of this Topic is the gap between reported performance and real-world utility. We encourage studies that examine how dataset construction, homology structure, train/test splitting choices, and evaluation metrics influence conclusions—and how to design assessments that better reflect deployment conditions (e.g., novel families, engineered sequences, sparse labels, noisy measurements, conflicting annotations, or shifting experimental protocols). We also invite work on reproducibility and transparency, including reporting standards, model documentation, and evaluation practices that help the community compare methods fairly.This Topic welcomes original research, methods, benchmarks, perspectives, and reviews that advance trustworthy protein AI, including (but not limited to):o Evaluation and benchmarkingo Leakage-aware splits, homology-aware evaluation, temporal splits, and distribution shift testingo Metric design beyond aggregate accuracy (e.g., per-family, per-length, per-architecture, tail-risk performance)o Benchmark suites for real-world scenarios (few-shot, noisy labels, long sequences, multi-domain proteins)o Uncertainty quantification and calibrationo Predictive uncertainty for classification/regression and structured outputs (e.g., structures, contacts, bindings)o Calibration methods, conformal prediction, credible sets, and selective prediction/abstentiono Linking uncertainty to experimental decision-making and resource allocationo Failure modes and robustnesso Adversarial or stress testing, counterfactuals, and sensitivity to artifacts (e.g., tags, truncations, alignment choices)o Out-of-distribution detection and applicability domains for protein modelso Systematic error taxonomies for protein LMs, structure predictors, and generative modelso Interpretability and diagnosticso Mechanistic interpretability, attribution methods, probing analyses, and representation diagnosticso Model behavior audits: spurious correlations, shortcut learning, and confounding biological signalso Interpretable uncertainty explanations (why the model is unsure, not just that it is)o Reproducibility, transparency, and responsible deploymento Reporting checklists, model cards/datasheets, reproducible pipelines, and open evaluation protocolso Evaluation of toolchains and end-to-end workflows (model + post-processing + downstream inference)o Ethical and safety considerations for generative protein models (misuse, dual use, and safeguards)By convening methodological advances and critical evaluations, this Research Topic seeks to establish best practices and shared standards for protein AI—helping the field move from impressive demos toward dependable, measurable, and actionable computational predictions.The following Article Types are accepted: A-Type Articles: Original Research, Systematic Review, Methods, Review, Hypothesis and Theory, Technology and CodeB-Type Articles: Mini Review, Perspective, Case Report, Data Report, Policy Brief, Brief Research Report, General Commentary, Opinion Topic editors: Rahul Kaushik, Rita Casadio Source: https://www.frontiersin.org/research-topics/78847/trustworthy-protein-ai-evaluation-uncertainty-and-failure-modes