AMPBench-MT: A Homology-Controlled Benchmark for Antimicrobial Peptide Potency, Spectrum, and Safety Prediction

• URL: http://arxiv.org/abs/2607.25518v1
• Date: Tue, 28 Jul 2026 10:01:30 GMT
• Title: AMPBench-MT: A Homology-Controlled Benchmark for Antimicrobial Peptide Potency, Spectrum, and Safety Prediction
• Abstract summary: AMP and peptide benchmarks do not jointly place AMP recognition, species-conditioned potency, spectrum, safety-facing proxy endpoints, and cross-endpoint behavior.<n> AMPBench-MT standardizes canonical peptide records and organizes them into binary recognition, species-conditioned pMIC regression, and endpoint-specific potency and safety-facing readouts.
• Score: 3.534208235875249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computational AMP discovery is often evaluated through AMP/non-AMP recognition, yet follow-up decisions depend on assay-derived evidence such as target-species potency, hemolysis, toxicity, and selectivity. Existing AMP and peptide benchmarks cover binary recognition, multilabel annotation, assay regression, or broader peptide-model comparison, but they do not jointly place AMP recognition, species-conditioned potency, spectrum, safety-facing proxy endpoints, and cross-endpoint behavior within one sequence-homology-controlled protocol. To address this problem, we introduce AMPBench-MT, a provenance-preserving benchmark that standardizes canonical peptide records and organizes them into binary recognition, species-conditioned pMIC regression, and endpoint-specific potency and safety-facing readouts. Across 161 endpoint-specific model evaluations, high binary performance does not reliably indicate assay-endpoint behavior. Frozen protein-language-model embeddings form the leading pMIC error cluster, while graph and classical regressors remain close. Spectrum labels further reveal that PR-oriented metrics can be misleading under scarce observed negatives, whereas low-toxicity, HC50 hemolysis, and selectivity expose smaller but more assay-facing signals. AMPBench-MT shows that AMP evaluation should move beyond recognition leaderboards toward endpoint-aware evidence auditing. Our proposed benchmark is available at https://huggingface.co/datasets/ZihengZhou06/AMPBench-MT.

Related papers

• Prompt Embedding Probes (PEP): Hallucination Detection in LLMs from Hidden States [4.37919186746582]
  Large language models (LLMs) can generate fluent and useful responses but remain prone to hallucinations.<n>We introduce Prompt Embedding Probes (PEP), a white-box method for answer-level hallucination detection from the hidden states of a frozen LLM.<n> PEP improves hidden-state-based detection over standard linear probes in the main in-distribution setting.
  arXiv  Detail & Related papers  (2026-08-08T09:14:47Z)
• Decoupled PFNs: Identifiable Epistemic-Aleatoric Decomposition via Structured Synthetic Priors [25.166488691037078]
  Prior-Fitted Networks (PFNs) amortize Bayesian prediction by meta-learning over a synthetic task prior, but their standard output is a posterior predictive distribution over noisy observations.<n>We show that this epistemic--aleatoric split is not identifiable in general from the posterior predictive distribution alone.<n>We use these labels to train a decoupled PFN with separate latent-signal and aleatoric heads.
  arXiv  Detail & Related papers  (2026-05-07T15:22:35Z)
• PepSpecBench: A Unified Evaluation Benchmark for Peptide Tandem Mass Spectrometry Prediction [17.33669468355787]
  PepSpecBench is a unified benchmark for peptide MS/MS spectrum prediction.<n>It standardizes data preprocessing across complementary public datasets.<n>It also enforces a strict backbone-disjoint splitting strategy to eliminate sequence leakage.
  arXiv  Detail & Related papers  (2026-05-03T16:11:27Z)
• Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering [94.37535002230504]
  We develop a training-free, inference-time control framework termed Semantically Decoupled Latent Steering.<n>Our approach constructs a semantic-free intervention vector via large language model (LLM)-driven semantic decomposition.<n>We show that our approach significantly reduces the probability of historical hallucinations.
  arXiv  Detail & Related papers  (2026-02-27T04:49:01Z)
• SGAC: A Graph Neural Network Framework for Imbalanced and Structure-Aware AMP Classification [7.044114650607729]
  Classifying antimicrobial peptides(AMPs) from the vast array of peptides mined from metagenomic sequencing data is a significant approach to addressing the issue of antibiotic resistance.<n>Current AMP classification methods, primarily relying on sequence-based data, neglect the spatial structure of peptides, thereby limiting the accurate classification of AMPs.
  arXiv  Detail & Related papers  (2024-12-20T17:17:57Z)
• NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics [58.03989832372747]
  We present the first unified benchmark NovoBench for emphde novo peptide sequencing. It comprises diverse mass spectrum data, integrated models, and comprehensive evaluation metrics. Recent methods, including DeepNovo, PointNovo, Casanovo, InstaNovo, AdaNovo and $pi$-HelixNovo are integrated into our framework.
  arXiv  Detail & Related papers  (2024-06-16T08:23:21Z)
• REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic Entropy [93.8400683020273]
  Decoding methods for large language models (LLMs) usually struggle with the tradeoff between ensuring factuality and maintaining diversity. We propose REAL sampling, a decoding method that improved factuality and diversity over nucleus sampling.
  arXiv  Detail & Related papers  (2024-06-11T21:44:49Z)
• AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides [4.826446796830595]
  This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. AMPCliff quantifies the activities of AMPs by the MIC, and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations.
  arXiv  Detail & Related papers  (2024-04-15T12:40:12Z)
• Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection [81.07346419422605]
  Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. We propose a novel hierarchical semi-supervised contrastive learning framework, for contamination-resistant anomaly detection.
  arXiv  Detail & Related papers  (2022-07-24T18:49:26Z)
• Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes [65.91730154730905]
  In applications of offline reinforcement learning to observational data, such as in healthcare or education, a general concern is that observed actions might be affected by unobserved factors. Here we tackle this by considering off-policy evaluation in a partially observed Markov decision process (POMDP) We extend the framework of proximal causal inference to our POMDP setting, providing a variety of settings where identification is made possible.
  arXiv  Detail & Related papers  (2021-10-28T17:46:14Z)
• Targeted stochastic gradient Markov chain Monte Carlo for hidden Markov models with rare latent states [48.705095800341944]
  Markov chain Monte Carlo (MCMC) algorithms for hidden Markov models often rely on the forward-backward sampler. This makes them computationally slow as the length of the time series increases, motivating the development of sub-sampling-based approaches. We propose a targeted sub-sampling approach that over-samples observations corresponding to rare latent states when calculating the gradient of parameters associated with them.
  arXiv  Detail & Related papers  (2018-10-31T17:44:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.