AMPBench-MT：用于抗菌肽效力、光谱和安全性预测的同源性控制基准
AMPBench-MT: A Homology-Controlled Benchmark for Antimicrobial Peptide Potency, Spectrum, and Safety Prediction
- Shanghai Ocean University（上海海洋大学）
- Tongji University（同济大学）
- Center for Safe AGI（安全通用人工智能中心）
- DP Technology（DP技术公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对抗菌肽计算发现评估问题，引入AMPBench-MT基准，将多种相关指标纳入同源性控制协议，通过模型评估揭示高二元性能与实验终点行为不符，指出应转向终点感知证据审核，为抗菌肽评估提供新方法。
AI中文摘要：
计算抗菌肽（AMP）发现通常通过AMP/非AMP识别进行评估，但后续决策取决于如目标物种效力、溶血、毒性和选择性等实验衍生证据。现有基准未将AMP识别、物种条件效力、光谱、安全相关代理终点和跨终点行为置于一个序列同源性控制协议中。为此引入AMPBench-MT基准，标准化肽记录并组织成二元识别、物种条件pMIC回归等。161个特定终点模型评估表明，高二元性能不能可靠指示实验终点行为。冻结的蛋白质语言模型嵌入形成主要pMIC误差簇。光谱标签显示，在观察到的阴性样本稀缺时，面向PR的指标可能有误导性。AMPBench-MT表明AMP评估应从识别排行榜转向终点感知证据审核。
英文摘要：
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.
