BiologybioRxiv
Ranked by formulaVerdict: NotableNoiTox: A Machine Learning Strategy for Peptide Toxicity Prediction Reveals Label Noise in Benchmark Datasets
Peptide toxicity remains a major bottleneck in drug development, as small sequence variations can drastically alter cytotoxic profiles.
Caveats
- Preprint; not yet peer reviewed.
VerdictWorth a reader's time today.
Abstract
Motivation: Peptide toxicity remains a major bottleneck in drug development, as small sequence variations can drastically alter cytotoxic profiles. Experimental assessment is costly and time-consuming, motivating computational predictors. Existing approaches often rely on single black-box architectures, limiting interpretability and obscuring the contribution of individual sequence features. Furthermore, benchmark datasets frequently define the negative class through annotation filters rather than direct experimental evidence, introducing structural label noise that can compromises training and evaluation. Results: We present NoiTox, a multi-model strategy for peptide toxicity prediction integrating three complementary architectures, evaluated as four model variants: NoiToxML (Bo/full), an Extremely Randomized Trees classifier with Boruta-selected or complete physicochemical, compositional, and atomic descriptors; NoiToxDP, a deep learning model leveraging ESM-2 protein language model embeddings; and NoiToxGP, a graph neural network that combines ESM-2 residue embeddings, a fully connected residue graph with attention, and the Boruta descriptors as global features. Averaged over five random seeds, the models approached the state-of-the-art predictor ToxiPep on CD-HIT-controlled test sets, and on an independent dataset the interpretable NoiToxML variants and a probability-averaging consensus of the four models exceeded it, showing that physicochemical descriptors are highly informative for toxicity prediction. Cross-referencing negative-labeled sequences against curated databases of hemolytic, cytotoxic, and neurotoxic peptides flagged a small set annotated as toxic elsewhere, consistent with label noise by omission. However, removing the sequences that all models contradicted did not improve, and for some models reduced, ranking performance, indicating that such sequences carry useful information and are better treated as candidates for curation than discarded automatically. Availability and implementation: https://github.com/goalmeida05/NoiTox.
The editor's rubric
| Dimension | Level | Weight | What that level means |
|---|---|---|---|
| Leverage | ███░░ 3 | 24% | A method or resource many groups across the field will adopt within a year. |
| Magnitude | ███░░ 3 | 16% | Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem. |
| Evidence | ████░ 4 | 20% | Strong: large scale, preregistered, independently replicated, or a well-powered randomized trial. |
| Novelty | ███░░ 3 | 20% | A genuinely new approach to an open problem. |
| Trajectory | ██░░░ 2 | 10% | Some room to improve with obvious engineering. |
| Stakes | ██░░░ 2 | 10% | Benefits a professional community (practitioners, clinicians, engineers). |
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); gains (state of the art, outperforms); novelty (alternative to status quo); design (randomized); verification (multiple benchmarks, independent replication, code released); stakes (global scale).
How the score was computed
- Merit
- 6.0 / 10
- Adjusted merit
- 4.9 / 10
- Attention
- 0%
- Freshness
- 88%