← Back to all news
arXiv Machine Learning By Raunak Kumar, Anuj Pal, Dhruvi Solanki, Parikshit Pareek, Juhi Singh, Jitin Singla

Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling

Read the original on arXiv Machine Learning →

The study demonstrates that a simple, sequence-only approach using 330 interpretable descriptors and the TabPFN tabular foundation model can outperform complex multimodal deep learning methods for multi-label antimicrobial peptide activity prediction. On the ESCAPE benchmark (82,359 peptides, five labels), a label‑powerset TabPFN model achieved a mean average precision of 77.8%, surpassing the previous best of 72.1%. The approach also shows that predicted structure is unnecessary, that a small set of global physicochemical scalars can recover most performance, and that modeling label dependence benefits rare activities and informs assay prioritization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
2d ago

FLaG: Frequency-Domain Latent-attention Gated Pooling for Token Aggregation

FLaG (Frequency‑Domain Latent‑attention Gated Pooling) is a plug‑in token‑aggregation module that transforms encoder outputs into the Fourier domain, summarizes spectral tokens with learnable latent queries, applies a sample‑conditioned channel gate, and reconstructs modulated token representations for downstream pooling. The method is evaluated on antimicrobial peptide activity prediction, CIFAR‑10/100 image classification, and several RoBERTa language tasks, achieving state‑of‑the‑art performance on most metrics. Analyses show that FLaG emphasizes low‑frequency components while selectively amplifying high‑frequency signals in later layers, providing a transferable frequency‑domain bias across protein, visual, and textual representations.

By Kewei Li, Rongying Zhang, Xueli Wang, Xiwen Gong, Zhongjian Wang, Qiuchen Zhao, Lan Huang, Ruochi Zhang, Fengfeng Zhou
computer-visionsafety
More like this →
arXiv Machine Learning
Aug 20

Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference

Monroe is a new molecular foundation model that improves upon existing models by pre‑training on over 81 million molecules from the PM6 quantum chemistry dataset, enhancing stereochemistry representation, and introducing novel training losses such as conformer denoising and embedding decorrelation. It also incorporates a prior‑data‑fitted model (TabPFN) for downstream in‑context prediction and demonstrates superior performance on Polaris benchmarks and activity cliff tests. Ablation studies show that the PFN‑based downstream approach can upgrade other models, producing state‑of‑the‑art variants MiniMol_PFN and CheMeleon_PFN.

By Blazej Banaszewski, Andrew W. Fitzgibbon
ragbenchmarks
More like this →