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.
By Raunak Kumar, Anuj Pal, Dhruvi Solanki, Parikshit Pareek, Juhi Singh, Jitin Singla
arXiv:2606. 08191v1 Announce Type: cross Abstract: Token aggregation is a common bottleneck in models that map token representations to sample-level predictions, yet most pooling methods operate only in the original token domain.
By Kewei Li, Rongying Zhang, Xueli Wang, Xiwen Gong, Zhongjian Wang, Lan Huang, Ruochi Zhang, Fengfeng Zhou
arXiv:2606. 17127v1 Announce Type: cross Abstract: Antimicrobial resistance causes to over a million deaths annually.
By Jay Jung, Xiaohan Zhang, Shenghan Song, Mahmoud Sayedahmed, Chijian Xiang, Yunong Xu, Ahmed AbdelKhalek, Severin T. Schneebeli, Matthew J. Wargo, Jianing Li, Safwan Wshah
arXiv:2606. 31126v1 Announce Type: new Abstract: Predicting biomolecular properties from limited labeled data is a central bottleneck in protein engineering and small-molecule design.
By Davy Guan, Lu Zhang, Asiri Wijesinghe, Allen Zhu, He Zhao, Helen Power, F. Hafna Ahmed, Andrew Warden, Cheng Soon Ong, Daniel M. Steinberg
arXiv:2504. 17247v3 Announce Type: replace Abstract: Deep learning-based antimicrobial peptide (AMP) discovery faces critical challenges such as limited controllability, lack of representations that efficiently model antimicrobial properties, and low experimental hit rates.
By Diogo Soares, Leon Hetzel, Paulina Szymczak, Marcelo Der Torossian Torres, Johanna Sommer, Cesar de la Fuente-Nunez, Fabian Theis, Stephan G\"unnemann, Ewa Szczurek
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
arXiv:2608. 08182v1 Announce Type: cross Abstract: Machine learning models for MALDI-TOF mass spectrometry have shown considerable promise for clinical microbiology tasks such as microbial identification and antimicrobial resistance prediction.
By Alejandro L. Garc\'ia-Navarro, Carlos Sevilla-Salcedo, Bel\'en Rodr\'iguez-S\'anchez, Vanessa G\'omez-Verdejo
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
The study evaluates the use of default decision thresholds (t=0.50) in multi‑label enzyme commission (EC) number prediction across 14,096 compounds and six EC classes. It finds a high mean accuracy of 77.16% but low macro F1 (0.3976) and macro recall (0.3872), indicating severe class‑imbalance issues: majority classes are over‑predicted while minority classes, especially EC6, have zero recall despite reasonable ROC‑AUC. The authors recommend target‑specific threshold tuning and conformal calibration as post‑processing safeguards to expose and correct these hidden errors.
By Bilal Ahmad, Rajed Mehmood
arXiv:2509.23552v2 Announce Type: replace-cross
Abstract: Antimicrobial Resistance (AMR) is a rapidly escalating global health crisis. While genomic sequencing enables rapid prediction of resistance...
By Md. Saiful Bari Siddiqui, Nowshin Tarannum
arXiv:2602. 22822v3 Announce Type: replace Abstract: Tandem mass spectrometry (MS/MS) is central to small molecule identification, but current deep learning systems for spectrum prediction still remain difficult to evaluate and deploy in practice.
By Yunhua Zhong, Yixuan Tang, Yifan Li, Pan Liu, Zhiwen Yang, Jie Yang, Jun Xia
arXiv:2608. 10595v1 Announce Type: cross Abstract: Proteolysis-targeting chimeras (PROTACs) induce protein degradation by recruiting a target protein to an E3 ubiquitin ligase, making degradation a joint outcome of the degrader molecule and its biological context.
By Dong Xu, Zhangfan Yang, Jiantao Wu, Zexuan Zhu, Jianqiang Li, Junkai Ji