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arXiv:2607. 04774v1 Announce Type: new Abstract: Untargeted tandem mass spectrometry (MS/MS) detects thousands of small molecules per biological sample, yet most go unidentified because they are absent from spectral libraries.
arXiv:2607. 22777v1 Announce Type: cross Abstract: Protein language models learn transferable sequence representations.
MT-ProtBERT is a multi‑task extension of ProtBERT designed for classifying intrinsically disordered proteins (IDPs) in low‑data settings. It combines Dynamic Window Masking, a Multi‑Scale 1D Convolutional classifier, and auxiliary biochemistry‑informed objectives to jointly optimize masked language modeling and domain‑specific tasks. In experiments on phosphorylation site prediction and protein compaction prediction, MT‑ProtBERT outperforms the RNN‑based IDP model PARROT across all limited‑data tasks.
ProteinJEPA introduces a joint‑embedding predictive architecture that supplements masked language modeling (MLM) with a cosine loss to predict latent representations of a teacher model. On 19 protein tasks, MLM+JEPA outperforms compute‑matched and step‑matched MLM‑only training across 78 and 76 of 114 comparisons, achieving notable gains on structure‑ and homology‑sensitive tasks such as SCOPe‑40 retrieval and remote homology. Ablation studies show the cosine loss is superior to mean squared error and that latent prediction complements rather than replaces MLM.
GyroNovo is a new framework for de novo peptide sequencing that improves fragment imputation by guiding the process with decoder errors observed during training. It introduces mass-aware attention using rotary embeddings to encode pairwise mass differences between spectral peaks, and creates easy and hard augmented views of spectra to train the decoder under varying corruption levels. Experiments on NovoBench demonstrate significant gains, with about 9 percentage points higher peptide-level precision and 7 percentage points higher amino-acid-level precision compared to the state-of-the-art baseline.
arXiv:2604. 26498v3 Announce Type: replace Abstract: The rapid growth of molecular foundation models and large language models (LLMs) has encouraged a scale centred view of AI in drug discovery, in which larger pretrained models are expected to supersede compact cheminformatics models.