Reverse target fishing seeks protein targets for a query molecule, a key step in drug discovery. The authors reframe this as a generative retrieval problem, introducing GenFish, which uses residual vector quantization to map protein language model features to shared semantic identifiers and autoregressively predicts protein codes from a molecular fingerprint. They also present ProTargetBench, a proteome‑scale benchmark covering 20,431 human proteins that distinguishes experimentally inactive from unmeasured associations, and show that GenFish outperforms both discriminative neural and non‑neural baselines on full‑catalogue retrieval metrics.
arXiv:2607. 17671v1 Announce Type: new Abstract: Large-scale single-cell perturbation atlases make it possible to ask an inverse question: given an observed transcriptional response, which annotated targets and compounds in a fixed library are most consistent with that response?
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.
arXiv:2608.25466v2 Announce Type: replace-cross Abstract: The functional annotation of genes in non-model organisms remains a significant challenge in computational biology, with 20-70% of sequenced...
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. Although public databases contain thousands of structured molecule-target-E3 records, degradation measurements are available for only a small fraction of them.
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.
arXiv:2608. 01734v1 Announce Type: new Abstract: Predicting transcriptomic responses to small-molecule perturbations across cell lines is central to drug discovery, but exhaustive profiling of drug-cell combinations is infeasible.
arXiv:2606. 16044v1 Announce Type: new Abstract: Protein language models (pLMs) can generate novel protein sequences with properties beyond those observed in nature, yet the mechanisms underlying protein generation remain poorly understood.
arXiv:2603. 25062v2 Announce Type: replace Abstract: Autoregressive molecular models assign probability to molecular serializations even though chemical identity is invariant to serialization.
arXiv:2608. 12090v1 Announce Type: new Abstract: Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology.
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.
RAGenome is a retrieval‑based genomic language model that extends pretraining to contexts 100 times longer than existing MSA‑based models, enabling it to capture both cross‑species evolutionary relationships and within‑species long‑range interactions. Trained on whole‑genome alignments from 100 vertebrates, it improves gene‑finding performance from 0.45 to 0.60 while staying competitive on evolutionary tasks such as prioritizing pathogenic variants. The model offers competitive performance at a fraction of the training cost, unifying evolutionary modeling and long‑range capabilities in a single scalable framework.
TopU-LBVS is a new multi‑target benchmark for ligand‑based virtual screening that addresses shortcomings of existing datasets by using hard‑negative decoys and a fixed 1:40 active‑to‑decoy ratio. It covers 93 protein targets across seven classes, provides three evaluation protocols (full, low‑data, and mini), and includes curated ChEMBL‑35 bioactivity data with property‑matched, structurally similar decoys. The benchmark demonstrates that performance drops sharply when moving from random‑decoy to hard‑negative evaluation, and it releases data, splits, code, and baseline implementations for reproducible comparison.