Assessing the selectivity of binding a small molecule with proteins remains a fundamental yet unresolved challenge in computational drug discovery. Conventional strategies for estimating ligand selectivity depend on exhaustive cross-prediction of binding affinities across thousands of potential off-target proteins—an approach that is computationally prohibitive for large-scale virtual screening or de novo molecular design. Here, we present DrugForm-TAS (Target-Agnostic Selectivity), the first model capable of directly predicting an unconditional, quantitative measure of small-molecule proteome-wide binding propensity without requiring any prior knowledge of target proteins or affinity thresholds. Built upon our previously developed DrugForm-DTA model, which enables affinity prediction for arbitrary protein targets, and trained on a thoroughly curated dataset derived from BindingDB, DrugForm-TAS employs a lightweight transformer-like neural network that operates solely on a ligand’s SMILES representation. The model acts as a fast pre-screening filter that can greatly reduce the candidate set, as evidenced by the correlation between its predictions and experimental observations. By eliminating the need for target-specific computations, DrugForm-TAS enables target-agnostic nonspecificity profiling. When combined with drug–target affinity calculation, it provides a novel tool for fast pre-screening selectivity estimation in early-stage drug design.

Cellular senescence is a key driver of aging and chronic diseases. However, accurately identifying senescent cells is challenging due to limitations of conventional biomarkers and senescence heterogeneity. Transcriptome-wide analyses offer powerful tools for deciphering cellular states. Yet, there is a critical gap in computational frameworks for senescence assessment from transcriptomic data. We developed the seneR package, which includes functions such as calculating senescence identity scores (SID scores), assessing senescence-related phenotypes, and plotting senescence trajectories, and provides an interactive Shiny interface. We applied seneR to transcriptome datasets from human islets and chondrocytes to investigate the role of senescence in Type 2 Diabetes (T2D) and osteoarthritis (OA). Additionally, in vitro validation confirmed phentolamine (PM)'s potential to delay chondrocyte senescence. seneR accurately identified senescent cells and revealed senescence-related phenotypes in transcriptome datasets. In T2D, SID scores were significantly higher in elderly islets. Senescent islet cells exhibited diminished responsiveness to nutrient stimuli, linking senescence to impaired insulin secretion. In OA, seneR identified SLPI as a molecule strongly associated with chondrocyte senescence, with PM treatment reducing SID scores. Trajectory analysis revealed chondrocyte senescence progression and potential therapeutic targets. In vitro experiments, PM reversed both IL-1β- and H₂O₂-induced chondrocyte senescence. Our study demonstrates that seneR is a valuable tool for assessing cellular senescence from transcriptomic data, revealing key phenotypes and potential therapeutic targets in T2D and OA. The identification of SLPI as a senescence-associated molecule and the therapeutic potential of PM highlights the utility of our approach in understanding senescence-related diseases.