arXiv:2606. 04021v1 Announce Type: cross Abstract: Proteolysis-targeting chimeras (PROTACs) can selectively degrade disease-causing proteins, yet predicting which targets are amenable to degradation remains a critical bottleneck: existing computational methods require the complete PROTAC molecular structure, information unavailable before synthesis.
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.
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.
SMILESGNN is a multimodal architecture that fuses a SMILES Transformer encoder with a GATv2 graph encoder through cross‑attention, enabling interpretable clinical toxicity predictions. The model retains an explicit graph branch, allowing GNNExplainer to identify substructures linked to toxicity. On the ClinTox dataset it achieves an AUC‑ROC of 0.987 and F1 of 0.906 with only 0.4 M parameters, while on Tox21 it attains a mean AUC‑ROC of 0.750, comparable to strong single‑modality baselines.
arXiv:2607. 04557v1 Announce Type: cross Abstract: Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles.
Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles. Preclinical transfer-learning models can simulate drug-induced expression changes but are often hard to interpret and unstable, whereas knowledge-graph methods provide mechanistic context yet remain static and fail to capture drug-induced transcriptomic perturbation dynamics.
arXiv:2608. 12906v1 Announce Type: cross Abstract: RNA-Protein Interactions (RPIs) are critical for regulating cellular functions.
arXiv:2608. 04257v1 Announce Type: new Abstract: Blood-brain barrier permeability (BBBP) prediction is a critical screening task in central nervous system drug discovery, where candidate molecules must be assessed for whether they can cross, or should be prevented from crossing, the blood-brain barrier.
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.
arXiv:2606. 09898v1 Announce Type: new Abstract: Cancer treatment planning requires decisions across multiple clinical dimensions at once.
ToxLens is a reproducible multi‑task graph‑learning framework designed for leakage‑aware, uncertainty‑calibrated prediction of 11 molecular toxicity endpoints, including Ames mutagenicity and hERG inhibition. The workflow integrates conservative chemical curation, sphere‑exclusion filtering, a leakage‑aware UMAP‑HDBSCAN split, parallel graph and global‑feature encoders with late concatenation, temperature‑scaled Monte Carlo dropout, conformal‑style prediction sets, applicability‑domain analysis, and SHAP‑guided toxicophore discovery with occlusion controls. On a leakage‑controlled test fold, a five‑seed soft‑voting ensemble achieved MCC 0.44, AUROC 0.83, and AUPRC 0.58, outperforming four ECFP4‑based shallow baselines across all endpoints.
arXiv:2607. 24314v1 Announce Type: new Abstract: Predicting the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small molecules remains a major challenge in drug discovery.
StabilityArc is a method that decodes protein sequence embeddings into generalizable stability landscapes. It uses a shared RoPE transformer to map frozen ESMC-600M residue representations into an Lx20 matrix of substitution effects, with a symmetric, contact-aware residual to predict epistasis. In extensive leave-one-protein-out tests on 134,794 ProteinGym variants, StabilityArc achieves a Spearman correlation of 0.7134, surpassing the best zero‑shot baseline, and further improves Kermut’s performance when used as a prior.