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.
arXiv:2607. 06605v1 Announce Type: new Abstract: Conformal prediction is being adopted in drug discovery to put an honest number on model reliability: pick an error rate alpha, and the method returns prediction sets containing the true label with probability at least 1 - alpha.
The paper introduces a conformal prediction framework designed for molecular property prediction under label shift. By weighting conformal scores with marginal label probability ratios, it generates statistically rigorous prediction intervals without retraining, enabling robust uncertainty quantification when property distributions change. This approach provides actionable confidence measures that improve the reliability of AI-driven predictions in drug discovery.
arXiv:2603. 10950v2 Announce Type: replace Abstract: Machine learning methods for identifying molecular structures from tandem mass spectra (MS/MS) have advanced rapidly, yet current approaches still exhibit significant error rates.
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.
Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classification. The ESCAPE...
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?
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.
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.
arXiv:2608. 07609v1 Announce Type: cross Abstract: High-throughput screening (HTS) assays are central to early-stage drug discovery but are often limited by extreme data sparsity, as primary screens typically use only a single replicate per test substance.
arXiv:2606. 07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns.
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:2505. 16941v4 Announce Type: replace-cross Abstract: Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of labeled data, (ii) task specificity, and (iii) poor transportability.