arXiv:2608. 09100v1 Announce Type: new Abstract: Millions of chemical structures appear in patents and papers only as drawings, and using that information at scale requires reading the drawings.
By Yani Guan, Dengpan Dong, Zi Wei, Shuang Luo, Dan Hannah, Yumin Zhang, Kang Xu
The study evaluates whether a portfolio of compact, semantically named descriptor blocks can match the performance of a 2048‑dimensional CheMeleon embedding in low‑data molecular assays. Using a fixed 11‑dimensional physicochemical base and greedily adding provenance‑screened blocks, the portfolio achieves a mean test AUC of 0.762 across nine ADME/Tox assays, comparable to CheMeleon’s 0.764 and better than Mordred’s 0.756. The results meet a predeclared pooled parity threshold but not all per‑assay thresholds, and further analysis confirms the competitiveness of the auditable representation while highlighting unresolved assay‑level differences.
By Yiqi Yao, Miquel Duran-Frigola
The study evaluates the reliability of shared odor descriptor words across four public corpora from Pyrfume, finding substantial disagreement (I² = 80 %) and limited agreement on descriptor application (median tetrachoric = 0.795, κ = 0.413). Only a fraction of the achievable variance in odor perception is captured by current models and descriptor sets, with valence emerging as the primary missing component. Even with extensive model capacity and merged corpora, the gap remains, indicating that valence must be measured directly to improve machine olfaction.
By Stylianos Kampakis, Fabio Rovai
arXiv:2609.00654v1 Announce Type: new
Abstract: We describe the SciTrue team's participation in both subtasks of the NTCIR-19 SciClaimEval task~\cite{sciclaimeval}, which asks systems to verify scien...
By Qiming Bao, Ne\c{s}et \"Ozkan Tan, Siyuan Wang, Mark Gahegan
arXiv:2607. 24848v1 Announce Type: cross Abstract: Pretrained molecular encoders are commonly evaluated through downstream prediction, but predictive accuracy alone does not establish that a learned representation captures reproducible scientific structure, adds information beyond strong conventional baselines, or transfers out of distribution.
By Kai Lun Huang (California State University, Fullerton), Wei Chieh Sun (University of Washington)
arXiv:2607. 27763v1 Announce Type: cross Abstract: We describe the DS@GT submissions to the ImageCLEFmedical Caption 2026 challenge, which continues a long-running benchmark on the ROCOv2 dataset with two tracks: Concept Detection (Task 1), assigning UMLS Concept Unique Identifiers (CUIs) to radiology images, and Caption Prediction (Task 2), generating natural-language captions.
By Bowen Wang, Youwen Zhang, Ritesh Mehta