The study evaluates how well large language models (LLMs) can annotate missing metadata in PubChem’s ~2 million bioassays, focusing on BioAssay Ontology (BAO) assay format and detection method fields. It finds that 36 % of assays lack an assay format, 89 % lack a BioAssay type, and over 99.9 % lack any BAO‑mapped format or detection technology term, highlighting a critical sparsity in metadata. Seven open‑source and proprietary LLMs achieve recall ≥0.96 for biochemical and cell‑based assay formats, with similar performance for detection technology, though disagreements rise for under‑represented classes and often stem from inconsistencies in silver labels rather than LLM errors. A qualitative test with an industrial curator shows LLM‑generated evidence can prompt revisions of existing labels, indicating that LLMs can flag potentially mislabeled assays.
By Laura van Weesep, Riccardo Tedoldi, Jens Sj\"olund, Hossein Azizpour, Susanne Winiwarter, Ola Engkvist, Jon Paul Janet, Samuel Genheden, Juan Viguera Diez
arXiv:2610.32490v1 Announce Type: new
Abstract: Public drug-protein bioactivity resources commonly identify targets by UniProt accession, although one accession may represent distinct experimental se...
By Lina Abou-Abbas, Nour Mhanna, Hasan Bazzi, Sleiman El Hajj, Abdulkader Helwan, Rabah Omrani, Khadidja Henni
arXiv:2408. 13378v5 Announce Type: replace Abstract: Workflows in drug-target interaction (DTI) assessment require integrating heterogeneous data from predictive models, curated resources, and observations from experimental literature.
By Yoshitaka Inoue, Tianci Song, Xinling Wang, Rui Kuang, Tianfan Fu, Augustin Luna
arXiv:2608.28974v1 Announce Type: new
Abstract: Clinically relevant oncology information is distributed across heterogeneous, longitudinal documentation, creating substantial abstraction burden and r...
By Daniel Kang, Michelle Hu, Soorya Ram Shimgekar, Shayan Vassef, Yufan Wang, Anit Kumar Sahu, Munmun De Choudhury, Vedant Das Swain, Christian Poellabauer, Li Yan Khor, Koustuv Saha, Robert Wojciechowski, Elliot Kidd, Piyum Zonooz, Navin Kumar
arXiv:2608.03154v2 Announce Type: replace
Abstract: Biomedical relation extraction (BioRE) supports knowledge graph construction and hypothesis generation. In real-world settings, most candidate enti...
By Shufan Ming, Yikun Han, Gibong Hong, Rui Zhang, Halil Kilicoglu
arXiv:2608. 14765v1 Announce Type: new Abstract: Data cleaning without a trusted clean reference is challenging because unusual values may represent either genuine errors or valid observations.
By Hadi Fadlallah
arXiv:2610.24806v1 Announce Type: cross
Abstract: Despite breakthroughs in AI-driven molecular generation, existing discovery pipelines operate largely in silico, with wet-lab feedback reserved for v...
By Zachary Cutts, John Coller, Rachel Chen, Hua Tang, Mathias Bellaiche, Ryder Easterlin, Dina Zverinski, Petar Sirkovic, Avinatan Hassidim, Yossi Matias, Pooja Rao, Annalisa Pawlosky
The paper introduces an LLM-as-a-Judge framework for evaluating the outputs of an agentic drug discovery assistant, ChatInvent, deployed at AstraZeneca. It defines four quality dimensions—Completeness, Relevancy, Structural Clarity, and Scope Adherence—alongside deterministic Tool Call Correctness checks, and validates the judge against five expert annotators. After optimizing the best-performing judge with few-shot demonstrations, alignment with human majority votes improves from 0.80 to 0.86, and the framework reveals that informal question phrasing does not degrade output quality.
By Emma Granqvist, Roc\'io Mercado, Samuel Genheden
arXiv:2605. 06177v2 Announce Type: replace Abstract: Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies across papers because the harness and tool registry differ, and integrating a new model into a comparable evaluation surface costs weeks of model-specific engineering.
By Jinge Wu, Hongjian Zhou, Mingde Zeng, Jiayuan Zhu, Junde Wu, Jiazhen Pan, Ayush Noori, Sean Wu, Honghan Wu, Fenglin Liu, David A. Clifton
arXiv:2606. 19245v1 Announce Type: new Abstract: Artificial intelligence (AI) agents promise to accelerate drug discovery by compressing interpretation and decision-making loops, but practical deployment requires trusted evaluation on realistic program decisions.
By Hannah Le, Ramesh Ramasamy, Alex Urrutia, Mahsa Yazdani, Tim Proctor, Kenny Workman
The article proposes a framework called quantitative evidence mining to transform biomedical findings into structured, context-rich evidence units. It outlines core elements such as claim, measured entity, value, comparator, population, conditions, temporal context, uncertainty, provenance, validation, and expert review. The authors present an eight-stage reference architecture and emphasize that plausibility should remain multidimensional rather than collapsed into a single truth label, linking extraction to evidence synthesis for applications like clinical trials, biomarker research, and knowledge-graph construction.
By Negin Sadat Babaiha, Stefan Geissler, Marie-Christine Simon, Martin Hofmann-Apitius, Marc Jacobs
arXiv:2606. 16540v1 Announce Type: cross Abstract: Biomolecular sequence models are increasingly reused outside the studies in which they were introduced, but public checkpoints rarely preserve the execution context needed to inspect source-defined behavior, adapt models to new assays, compare models under shared task definitions or deploy biological predictions.
By Zhiyuan Chen