VOC·BIO Library: An Integrated Platform for Published Aphid GC-MS Evidence, Biological Contexts, Cheminformatics and Exploratory OBP Docking

Published aphid volatile records are difficult to explore when chemical annotations, identification confidence and biological context are stored separately. We developed VOC·BIO Library to integrate these evidence layers with reproducible computational tools. Version 1.0 preserves 81 published GC-MS Bio features across 15 biological and sample contexts, with 352 reported feature-context presence records. The features comprise 11 authentic-standard-supported annotations, 59 tentative library identifications and 11 unknowns. Seventy named candidate interpretations are linked to 55 curated molecular structures. Eleven interface features provide an overview dashboard, a published-feature catalogue, chemical structures and descriptors, biological contexts, descriptive statistics, evidence tracing, docking predictions, a feature-context network, structural similarity, source references and deterministic guided questions. The 81 × 15 presence matrix supports context prevalence, Jaccard similarity and average-linkage clustering. Twenty-eight features are listed in only one context. The full evidence network contains 190 nodes and 452 documented links. These are descriptive publication-based relationships, not biological replicate measurements or ecological effects. Structure-enabled comparisons use Morgan fingerprints and Tanimoto similarity without upgrading the published identification confidence. An additional exploratory study screens 20 candidates against MvicOBP3 and NribOBP3 in 200 docking runs with 2000 retained poses. Independent redocking controls distinguish pose recovery from score ranking. The platform therefore connects source evidence, biological context and complementary analyses within a traceable research environment. Docking, structural similarity and documented occurrence do not establish compound origin, binding affinity, behaviour or pest-control efficacy.