{
  "abstract": "Artificial intelligence methods now span the drug-discovery pipeline --structure prediction, de novo design, docking, affinity estimation, and ADMET -- yet each tool ships with its own, often incompatible, GPU software stack, so chaining several into a workflow requires reproducing conflicting software environments and access to datacenter-class hardware that most labs do not have. bioq is a dependency-light command-line client backed by bioq-services (accontrol-plane gateway plus a growing fleet of 38+ containerized drug-discovery tools spanning 7 discovery stages and 6 molecular modalities). The bioq CLI is self-describing and consistent across the fleet of computational tools, giving researchers and coding agents uniform access to any tool from a laptop, with no local model code, CUDA setup, or cloud configuration, running on serverless GPUs billed per job. The interface-gateway-services architecture makes bioq an execution substrate for automated, agent-driven discovery. bioq and bioq-services are open source under the MIT License and available at https://github.com/wolfsonliu/bioq and https://github.com/wolfsonliu/bioq-services. bioq runs on Python > 3.10 with httpx as its only runtime dependency; services run as Linux containers and are self-hostable via the provided local deployment scripts (alongside scripts for Alibaba Cloud Function Compute). Install, quickstart, test data, and self-hosting instructions are in the repository. The project is actively maintained and will continue to receive updates to both features and services.",
  "abstract_shown": true,
  "affiliations": [
    "Noah AI"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Z. Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Y. Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dxzkmm3xqpzx5.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.03.749148",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.03.749148v1.full",
  "id": "dxzkmm3xqpzx5",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.03.749148v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7212085708",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.03.749148v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dxzkmm3xqpzx5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:28Z",
      "native_id": "10.64898/2026.09.03.749148",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.03.749148v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "bioq: a unified, agent-native command-line interface to a fleet of AI drug-discovery methods",
  "title_text": "bioq: a unified, agent-native command-line interface to a fleet of AI drug-discovery methods",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:28Z",
  "url": "https://research.draconitic.com/paper/dxzkmm3xqpzx5",
  "venue": "bioRxiv",
  "versions": []
}