{
  "abstract": "Machine learning-based quantitative structure–activity relationship (ML-QSAR) modeling requires consistent data preparation, validation, and model comparison. We present LASSBio-classFLOW, an open, modular, and semiautomated KNIME workflow for classification-based ligand screening. It integrates basic molecular structure preparation, configurable class assignment, descriptors and fingerprints calculation, dataset partitioning, hyperparameter optimization of nine algorithms, interactive performance evaluation, user-guided model selection, and external-library prediction. As an example, the workflow was evaluated using 1408 ROCK2 compounds from ChEMBL (218 active and 1190 inactive). Models were trained and optimized by fivefold cross-validation and evaluated on a held-out 20% test set. Support vector machine (SVM) and k-nearest neighbors (kNN) provided the most consistent results across both stages. On the held-out set, SVM favored active-class precision (0.861), whereas kNN achieved higher active recall (0.750). LASSBio-classFLOW (available at https://github.com/pedrosenamp/LASSBio-classFLOW_v1.0.git) therefore provides a transparent, reusable environment for benchmarking, comparative ML-QSAR development, and flexible deployment, while preserving user control over model selection and screening decisions.",
  "abstract_shown": true,
  "affiliations": [
    "Universidade Federal do Rio de Janeiro , , Av. Carlos Chagas Filho, 373─Cidade Universitária−CCS , , ,",
    "Royal College of Surgeons in Ireland , , 123 St Stephen’s Green , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Pedro de Sena Murteira Pinheiro",
      "orcid": "0000-0003-4148-4243"
    },
    {
      "affs": [
        0
      ],
      "name": "Jefferson Muniz Alves da Silva"
    },
    {
      "affs": [
        0
      ],
      "name": "Bárbara da Silva Mascarenhas de Jesus"
    },
    {
      "affs": [
        1
      ],
      "name": "Daniel Alencar Rodrigues"
    },
    {
      "affs": [
        0
      ],
      "name": "Lídia Moreira Lima",
      "orcid": "0000-0002-8625-6351"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3s7s6da203sdv.bib",
  "cited_by": 0,
  "doi": "10.1021/acsomega.6c09298",
  "fulltext_url": "https://europepmc.org/articles/PMC13689159",
  "id": "3s7s6da203sdv",
  "issns": [
    "2470-1343"
  ],
  "landing_url": "https://pubs.acs.org/acsodf/article/11/41/64107/5444587/LASSBio-classFLOW-A-Semiautomated-KNIME-Workflow",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7220450929",
  "pdf_url": "https://pubs.acs.org/doi/pdf/10.1021/acsomega.6c09298",
  "pmcid": "PMC13689159",
  "pmid": "42923581",
  "published": "2026-10-05",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/3s7s6da203sdv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:39Z",
      "native_id": "10.1021/acsomega.6c09298",
      "source": "crossref",
      "url": "https://pubs.acs.org/acsodf/article/11/41/64107/5444587/LASSBio-classFLOW-A-Semiautomated-KNIME-Workflow"
    },
    {
      "first_seen": "2026-10-30T12:20:36Z",
      "native_id": "MED:42923581",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42923581"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2470-1343",
      "subfield": "general"
    }
  ],
  "title": "LASSBio-classFLOW: A Semiautomated KNIME Workflow for Classification Model Benchmarking and Virtual Screening",
  "title_text": "LASSBio-classFLOW: A Semiautomated KNIME Workflow for Classification Model Benchmarking and Virtual Screening",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:36Z",
  "url": "https://research.draconitic.com/paper/3s7s6da203sdv",
  "venue": "ACS Omega",
  "versions": []
}