{
  "abstract": "Reliable prediction of pharmaceutical solubility in supercritical carbon dioxide (scCO2) is complicated by strong compound-specific behavior and response values. This study investigated whether sparse measurements from a newly introduced drug can calibrate transferable machine-learning predictions for computational estimation of solubility in wide range of parameters. A curated workbook containing 1,618 observations for 60 drugs was modeled using molecular weight, melting point, temperature, and pressure. Solubility was transformed as log₁₀(S), and XGBoost, CatBoost, and multilayer perceptron regression were each tuned using tree-structured Parzen estimation, differential evolution, and particle swarm optimization. Evaluation used five-fold nested group cross-validation, with complete drugs held out from training and tuning. For each test drug, predictions were assessed before calibration and after 1, 3, or 5 randomly selected measurements, repeated ten times. Within the present evaluation framework, the lowest-log-MAE zero-shot configuration was XGBoost optimized by tree-structured Parzen estimation (log-MAE = 0.584; R² = −0.087; RMSE = 1.713 g/L), showing that the four descriptors were insufficient for reliable extrapolation to unseen compounds. Sparse calibration substantially improved transfer: the lowest-log-MAE one-shot result was obtained by CatBoost–particle swarm optimization (log-MAE = 0.418; R² = 0.166), whereas XGBoost–tree-structured Parzen estimation had the lowest log-MAE at three shots (log-MAE = 0.298; R² = 0.317) and at five shots (log-MAE = 0.251; R² = 0.452; RMSE = 1.223 g/L). Median absolute relative deviation decreased from 82.4% to 42.9% between zero and five shots. These results establish sparse, leakage-controlled calibration as a more defensible use case than unconditional prediction for unseen drugs, while identifying the need for richer molecular descriptors and external prospective validation. In practical terms, this few-shot strategy can reduce the experimental burden for a newly introduced drug by using a small screening set to calibrate a global model before mapping the remaining pressure-temperature domain.",
  "abstract_shown": true,
  "affiliations": [
    "Center of Research Excellence in Renewable Energy and Power Systems/Energy Efficiency Group, Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University , ,",
    "Mechanical Engineering Department, College of Engineering and Architecture, Umm Al-Qura University , ,",
    "Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Nidal H. Abu-Hamdeh"
    },
    {
      "affs": [
        1
      ],
      "name": "Hasan H. Hijji"
    },
    {
      "affs": [
        2
      ],
      "name": "Khalid H. Almitani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9k5m0d57t58tr.bib",
  "cited_by": 0,
  "doi": "10.3389/fmed.2026.1985801",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719417/?tool=EBI",
  "id": "9k5m0d57t58tr",
  "issns": [
    "2296-858X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13719417",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2204|Phase Equilibria and Thermodynamics"
    ]
  ],
  "openalex": "W7222725950",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719417/pdf/?tool=EBI",
  "pmcid": "PMC13719417",
  "pmid": "42964093",
  "published": "2026-10-16",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/9k5m0d57t58tr.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:36Z",
      "native_id": "PMC:PMC13719417",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13719417"
    },
    {
      "first_seen": "2026-11-02T12:23:23Z",
      "native_id": "PMC13719417",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fmed.2026.1985801"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "Computational modeling and optimized machine learning for multidrug solubility prediction in supercritical carbon dioxide",
  "title_text": "Computational modeling and optimized machine learning for multidrug solubility prediction in supercritical carbon dioxide",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:20:29Z",
  "url": "https://research.draconitic.com/paper/9k5m0d57t58tr",
  "venue": "Frontiers in medicine",
  "versions": []
}