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    <responseDate>2026-10-12T19:06:53Z</responseDate>
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    <identifier>10.57760/sciencedb.08373</identifier>
    <datestamp>2023-05-22T10:59:46Z</datestamp>
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  <dc:date>2023-05-22</dc:date>
  <dc:title>A Dataset of Solubility of Various Anticancer Drugs in Supercritical Carbon Dioxide</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.08373</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>The solubility of anticancer drugs (ACDs) in supercritical carbon dioxide (SC-CO2) has a pivotal role in the extraction and purification processes of drugs by this powerful and environmentally-friendly methodology. In this study, the ability of gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) techniques comprising experimental models were investigated to estimate the solubility of ACDs in SC-CO2. An extensive database comprising 744 datasets in the wide range of operational conditions (308-348.2 K and 80-400 bar), ACD's molecular weight (MWACDs), and melting point (MPACDs)were established after a comprehensive literature search. The findings of this article are consistent with previous research and show the high accuracy of artificial intelligence techniques in predicting the solubility of ACDs in SC-CO2. </dc:description>
  <dc:subject>Anticancer drugs; Supercritical CO2; Solubility; Artificial Intelligence Modeling</dc:subject>
  <dc:creator>Zahra Bahrami</dc:creator>
  <dc:creator>Fatemeh Bashipour</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/publicdomain/zero/1.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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