Green processing based on supercritical carbon dioxide for preparation of nanomedicine: Model development using machine learning and experimental validation

Alshahrani, Saad; Albaghdadi, Mustafa Fahem; Yasmin, Sabina; Alosaimi, Manal E.; Alsalhi, Abdullah; Algarni, Mohammed; Felemban, Bassem F.; Fadhil, Ali Abdulhussain; Mohammed, Ibrahim Mourad

doi:10.1016/j.csite.2022.102620

Cited by 8 publications

(1 citation statement)

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“…At present, one of the most promising approaches for such a task involves the use of machine learning (ML) methods, as evidenced by the recent growth in the literature dedicated to the topic. − However, it is important to note that a majority of the constructed QSPR models are mostly used for the inter- or extrapolation of data, based on a rather small initial set. Notably, there are two noteworthy works , which utilize a larger data set of over a hundred drugs for model training, claiming that their models can successfully predict the properties of new compounds, expanding beyond the original training set.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Drug-like Compounds Solubility in Supercritical Carbon Dioxide: A Comparative Study between Classical Density Functional Theory and Machine Learning Approaches

Makarov,

Kalikin,

Budkov

2024

Ind. Eng. Chem. Res.

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Supercritical carbon dioxide (scCO 2 ) plays an essential role in various technological procedures, making the solubility of drugs in scCO 2 a crucial aspect of the drug formulation process. This study focuses on utilizing theoretical approaches to predict the solubility of drug-like compounds in scCO2 in order to select the optimum parameters for subsequent experimental procedures. Several machine learning models were developed and compared with a previously established theoretical approach based on classical density functional theory (cDFT). The CatBoost model, utilizing alvaDesc descriptors, demonstrated reasonably accurate predictions for the solubility of 187 drugs (AARD = 1.8%). Meanwhile, the CatBoost model, incorporating CDK descriptors and melting points of drugs as input parameters, exhibited satisfactory accuracy (AARD = 14.3%) in extrapolating predictions for new compounds. Comparing the results between the machine learning approach and the cDFT-based one revealed, on average, a higher accuracy and faster prediction speed for the former. However, cDFT demonstrated a more physical behavior of solubility isotherms compared with the machine learning models. This was particularly evident when the ML models struggled to accurately extrapolate solubility values beyond the experimental range of parameters in the supercritical state. Model CatBoost/CDK is freely accessible at http://chem-predictor.isc-ras.ru/ individual/scco/.

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