首页|Findings on Machine Learning Detailed by Investigators at Russian Academy of Sciences (Prediction of Drug-like Compounds Solubility In Supercritical Carbon Dioxide: a Comparative Study Between Classical Density Functional Theory and Machine ...)
Findings on Machine Learning Detailed by Investigators at Russian Academy of Sciences (Prediction of Drug-like Compounds Solubility In Supercritical Carbon Dioxide: a Comparative Study Between Classical Density Functional Theory and Machine ...)
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NETL
NSTL
Amer Chemical Soc
Current study results on Machine Learning have been published. According to news reporting out of Ivanovo, Russia, by NewsRx editors, research stated, “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.” Financial supporters for this research include Russian Science Foundation (RSF), Russian Science Foundation (RSF). Our news journalists obtained a quote from the research from the Russian Academy of Sciences, “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.”
IvanovoRussiaAnionsCarbon DioxideChemicalsCyborgsEmerging TechnologiesInorganic Carbon CompoundsMachine LearningOxidesRussian Academy of Sciences
NETL
NSTL
Amer Chemical Soc
