Classification of Potent and Weak Penetration Enhancers Using Multiple Feature Selection Methods and Machine Learning Models

نویسندگان

چکیده

Chemical penetration enhancers (CPEs) are important in transdermal drug delivery (TDDD) formulations because they assist drugs moving across the stratum corneum. Hydrocortisone (0.1% hydrocortisone, propylene glycol), oestradiol (0.045 mg estradiol/0.015 levonorgestrel, and testosterone (2% testosterone, glycol) some examples of marketing TDDD formulations. As route for administration becomes a safer more appealing alternative to hypodermic needles, search new CPEs their development important. Thus, current work was directed toward rapid identification potent through robust machine learning (ML) classification models. Two large enhancer (PE) data sets reported date such as hydrocortisone (139 PEs) theophylline (101 were used build In present investigation, combination feature selection methods, i.e., Boruta Recursive Feature Elimination (RFE), algorithms support vector (SVM), random forest (RF), artificial neural network (ANN) employed classify weak theophylline. The tenfold cross-validation Y-randomization methods evaluate prediction performance developed Significant models built both when RFE method RF algorithm used. classifiers outperformed with test set accuracy Matthew’s correlation coefficient (MCC) greater than 0.78. Simultaneously, four features required accurate PEs identified, nHCsatu, minHCsatu, AATS4p, GATS4e. Our approach produced ML that can be applied prioritize from databases. Utilization these virtual screening experiments could save time effort potential PEs.

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ژورنال

عنوان ژورنال: Journal of Pharmaceutical Innovation

سال: 2023

ISSN: ['1872-5120', '1939-8042']

DOI: https://doi.org/10.1007/s12247-023-09757-y