# Prediction of Drug Penetration Coefficients for Transdermal Drug Delivery using Artificial Neural Networks

### Yilun Han

#### 2022

#### Abstract

The penetration of drug molecules into the skin is a crucial stage in the transdermal drug delivery process. Traditional direct measuring techniques have a number of flaws. The creation of a transdermal penetration model that predicts a drug's penetration coefficient might be a viable answer to these issues. Combined with the analysis of the quantitative structure-activity relationship, a new statistical method, artificial neural network, is introduced. Establish a BP neural network, take the molecular weight of the drug molecule, the noctanol/water partition coefficient, the number of hydrogen bond donors and acceptors as the input values of the artificial neural network, and the drug transdermal permeability coefficient as the output of the neural network value. Train and optimize the built network model and predict the transdermal permeability coefficients of 10 drugs. The correlation coefficient between the predicted value and the measured value is R2=0.9953, and there is no significant difference within the 99% confidence interval. It shows that the model has a high prediction accuracy and a wide prediction range, which can provide reliable data reference help for the actual drug design stage.

Download#### Paper Citation

#### in Harvard Style

Han Y. (2022). **Prediction of Drug Penetration Coefficients for Transdermal Drug Delivery using Artificial Neural Networks**. In *Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics - Volume 1: ICBEB,* ISBN 978-989-758-595-1, pages 999-1006. DOI: 10.5220/0011375400003443

#### in Bibtex Style

@conference{icbeb22,

author={Yilun Han},

title={Prediction of Drug Penetration Coefficients for Transdermal Drug Delivery using Artificial Neural Networks},

booktitle={Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics - Volume 1: ICBEB,},

year={2022},

pages={999-1006},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0011375400003443},

isbn={978-989-758-595-1},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics - Volume 1: ICBEB,

TI - Prediction of Drug Penetration Coefficients for Transdermal Drug Delivery using Artificial Neural Networks

SN - 978-989-758-595-1

AU - Han Y.

PY - 2022

SP - 999

EP - 1006

DO - 10.5220/0011375400003443