We have located links that may give you full text access.
Artificial Intelligence for Skin Permeability Prediction: Deep Learning.
Journal of Drug Targeting 2024 January 24
BACKGROUND AND OBJECTIVE: Researchers have put in significant laboratory time and effort in measuring the permeability coefficient (Kp) of xenobiotics. In order to develop alternative approaches to this labor-intensive procedure, predictive models have been employed by scientists to describe the transport of xenobiotics across the skin. Most quantitative structure-permeability relationship (QSPR) models are derived statistically from experimental data. Recently, artificial intelligence-based computational drug delivery has attracted tremendous interest. Deep learning is an umbrella term for machine-learning algorithms consisting of deep neural networks (DNNs). Distinct network architectures, like convolutional neural networks (CNNs), feedforward neural networks (FNNs), and recurrent neural networks (RNNs), can be employed for prediction.
METHODS: In this project, we used convolutional neural network, feedforward neural network and recurrent neural network to predict skin permeability coefficients from a publicly available database reported by Cheruvu et al[16]. The dataset contains 476 records of 145 chemicals, xenobiotics, and pharmaceuticals, administered on the human epidermis in vitro from aqueous solutions of constant concentration either saturated in infinite dose quantities or diluted. All the computations were conducted with Python under Anaconda and Jupyterlab environment after importing the required Python, Keras, and Tensorflow modules.
RESULTS: We used convolutional neural network, feedforward neural network and recurrent neural network to predict log kp.
CONCLUSION: This research work shows that deep learning network can be successfully used to digitally screen and predict the skin permeability of xenobiotics.
METHODS: In this project, we used convolutional neural network, feedforward neural network and recurrent neural network to predict skin permeability coefficients from a publicly available database reported by Cheruvu et al[16]. The dataset contains 476 records of 145 chemicals, xenobiotics, and pharmaceuticals, administered on the human epidermis in vitro from aqueous solutions of constant concentration either saturated in infinite dose quantities or diluted. All the computations were conducted with Python under Anaconda and Jupyterlab environment after importing the required Python, Keras, and Tensorflow modules.
RESULTS: We used convolutional neural network, feedforward neural network and recurrent neural network to predict log kp.
CONCLUSION: This research work shows that deep learning network can be successfully used to digitally screen and predict the skin permeability of xenobiotics.
Full text links
Related Resources
Trending Papers
A Guide to the Use of Vasopressors and Inotropes for Patients in Shock.Journal of Intensive Care Medicine 2024 April 14
British Society for Rheumatology guideline on management of adult and juvenile onset Sjögren disease.Rheumatology 2024 April 17
Albumin: a comprehensive review and practical guideline for clinical use.European Journal of Clinical Pharmacology 2024 April 13
Renin-Angiotensin-Aldosterone System: From History to Practice of a Secular Topic.International Journal of Molecular Sciences 2024 April 5
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app