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Permeability

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A merged molecular representation deep learning method for blood-brain barrier permeability prediction.

Briefings in bioinformatics
The ability of a compound to permeate across the blood-brain barrier (BBB) is a significant factor for central nervous system drug development. Thus, for speeding up the drug discovery process, it is crucial to perform high-throughput screenings to p...

Perspective on a chemistry classification system for AI-assisted formulation development.

Journal of controlled release : official journal of the Controlled Release Society
This perspective article draws a distinction between some of the well-known drug classification systems and a "Chemistry Classification System" (CCS). Rather than have drug classification based on some simple properties like solubility and permeabili...

Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability.

Environmental science and pollution research international
Gas breakthrough pressure is a significant parameter for the gas exploration and safety evaluation of engineering barrier systems in the carbon dioxide storage, remediation of contaminated sites, and deep geological repository for disposal of high-le...

Machine learning based dynamic consensus model for predicting blood-brain barrier permeability.

Computers in biology and medicine
The blood-brain barrier (BBB) is an important defence mechanism that restricts disease-causing pathogens and toxins to enter the brain from the bloodstream. In recent years, many in silico methods were proposed for predicting BBB permeability, howeve...

Stacked ensemble machine learning for porosity and absolute permeability prediction of carbonate rock plugs.

Scientific reports
This study employs a stacked ensemble machine learning approach to predict carbonate rocks' porosity and absolute permeability with various pore-throat distributions and heterogeneity. Our dataset consists of 2D slices from 3D micro-CT images of four...

Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function.

NeuroImage
PURPOSE: In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning...

Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules.

Journal of chemical information and modeling
Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to ac...

Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides.

Journal of medicinal chemistry
Cyclic peptides are gaining attention for their strong binding affinity, low toxicity, and ability to target "undruggable" proteins; however, their therapeutic potential against intracellular targets is constrained by their limited membrane permeabil...

Artificial intelligence for skin permeability prediction: deep learning.

Journal of drug targeting
BACKGROUND AND OBJECTIVE: Researchers have put in significant laboratory time and effort in measuring the permeability coefficient (Kp) of xenobiotics. To develop alternative approaches to this labour-intensive procedure, predictive models have been ...

Machine Learning Exploration of the Relationship Between Drugs and the Blood-Brain Barrier: Guiding Molecular Modification.

Pharmaceutical research
OBJECTIVE: This study aimed to improve the efficiency of pharmacotherapy for CNS diseases by optimizing the ability of drug molecules to penetrate the Blood-Brain Barrier (BBB).