Cell Membrane Permeability

Antibacterial activity and its mechanisms of a recombinant Funme peptide against Cronobacter sakazakii in powdered infant formula.

Food research international (Ottawa, Ont.) 30716944

Cronobacter sakazakii (Cs) is a typical foodborne bacterium that infect powdered infant formula (PIF) worldwide. In this study, a recombinant antimicrobial peptide, branded as Funme peptide (FP)was applied to protect PIF from Cs contamination. The re...

Amino Acid Sequence Ampicillin Anti-Bacterial Agents Antimicrobial Cationic Peptides Bacteria Biofilms Biological Control Agents Cell Membrane Permeability Cronobacter sakazakii Erythrocytes Food Contamination Food Handling Food Microbiology Humans Infant Infant Formula Microbial Sensitivity Tests Powders

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Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning.

Journal of chemical information and modeling 30540459

Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the st...

Cell Membrane Permeability Drug Evaluation, Preclinical Machine Learning Models, Biological Thermodynamics

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A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides.

Current computer-aided drug design 30251610

INTRODUCTION: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates.

Amino Acid Sequence Cell Membrane Permeability Cell-Penetrating Peptides Computational Biology Computer Simulation Humans Machine Learning Structure-Activity Relationship

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Development of Simplified in Vitro P-Glycoprotein Substrate Assay and in Silico Prediction Models To Evaluate Transport Potential of P-Glycoprotein.

Molecular pharmaceutics 30933526

For efficient drug discovery and screening, it is necessary to simplify P-glycoprotein (P-gp) substrate assays and to provide in silico models that predict the transport potential of P-gp. In this study, we developed a simplified in vitro screening m...

Animals ATP Binding Cassette Transporter, Subfamily B ATP Binding Cassette Transporter, Subfamily B, Member 1 Biological Availability Cell Membrane Permeability Central Nervous System Agents Computer Simulation Data Accuracy Drug Discovery Drug Evaluation, Preclinical Intestinal Absorption LLC-PK1 Cells Machine Learning Protein Transport Reproducibility of Results Swine Transfection

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Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning.

Biochemical pharmacology 34461115

For medicines, the apparent membrane permeability coefficients (P) across human colorectal carcinoma cell line (Caco-2) monolayers under a pH gradient generally correlate with the fraction absorbed after oral intake. Furthermore, the in vitro P value...

Caco-2 Cells Cell Membrane Permeability Computer Simulation Forecasting Humans Inorganic Chemicals Intestinal Absorption Linear Models Machine Learning

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

Journal of chemical information and modeling 37682640

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...

Artificial Intelligence Cell Membrane Permeability Chemistry, Pharmaceutical Ligands Permeability

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

Journal of medicinal chemistry 38270541

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...

Cell Membrane Permeability Chemistry, Pharmaceutical Deep Learning Peptides, Cyclic Permeability

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MuCoCP: a priori chemical knowledge-based multimodal contrastive learning pre-trained neural network for the prediction of cyclic peptide membrane penetration ability.

Bioinformatics (Oxford, England) 39067027

MOTIVATION: There has been a burgeoning interest in cyclic peptide therapeutics due to their various outstanding advantages and strong potential for drug formation. However, it is undoubtedly costly and inefficient to use traditional wet lab methods ...

Cell Membrane Permeability Machine Learning Neural Networks, Computer Peptides, Cyclic

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Modelling of intrinsic membrane permeability of drug molecules by explainable ML-based q-RASPR approach towards better pharmacokinetics and toxicokinetics properties.

SAR and QSAR in environmental research 40190164

Drug discovery's success lies in potent inhibition against a target and optimum pharmacokinetic and toxicokinetic properties of drug molecules. Membrane permeability is a crucial factor in determining the absorption, distribution, metabolism, and exc...

Algorithms Cell Membrane Permeability Machine Learning Permeability Pharmaceutical Preparations Pharmacokinetics Quantitative Structure-Activity Relationship Toxicokinetics

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Seeking Correlation Among Porin Permeabilities and Minimum Inhibitory Concentrations Through Machine Learning: A Promising Route to the Essential Molecular Descriptors.

Molecules (Basel, Switzerland) 40142001

Developing effective antibiotics against Gram-negative bacteria remains challenging due to their protective outer membrane. With this study, we investigated the relationship between antibiotic permeation through the OmpF porin of and antimicrobial e...

Anti-Bacterial Agents Cell Membrane Permeability Escherichia coli Machine Learning Microbial Sensitivity Tests Permeability Porins

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Antibacterial activity and its mechanisms of a recombinant Funme peptide against Cronobacter sakazakii in powdered infant formula.

Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning.

A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides.

Development of Simplified in Vitro P-Glycoprotein Substrate Assay and in Silico Prediction Models To Evaluate Transport Potential of P-Glycoprotein.

Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning.

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

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

MuCoCP: a priori chemical knowledge-based multimodal contrastive learning pre-trained neural network for the prediction of cyclic peptide membrane penetration ability.

Modelling of intrinsic membrane permeability of drug molecules by explainable ML-based q-RASPR approach towards better pharmacokinetics and toxicokinetics properties.

Seeking Correlation Among Porin Permeabilities and Minimum Inhibitory Concentrations Through Machine Learning: A Promising Route to the Essential Molecular Descriptors.

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