AI Medical Compendium Journal:
Molecular pharmaceutics

Showing 11 to 20 of 52 articles

POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles.

Molecular pharmaceutics
Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polyme...

Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions.

Molecular pharmaceutics
This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated no...

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence.

Molecular pharmaceutics
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth,...

Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.

Molecular pharmaceutics
Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties. Such quantitative structure-property relationship models are generally trained on large data sets ...

Descriptor-Free Deep Learning QSAR Model for the Fraction Unbound in Human Plasma.

Molecular pharmaceutics
Chemical-specific parameters are either measured in vitro or estimated using quantitative structure-activity relationship (QSAR) models. The existing body of QSAR work relies on extracting a set of descriptors or fingerprints, subset selection, and t...

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence.

Molecular pharmaceutics
Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carr...

Artificial Intelligence-Based Quantitative Structure-Property Relationship Model for Predicting Human Intestinal Absorption of Compounds with Serotonergic Activity.

Molecular pharmaceutics
Oral medicines represent the largest pharmaceutical market area. To achieve a therapeutic effect, a drug must penetrate the intestinal walls, the main absorption site for orally delivered active pharmaceutical ingredients (APIs). Indeed, predicting d...

Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties.

Molecular pharmaceutics
Machine learning (ML) has become an indispensable tool to predict absorption, distribution, metabolism, and excretion (ADME) properties in pharmaceutical research. ML algorithms are trained on molecular structures and corresponding ADME assay data to...

Machine Learning Models Identify New Inhibitors for Human OATP1B1.

Molecular pharmaceutics
The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors o...