AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Hydrophobic and Hydrophilic Interactions

Showing 41 to 50 of 68 articles

Clear Filters

Micelle-dominated distribution strategy for non-matrix matched calibration without an internal standard: "Extract-and-shoot" approach for analyzing hydrophilic targets in blood and cell samples.

Analytica chimica acta
The analysis of trace hydrophilic targets in complex aqueous-rich matrices is considerably challenging, generally requiring matrix-matched calibration, internal standard, or time-and-labor-intensive sample preparation. To address this analytical bott...

Absolute quantitation of high abundant Fc-glycopeptides from human serum IgG-1.

Analytica chimica acta
Absolute quantitation of IgG-1 Fc-glycosylation, which is crucial for the clinical practice of glyco-biomarkers and quality control of biopharmaceuticals, has been hindered by the lack of glycopeptide standards. In this study, eleven high abundant Ig...

Hypoxia-responsive micelles self-assembled from amphiphilic block copolymers for the controlled release of anticancer drugs.

Journal of materials chemistry. B
Amphiphilic block copolymers poly(ethylene glycol)-block-poly(methacrylic acid-co-2-nitroimidazole methacrylate) (PEG-b-P(MAA-co-NIMA)) were synthesized by the combination of atom transfer radical polymerization (ATRP), hydrolysis and EDC reactions. ...

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions.

Protein and peptide letters
The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to det...

Shape-Based Generative Modeling for de Novo Drug Design.

Journal of chemical information and modeling
In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image ana...

Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

Journal of molecular graphics & modelling
BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for reproductive health. The role of FSHR in tumor progression as well as osteoporosis advancement has al...

In silico minimalist approach to study 2D HP protein folding into an inhomogeneous space mimicking osmolyte effect: First trial in the search of foldameric backbones.

Bio Systems
We have employed our bioinformatics workbench, named Evolution, a Multi-Agent System based architecture with lattice-bead-models, evolutionary-algorithms, and correlated-networks as inhomogeneous spaces, with different correlation lengths, mimicking ...

Prediction of Hemolytic Toxicity for Saponins by Machine-Learning Methods.

Chemical research in toxicology
Saponins are a type of compounds bearing a hydrophobic steroid/triterpenoid moiety and hydrophilic carbohydrate branches. The majority of the saponins demonstrate a broad range of prominent pharmacological activities. Nevertheless, many saponins also...

Discrimination power of knowledge-based potential dictated by the dominant energies in native protein structures.

Amino acids
Extracting a well-designed energy function is important for protein structure evaluation. Knowledge-based potential functions are one type of the energy functions which can be obtained from known protein structures. The pairwise potential between ato...

The Classifying Autoencoder: Gaining Insight into Amyloid Assembly of Peptides and Proteins.

The journal of physical chemistry. B
Despite the importance of amyloid formation in disease pathology, the understanding of the primary structure?activity relationship for amyloid-forming peptides remains elusive. Here we use a new neural-network based method of analysis: the classifyin...