Results 171 to 180 of about 1,615,291 (236)

Quantitative Structure-Activity Relationship Analysis of Isosteviol-Related Compounds as Activated Coagulation Factor X (FXa) Inhibitors. [PDF]

Nutrients, 2022
Gackowski M, Szewczyk-Golec K, Mądra-Gackowska K, Pluskota R, Koba M.   +4 more
europepmc   +1 more source

Implementation of Drug‐Induced Rhabdomyolysis and Acute Kidney Injury in Microphysiological System

Advanced Functional Materials, EarlyView.
A modular Muscle–Kidney proximal tubule‐on‐a‐chip integrates 3D skeletal muscle and renal proximal tubule tissues to model drug‐induced rhabdomyolysis and acute kidney injury. The coculture system enables dynamic tissue interaction, functional contraction monitoring, and quantification of nephrotoxicity, revealing drug side effect‐induced metabolic ...
Jaesang Kim, Zhuo Feng Lee, Gi‐Dong Sim, Sejoong Kim, Jessie S. Jeon   +4 more
wiley   +1 more source

Comprehensive safety evaluation of <i>Withania somnifera</i> (Ashwagandha): an AI-driven meta-analysis and quantitative structure-activity relationship based toxicity assessment. [PDF]

Front Nutr
Ronen Y, Ebert C, Tamim-Yecheskel BC, Zev S, Kantor O, Arbel HB.   +5 more
europepmc   +1 more source

Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure-Activity Relationship. [PDF]

ACS Med Chem Lett, 2022
Zhang J, Yang L, Tian Z, Zhao W, Sun C, Zhu L, Huang M, Guo G, Liang G.   +8 more
europepmc   +1 more source

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley   +1 more source

Food Grade Synthesis of Hetero-Coupled Biflavones and 3D-Quantitative Structure-Activity Relationship (QSAR) Modeling of Antioxidant Activity. [PDF]

Antioxidants (Basel)
Zheng H, Yang X, Zhang Q, Toy JYH, Huang D.   +4 more
europepmc   +1 more source

Quantitative Structure-Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure. [PDF]

J Med Chem, 2022
Cai Z, Zafferani M, Akande OM, Hargrove AE.   +3 more
europepmc   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Quantitative Structure-Activity Relationship Models to Predict Cardiac Adverse Effects. [PDF]

Chem Res Toxicol
Mou Z, Volarath P, Racz R, Cross KP, Girireddy M, Chakravarti S, Stavitskaya L.   +6 more
europepmc   +1 more source

Quantitative Structure-Activity Relationship, Ontology-Based Model of the Antioxidant and Cell Protective Activity of Peat Humic Acids. [PDF]

Polymers (Basel), 2022
Zykova MV, Brazovskii KS, Bratishko KA, Buyko EE, Logvinova LA, Romanenko SV, Konstantinov AI, Krivoshchekov SV, Perminova IV, Belousov MV.   +9 more
europepmc   +1 more source