A QSPR scheme for the computation of lipophilicities of ⁶⁴Cu complexes was developed with a training set of 24 tetraazamacrocylic and bispidine-based Cu(II) compounds and their experimentally available 1-octanol-water distribution coefficients. A minimum number of physically meaningful parameters were used in the scheme, and these are primarily based on data available from molecular mechanics calculations, using an established force field for Cu(II) complexes and a recently developed scheme for the calculation of fluctuating atomic charges. The developed model was also applied to an independent validation set and was found to accurately predict distribution coefficients of potential ⁶⁴Cu PET (positron emission tomography) systems. A possible next step would be the development of a QSAR-based biodistribution model to track the uptake of imaging agents in different organs and tissues of the body. It is expected that such simple, empirical models of lipophilicity and biodistribution will be very useful in the design and virtual screening of positron emission tomography (PET) imaging agents.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1039/c3dt51049b | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Identification of Novel Potential Herbal Drug Targets against Beta-Catenin in the Treatment of Oral Squamous Cell Carcinoma.
Asian Pac J Cancer Prev
December 2024
Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
Objective: The study aims to identify potential pharmacophore models for targeting beta-catenin, a crucial protein involved in the development of oral squamous cell carcinoma (OSCC), using a combination of herbal compounds and computational approaches.
Methods: Five natural compounds namely Quercetin, Lycopene, Ovatodiolide, Karsil, and Delphinidin were selected based on their reported activity against beta-catenin. Ligand characteristics were analyzed using SwissADME to evaluate drug-likeness, lipophilicity (logP), and bioavailability.
Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow.
J Chem Inf Model
December 2024
Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.
Machine learning (ML) methods provide a pathway to accurately predict molecular properties, leveraging patterns derived from structure-property relationships within materials databases. This approach holds significant importance in drug discovery and materials design, where the rapid, efficient screening of molecules can accelerate the development of new pharmaceuticals and chemical materials for highly specialized target application. Unsupervised and self-supervised learning methods applied to graph-based or geometric models have garnered considerable traction.
View Article and Find Full Text PDFRecent Developments in Drug Design of Oral Synthetic Free Fatty Acid Receptor 1 Agonists.
Drug Des Devel Ther
December 2024
Tasly Academy, Tasly Pharma Co., Ltd., Tianjin, People's Republic of China.
Over the past two decades, synthetic FFAR1 agonists such as TAK-875 and TSL1806 have undergone meticulous design and extensive clinical trials. However, due to issues primarily related to hepatotoxicity, no FFAR1 agonist has yet received regulatory approval. Research into the sources of hepatotoxicity suggests that one potential cause lies in the molecular structure itself.
View Article and Find Full Text PDFCopper-Mediated -CF(OCF)(CFH) Transfer to Organic Electrophiles.
ACS Org Inorg Au
December 2024
Department of Chemistry and Biomolecular Sciences and Centre for Catalysis Research and Innovation, University of Ottawa, 30 Marie Curie, Ottawa, Ontario K1N 6N5, Canada.
The integration of fluorine into medicinal compounds has become a widely used strategy to improve the biochemical and therapeutic properties of drugs. Inclusion of -CFH and -OCF fluoroalkyl groups has garnered attention due to their bioisosteric properties, enhanced lipophilicity, and potential hydrogen-bonding capability in bioactive substances. In this study, we prepared a series of stable Cu[CF(OCF)(CFH)]L complexes by insertion of commercially available perfluoro(methyl vinyl ether), CF=CF(OCF), into Cu-H bonds derived from Stryker's reagent, [CuH(PPh)], using ancillary ligands L.
View Article and Find Full Text PDFPreventing mislabeling of organic white button mushrooms (Agaricus bisporus) combining NMR-based foodomics, statistical, and machine learning approach.
Food Res Int
December 2024
University of Belgrade - Institute of Chemistry, Technology and Metallurgy, Department of Chemistry, Njegoševa 12, 11000 Belgrade, Serbia. Electronic address:
Organic foods are among the most susceptible to fraud and mislabeling since the differentiation between organic and conventionally grown food relies on a paper-trail-based system. This study aimed to develop a differentiation model that combines nuclear magnetic resonance (NMR), statistical approach (principal component analysis - PCA and partial least square discriminant analysis - PLS-DA), and classification artificial neural network (cANN). The model was tested for hydrophilic and lipophilic extracts of Agaricus bisporus.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!