The regression QSAR models were built to predict the antimicrobial activity of new thiazole derivatives. Compounds with high predicting activity were synthesized and evaluated against Gram-positive and Gram-negative bacteria and fungi. 1,3-Thiazole-4-ylphosphonium salts 4 and 5 displayed good antibacterial properties and high antifungal activity. The predictions are in a good agreement with the experiment results, which indicate the good predictive power of the created QSAR models.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.15407/ubj88.04.057 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Advancements in drug discovery: integrating CADD tools and drug repurposing for PD-1/PD-L1 axis inhibition.
RSC Adv
January 2025
LAQV and REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa Caparica Portugal
Despite significant strides in improving cancer survival rates, the global cancer burden remains substantial, with an anticipated rise in new cases. Immune checkpoints, key regulators of immune responses, play a crucial role in cancer evasion mechanisms. The discovery of immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 has revolutionized cancer treatment, with monoclonal antibodies (mAbs) becoming widely prescribed.
View Article and Find Full Text PDFA Simple Machine Learning-Based Quantitative Structure-Activity Relationship Model for Predicting pIC Inhibition Values of FLT3 Tyrosine Kinase.
Pharmaceuticals (Basel)
January 2025
Centro de Química Médica, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Santiago 7780272, Chile.
Acute myeloid leukemia (AML) presents significant therapeutic challenges, particularly in cases driven by mutations in the FLT3 tyrosine kinase. This study aimed to develop a robust and user-friendly machine learning-based quantitative structure-activity relationship (QSAR) model to predict the inhibitory potency (pIC values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting.
View Article and Find Full Text PDFMachine Learning-Assisted Drug Repurposing Framework for Discovery of Aurora Kinase B Inhibitors.
Pharmaceuticals (Basel)
December 2024
Faculty of Pharmacy, "Carol Davila" University of Medicine and Pharmacy, Traian Vuia 6, 020956 Bucharest, Romania.
Aurora kinase B (AurB) is a pivotal regulator of mitosis, making it a compelling target for cancer therapy. Despite significant advances in protein kinase inhibitor development, there are currently no AurB inhibitors readily available for therapeutic use. This study introduces a machine learning-assisted drug repurposing framework integrating quantitative structure-activity relationship (QSAR) modeling, molecular fingerprints-based classification, molecular docking, and molecular dynamics (MD) simulations.
View Article and Find Full Text PDFQuantitative Structure-Activity Relationship (QSAR) Modeling of Chiral CCR2 Antagonists with a Multidimensional Space of Novel Chirality Descriptors.
Molecules
January 2025
Independent Researcher, 1802 Stanford Avenue, Duluth, MN 55811, USA.
The development of chirality descriptors for quantitative chirality structure-activity relationship (QCSAR) modeling has always attracted attention, owing to the importance of chiral molecules in pharmaceutical, agriculture, food, and fragrance industries, and environmental toxicology. The utility of a multidimensional space of novel relative chirality indices (RCIs) in the QCSAR modeling of twenty CCR2 antagonists is reported upon in this paper. The numerical characterization of chirality by the RCI approach gives a large pool of chirality descriptors with different degrees of mutual correlation (the correlation coefficient among the computed descriptors varied from 0.
View Article and Find Full Text PDFSystematic Study of Steroid Drugs' Ability to Cross Biomembranes-The Possible Environmental Impact and Health Risks Associated with Exposure During Pregnancy.
Membranes (Basel)
December 2024
Faculty of Chemistry, University of Lodz, 91-403 Lodz, Poland.
Thirty-seven steroid drugs of different types were investigated in silico for their environmental and pharmacokinetic properties (partition between soil and water, bioaccumulation in aquatic organisms, ability to be absorbed from the gastrointestinal tract and to cross biological barriers-skin, blood-brain barrier and placenta) using on-line tools and novel QSAR models. The same drugs were studied by Molecular Docking in the context of their ability to interact with two enzymes-glutathione S-transferase (GST) and human N-acetyltransferase 2 (NAT2), which are involved in the placenta's protective system against harmful xenobiotics. Steroid drugs are released to the environment from households, hospitals, manufacturing plants and farms (e.
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!