AIMC Topic: Structure-Activity Relationship

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Cubosomes as Delivery System to Repositioning Nitrofurantoin in Breast Cancer Management.

Drug design, development and therapy Dec 19, 2024
PURPOSE: Nitrofurantoin (NITRO), a long-standing antibiotic to treat urinary tract infections, is activated by Nitro reductases. This activation mechanism has led to its exploration for repositioning applications in controlling and treating breast ca...
Cell Survival Structure-Activity Relationship Particle Size Drug Delivery Systems Nitrofurantoin Drug Repositioning Humans Cell Proliferation Drug Screening Assays, Antitumor Female MCF-7 Cells Dose-Response Relationship, Drug Breast Neoplasms
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Classification models and SAR analysis of anaplastic lymphoma kinase (ALK) inhibitors using machine learning algorithms with two data division methods.

Molecular diversity Nov 12, 2024
Anaplastic lymphoma kinase (ALK) plays a critical role in the development of various cancers. In this study, the dataset of 1810 collected inhibitors were divided into a training set and a test set by the self-organizing map (SOM) and random method, ...
Humans Structure-Activity Relationship Protein Kinase Inhibitors Anaplastic Lymphoma Kinase Machine Learning Algorithms Support Vector Machine
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Machine learning-based rational design for efficient discovery of allatostatin analogs as promising lead candidates for novel IGRs.

Pest management science Nov 8, 2024
BACKGROUND: Insect neuropeptide allatostatins (ASTs) play a vital role in regulating insect growth, development, and reproduction, making them potential candidates for new insect growth regulators (IGRs). However, the practical use of natural ASTs in...
Machine Learning Neuropeptides Animals Drug Design Juvenile Hormones Drug Discovery Structure-Activity Relationship
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Wee1 inhibitor optimization through deep-learning-driven decision making.

European journal of medicinal chemistry Sep 29, 2024
Deep learning has gained increasing attention in recent years, yielding promising results in hit screening and molecular optimization. Herein, we employed an efficient strategy based on multiple deep learning techniques to optimize Wee1 inhibitors, w...
Cell Cycle Proteins Structure-Activity Relationship Molecular Structure Protein Kinase Inhibitors Cell Line, Tumor Cell Proliferation Dose-Response Relationship, Drug Protein-Tyrosine Kinases Decision Making Drug Screening Assays, Antitumor Humans Antineoplastic Agents Deep Learning
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Deep-Learning-Driven Discovery of SN3-1, a Potent NLRP3 Inhibitor with Therapeutic Potential for Inflammatory Diseases.

Journal of medicinal chemistry Sep 20, 2024
The NLRP3 inflammasome plays a central role in the pathogenesis of various intractable human diseases, making it an urgent target for therapeutic intervention. Here, we report the development of SN3-1, a novel orally potent NLRP3 inhibitor, designed ...
Humans Animals Mice Inflammation Mice, Inbred C57BL Male Drug Discovery Deep Learning Crystallography, X-Ray Structure-Activity Relationship NLR Family, Pyrin Domain-Containing 3 Protein Inflammasomes
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Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning.

Critical reviews in toxicology Sep 3, 2024
This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and...
Animals Structure-Activity Relationship Quantitative Structure-Activity Relationship Cheminformatics Humans Machine Learning
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Discovery of AMPs from random peptides via deep learning-based model and biological activity validation.

European journal of medicinal chemistry Aug 26, 2024
The ample peptide field is the best source for discovering clinically available novel antimicrobial peptides (AMPs) to address emerging drug resistance. However, discovering novel AMPs is complex and expensive, representing a major challenge. Recent ...
Staphylococcal Infections Hemolysis Antimicrobial Peptides Deep Learning Microbial Sensitivity Tests Animals Staphylococcus aureus Mice Peptides Dose-Response Relationship, Drug Drug Discovery Structure-Activity Relationship Humans Anti-Bacterial Agents
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Deep mutational scanning and machine learning for the analysis of antimicrobial-peptide features driving membrane selectivity.

Nature biomedical engineering Jul 31, 2024
Many antimicrobial peptides directly disrupt bacterial membranes yet can also damage mammalian membranes. It is therefore central to their therapeutic use that rules governing the membrane selectivity of antimicrobial peptides be deciphered. However,...
Animals Microbial Sensitivity Tests Amino Acid Sequence Machine Learning Mice Cell Membrane Antimicrobial Cationic Peptides Female Antimicrobial Peptides Structure-Activity Relationship Mutation
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From mundane to surprising nonadditivity: drivers and impact on ML models.

Journal of computer-aided molecular design Jul 25, 2024
Nonadditivity (NA) in Structure-Activity and Structure-Property Relationship (SAR) data is a rare but very information rich phenomenon. It can indicate conformational flexibility, structural rearrangements, and errors in assay results and structural ...
Structure-Activity Relationship Ligands Molecular Conformation Humans Machine Learning Drug Discovery
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Deriving general structure-activity/selectivity relationship patterns for different subfamilies of cyclin-dependent kinase inhibitors using machine learning methods.

Scientific reports Jul 3, 2024
Cyclin-dependent kinases (CDKs) play essential roles in regulating the cell cycle and are among the most critical targets for cancer therapy and drug discovery. The primary objective of this research is to derive general structure-activity relationsh...
Humans Neural Networks, Computer Machine Learning Drug Discovery Structure-Activity Relationship Protein Kinase Inhibitors Cyclin-Dependent Kinases
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