AIMC Topic: Structure-Activity Relationship

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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 Structure-Activity Relationship Neuropeptides Juvenile Hormones Animals Drug Design Drug Discovery
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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...
Dose-Response Relationship, Drug Protein-Tyrosine Kinases Decision Making Deep Learning Cell Line, Tumor Cell Proliferation Protein Kinase Inhibitors Cell Cycle Proteins Drug Screening Assays, Antitumor Structure-Activity Relationship Molecular Structure Humans Antineoplastic Agents
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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 Mice, Inbred C57BL Structure-Activity Relationship Deep Learning NLR Family, Pyrin Domain-Containing 3 Protein Inflammasomes Crystallography, X-Ray Animals Male Drug Discovery Mice Inflammation
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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...
Quantitative Structure-Activity Relationship Humans Cheminformatics Machine Learning Animals Structure-Activity Relationship
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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 ...
Structure-Activity Relationship Staphylococcal Infections Hemolysis Antimicrobial Peptides Mice Staphylococcus aureus Dose-Response Relationship, Drug Drug Discovery Deep Learning Microbial Sensitivity Tests Humans Anti-Bacterial Agents Animals Peptides
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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,...
Mutation Machine Learning Female Amino Acid Sequence Mice Antimicrobial Peptides Animals Cell Membrane Antimicrobial Cationic Peptides Microbial Sensitivity Tests Structure-Activity Relationship
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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 ...
Molecular Conformation Drug Discovery Structure-Activity Relationship Humans Ligands Machine Learning
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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...
Protein Kinase Inhibitors Machine Learning Humans Cyclin-Dependent Kinases Drug Discovery Neural Networks, Computer Structure-Activity Relationship
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Synthesis, Docking, and Machine Learning Studies of Some Novel Quinolinesulfonamides-Triazole Hybrids with Anticancer Activity.

Molecules (Basel, Switzerland) Jul 2, 2024
In the presented work, a series of 22 hybrids of 8-quinolinesulfonamide and 1,4-disubstituted triazole with antiproliferative activity were designed and synthesised. The title compounds were designed using molecular modelling techniques. For this pur...
Cell Proliferation Structure-Activity Relationship Molecular Structure Molecular Dynamics Simulation Sulfonamides Quinolines Molecular Docking Simulation Triazoles Antineoplastic Agents Cell Line, Tumor Humans Machine Learning
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PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization.

European journal of medicinal chemistry Jun 25, 2024
Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial in...
Peptides, Cyclic Humans Dose-Response Relationship, Drug Macrocyclic Compounds Peptides Structure-Activity Relationship Deep Learning Molecular Structure
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