AIMC Topic: Structure-Activity Relationship

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Discovery and characterization of novel FAK inhibitors for breast cancer therapy via hybrid virtual screening, biological evaluation and molecular dynamics simulations.

Bioorganic chemistry Mar 26, 2025
Focal adhesion kinase (FAK) is a critical drug target implicated in various disease pathways, including hematological malignancies and breast cancer. Therefore, identifying FAK inhibitors with novel scaffolds could offer new opportunities for develop...
Female Breast Neoplasms Molecular Docking Simulation Humans Molecular Structure Molecular Dynamics Simulation Drug Evaluation, Preclinical Protein Kinase Inhibitors Focal Adhesion Kinase 1 Focal Adhesion Protein-Tyrosine Kinases Dose-Response Relationship, Drug Drug Screening Assays, Antitumor Structure-Activity Relationship Drug Discovery Antineoplastic Agents Cell Proliferation
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Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques.

Experimental biology and medicine (Maywood, N.J.) Mar 19, 2025
Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting pain transmission in the spinal cord. However, current opioids are addictive, often leading to overd...
Deep Learning Protein Binding Structure-Activity Relationship Receptors, Opioid, mu Humans Support Vector Machine Analgesics, Opioid Machine Learning Ligands
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Machine learning-based activity prediction of phenoxy-imine catalysts and its structure-activity relationship study.

Molecular diversity Mar 7, 2025
This study systematically investigates the structure-activity relationships of 30 Ti-phenoxy-imine (FI-Ti) catalysts using machine learning (ML) approaches. Among the tested algorithms, XGBoost demonstrated superior predictive performance, achieving ...
Titanium Imines Machine Learning Structure-Activity Relationship Catalysis
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Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms.

Journal of agricultural and food chemistry Feb 27, 2025
A large number of mycotoxins and related fungal metabolites have not been assessed in terms of their toxicological impacts. Current methodologies often prioritize specific target families, neglecting the complexity and presence of co-occurring compou...
Cluster Analysis Machine Learning Humans Molecular Structure Unsupervised Machine Learning Algorithms Neural Networks, Computer Structure-Activity Relationship Mycotoxins Animals Supervised Machine Learning
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Machine Learning-Driven Discovery of Structurally Related Natural Products as Activators of the Cardiac Calcium Pump SERCA2a.

ChemMedChem Feb 6, 2025
A key molecular dysfunction in heart failure is the reduced activity of the cardiac sarcoplasmic reticulum Ca-ATPase (SERCA2a) in cardiac muscle cells. Reactivating SERCA2a improves cardiac function in heart failure models, making it a validated targ...
Biological Products Structure-Activity Relationship Molecular Structure Drug Discovery Mice Dose-Response Relationship, Drug Calcium Sarcoplasmic Reticulum Calcium-Transporting ATPases Machine Learning Animals
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Rational design and synthesis of pyrazole derivatives as potential SARS-CoV-2 M inhibitors: An integrated approach merging combinatorial chemistry, molecular docking, and deep learning.

Bioorganic & medicinal chemistry Feb 3, 2025
The global impact of SARS-CoV-2 has highlighted the urgent need for novel antiviral therapies. This study integrates combinatorial chemistry, molecular docking, and deep learning to design, evaluate and synthesize new pyrazole derivatives as potentia...
Pyrazoles Protease Inhibitors Combinatorial Chemistry Techniques Coronavirus 3C Proteases Deep Learning Antiviral Agents Structure-Activity Relationship SARS-CoV-2 Molecular Docking Simulation Drug Design Humans
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General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach.

Molecular diversity Jan 20, 2025
Adenosine receptors (A, A, A, A) play critical roles in cellular signaling and are implicated in various physiological and pathological processes, including inflammations and cancer. The main aim of this research was to investigate structure-activity...
Humans Structure-Activity Relationship Models, Molecular Receptors, Purinergic P1 Purinergic P1 Receptor Antagonists Neural Networks, Computer Machine Learning Algorithms
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High-Accuracy Identification and Structure-Activity Analysis of Antioxidant Peptides via Deep Learning and Quantum Chemistry.

Journal of chemical information and modeling Jan 7, 2025
Antioxidant peptides (AOPs) hold great promise for mitigating oxidative-stress-related diseases, but their discovery is hindered by inefficient and time-consuming traditional methods. To address this, we developed an innovative framework combining ma...
Antioxidants Structure-Activity Relationship Quantum Theory Peptides Deep Learning
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DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.

Nature communications Jan 2, 2025
Computational methods for predicting protein function are of great significance in understanding biological mechanisms and treating complex diseases. However, existing computational approaches of protein function prediction lack interpretability, mak...
Computational Biology Proteins Databases, Protein Humans Protein Conformation Structure-Activity Relationship Algorithms Models, Molecular Deep Learning Protein Domains
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Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery.

Nature communications Dec 30, 2024
Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial i...
Mice Female Liposomes COVID-19 Vaccines SARS-CoV-2 Nanoparticles Humans RNA, Messenger Animals Lipids Artificial Intelligence Structure-Activity Relationship COVID-19
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