Structure-Activity Relationship

1,3,4-oxadiazole derivatives: synthesis, characterization, antifungal activity, DNA binding investigations, TD-DFT calculations, and molecular modelling.

Journal of biomolecular structure & dynamics 39886763

1,3,4-Oxadiazole-based heterocyclic analogs (3a-3m) were synthesized cyclization of Schiff bases with substituted aldehydes in the presence of bromine and acetic acid. The structural clarification of synthesized molecules was carried out with variou...

Antifungal Agents Antioxidants Density Functional Theory DNA Humans Microbial Sensitivity Tests Models, Molecular Molecular Docking Simulation Molecular Structure Oxadiazoles Structure-Activity Relationship

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Identification of nanoparticle infiltration in human breast milk: Chemical profiles and trajectory pathways.

Proceedings of the National Academy of Sciences of the United States of America 40354532

Breast milk is crucial for infant health, offering essential nutrients and immune protection. However, despite increasing exposure risks from nanoparticles (NPs), their potential infiltration into human breast milk remains poorly understood. This stu...

Female Humans Milk, Human Nanoparticles Particle Size Structure-Activity Relationship

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Data-Driven Insights into Porphyrin Geometry: Interpretable AI for Non-Planarity and Aromaticity Analyses.

Journal of chemical information and modeling 40253627

Porphyrins are involved in numerous and very different chemical and biological processes, due to the sensitivity of their application-relevant properties to subtle structural changes. Applying modern machine learning methodology is very appealing for...

Ligands Machine Learning Models, Molecular Porphyrins Structure-Activity Relationship

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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.) 40177220

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...

Analgesics, Opioid Deep Learning Humans Ligands Machine Learning Protein Binding Receptors, Opioid, mu Structure-Activity Relationship Support Vector Machine

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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 39772654

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 Deep Learning Peptides Quantum Theory Structure-Activity Relationship

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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 40013497

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...

Algorithms Animals Cluster Analysis Humans Machine Learning Molecular Structure Mycotoxins Neural Networks, Computer Structure-Activity Relationship Supervised Machine Learning Unsupervised Machine Learning

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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 39929031

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...

Antiviral Agents Combinatorial Chemistry Techniques Coronavirus 3C Proteases Deep Learning Drug Design Humans Molecular Docking Simulation Protease Inhibitors Pyrazoles SARS-CoV-2 Structure-Activity Relationship

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Machine Learning-Driven Discovery of Structurally Related Natural Products as Activators of the Cardiac Calcium Pump SERCA2a.

ChemMedChem 39853697

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...

Animals Biological Products Calcium Dose-Response Relationship, Drug Drug Discovery Machine Learning Mice Molecular Structure Sarcoplasmic Reticulum Calcium-Transporting ATPases Structure-Activity Relationship

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A deep learning model for structure-based bioactivity optimization and its application in the bioactivity optimization of a SARS-CoV-2 main protease inhibitor.

European journal of medicinal chemistry 40239482

Bioactivity optimization is a crucial and technical task in the early stages of drug discovery, traditionally carried out through iterative substituent optimization, a process that is often both time-consuming and expensive. To address this challenge...

Antiviral Agents Coronavirus 3C Proteases COVID-19 COVID-19 Drug Treatment Deep Learning Drug Discovery Humans Molecular Structure Protease Inhibitors SARS-CoV-2 Structure-Activity Relationship

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Discovery of naturally inspired antimicrobial peptides using deep learning.

Bioorganic chemistry 40209356

Non-ribosomal peptides (NRPs) are promising lead compounds for novel antibiotics. Bioinformatic mining of silent microbial NRPS gene clusters provide crucial insights for the discovery and de novo design of bioactive peptides. Here, we describe the e...

Anti-Bacterial Agents Antimicrobial Peptides Deep Learning Dose-Response Relationship, Drug Drug Discovery Humans Microbial Sensitivity Tests Molecular Structure Multigene Family Structure-Activity Relationship

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Explore the latest research on artificial intelligence and machine learning in medicine.

1,3,4-oxadiazole derivatives: synthesis, characterization, antifungal activity, DNA binding investigations, TD-DFT calculations, and molecular modelling.

Identification of nanoparticle infiltration in human breast milk: Chemical profiles and trajectory pathways.

Data-Driven Insights into Porphyrin Geometry: Interpretable AI for Non-Planarity and Aromaticity Analyses.

Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques.

High-Accuracy Identification and Structure-Activity Analysis of Antioxidant Peptides via Deep Learning and Quantum Chemistry.

Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms.

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.

Machine Learning-Driven Discovery of Structurally Related Natural Products as Activators of the Cardiac Calcium Pump SERCA2a.

A deep learning model for structure-based bioactivity optimization and its application in the bioactivity optimization of a SARS-CoV-2 main protease inhibitor.

Discovery of naturally inspired antimicrobial peptides using deep learning.

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