Quantitative Structure-Activity Relationship

Machine Learning Tool for New Selective Serotonin and Serotonin-Norepinephrine Reuptake Inhibitors.

Molecules (Basel, Switzerland) 39942741

Depression, a serious mood disorder, affects about 5% of the population. Currently, there are two groups of antidepressants that are the first-line treatment for depressive disorder: selective serotonin reuptake inhibitors and serotonin-norepinephrin...

Antidepressive Agents Humans Machine Learning Norepinephrine Plasma Membrane Transport Proteins Quantitative Structure-Activity Relationship Selective Serotonin Reuptake Inhibitors Serotonin and Noradrenaline Reuptake Inhibitors Serotonin Plasma Membrane Transport Proteins

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Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches.

Journal of chemical information and modeling 40016123

Nitroaromatic compounds (NAs) are widely used in industrial applications but pose significant genotoxic risks, necessitating accurate mutagenicity prediction for chemical safety assessments. This study integrates conceptual density functional theory ...

Density Functional Theory Machine Learning Mutagenicity Tests Mutagens Nitro Compounds Quantitative Structure-Activity Relationship

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Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning.

SAR and QSAR in environmental research 39980415

Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating symptoms of dementia that downgrades to cholinergic neuronal loss in Alzheimer's disease. We developed a QSAR model to facilitate the identification of ...

Algorithms Butyrylcholinesterase Cholinesterase Inhibitors Computer Simulation Drug Discovery Humans Machine Learning Quantitative Structure-Activity Relationship

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A semiempirical and machine learning approach for fragment-based structural analysis of non-hydroxamate HDAC3 inhibitors.

Biophysical chemistry 39978120

Interest in HDAC3 inhibitors (HDAC3i) for pharmacological applications outside of cancer is growing. However, concerns regarding the possible mutagenicity of the commonly used hydroxamates (zinc-binding groups, ZBGs) are also increasing. Considering ...

Histone Deacetylase Inhibitors Histone Deacetylases Humans Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation Molecular Structure Quantitative Structure-Activity Relationship Quantum Theory Thermodynamics

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Discovery of New HER2 Inhibitors via Computational Docking, Pharmacophore Modeling, and Machine Learning.

Molecular informatics 39976334

The human epidermal growth factor receptor 2 (HER2) is a critical oncogene implicated in the development of various aggressive cancers, particularly breast cancer. Discovering novel HER2 inhibitors is crucial for expanding therapeutic options for HER...

Antineoplastic Agents Drug Discovery Humans Machine Learning Molecular Docking Simulation Pharmacophore Protein Kinase Inhibitors Quantitative Structure-Activity Relationship Receptor, ErbB-2

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AI-Augmented R-Group Exploration in Medicinal Chemistry.

Journal of chemical information and modeling 39959996

Efficient R-group exploration in the vast chemical space, enabled by increasingly available building blocks or generative AI, remains an open challenge. Here, we developed an enhanced Free-Wilson QSAR model embedding R-groups by atom-centric pharmaco...

Artificial Intelligence Chemistry, Pharmaceutical Quantitative Structure-Activity Relationship Stereoisomerism

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Prediction of the Extent of Blood-Brain Barrier Transport Using Machine Learning and Integration into the LeiCNS-PK3.0 Model.

Pharmaceutical research 39930309

INTRODUCTION: The unbound brain-to-plasma partition coefficient (K) is an essential parameter for predicting central nervous system (CNS) drug disposition using physiologically-based pharmacokinetic (PBPK) modeling. K values for specific compounds ar...

Algorithms Animals Biological Transport Blood-Brain Barrier Brain Central Nervous System Agents Machine Learning Models, Biological Pharmaceutical Preparations Quantitative Structure-Activity Relationship Rats

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Classification and regression machine learning models for predicting mixed toxicity of carbamazepine and its transformation products.

Environmental research 39929412

Carbamazepine (CBZ) and its transformation products (TPs) often occur in aquatic environments in the form of mixtures, posing potential risks to ecosystems. However, establishing standardized protocols for synthesizing, isolating, and acquiring these...

Acridines Anticonvulsants Carbamazepine Machine Learning Models, Statistical Proflavine Quantitative Structure-Activity Relationship Regression Analysis Toxicity Tests Vibrio Water Pollutants, Chemical

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Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 39921483

Novel antifungal drugs that evade resistance are urgently needed for Candida infections. Antifungal peptides (AFPs) are potential candidates due to their specific mechanism of action, which makes them less prone to developing drug resistance. An AFP ...

Animals Antifungal Agents Candida albicans Candidiasis Deep Learning Drug Design Microbial Sensitivity Tests Peptides Quantitative Structure-Activity Relationship

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In-silico exploring pathway and mechanism-based therapeutics for allergic rhinitis: Network pharmacology, molecular docking, ADMET, quantum chemistry and machine learning based QSAR approaches.

Computers in biology and medicine 39908918

Allergic rhinitis is a devastating health complication that interrupts the quality of daily life and significantly affects around 40 % of the population worldwide. Despite the availability of various treatment options, many patients continue to strug...

Computer Simulation Humans Machine Learning Molecular Docking Simulation Network Pharmacology Quantitative Structure-Activity Relationship Rhinitis, Allergic

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

Quantitative Structure-Activity Relationship

Machine Learning Tool for New Selective Serotonin and Serotonin-Norepinephrine Reuptake Inhibitors.

Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches.

Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning.

A semiempirical and machine learning approach for fragment-based structural analysis of non-hydroxamate HDAC3 inhibitors.

Discovery of New HER2 Inhibitors via Computational Docking, Pharmacophore Modeling, and Machine Learning.

AI-Augmented R-Group Exploration in Medicinal Chemistry.

Prediction of the Extent of Blood-Brain Barrier Transport Using Machine Learning and Integration into the LeiCNS-PK3.0 Model.

Classification and regression machine learning models for predicting mixed toxicity of carbamazepine and its transformation products.

Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.

In-silico exploring pathway and mechanism-based therapeutics for allergic rhinitis: Network pharmacology, molecular docking, ADMET, quantum chemistry and machine learning based QSAR approaches.

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