Comparative analysis of topological entropy levels in covalent organic radical frameworks and mathematical models for predicting graph energy

Chemical Papers, Год журнала: 2025, Номер unknown

Опубликована: Янв. 11, 2025

Язык: Английский

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Eccentric indices based QSPR evaluation of drugs for schizophrenia treatment

Heliyon, Год журнала: 2025, Номер 11(2), С. e42222 - e42222

Опубликована: Янв. 1, 2025

Schizophrenia is a long-term, serious mental health condition that affects how person thinks, perceives, and behaves. This disorder often results in substantial difficulties social interactions work performance. Individuals with schizophrenia might appear disconnected from reality, causing significant distress both for themselves their Friends. Although symptoms of can vary to person, they typically fall into three main categories: cognitive, negative, psychotic. Creating computational tools find develop drugs has more interest the past few years. Regardless developments drug design, fundamental approach still uses topological descriptors. Topological indices are used estimate bioactivity chemical compounds QSAR/QSPR studies. In general, use quantitative structure-property relationship (QSPR), numerical values connected structures predict reactivity, stability, properties. focuses on calculating different eccentric (EIs), developing regression model thirteen anti-schizophrenia drugs, applying statistical methods establish linear between QSPR correlating properties indices. Statistical analysis shows p-values less than equals 0.05, f-test value (>2.5) , correlation r greater 0.7 validate calculations. The coefficient (r2) convenient tool evaluating models' quality. r2>0.7 essential good model. show significance results, while accuracy results. order fit models calculated index values, eight physicochemical examined. Drug like molar refractivity (cm3), refractive enthalpy (kJ/mol), melting, boiling flash points (°C), complexity, molecular weight all effectively estimated by By examining actual verified.

Язык: Английский

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On QSPR analysis of glaucoma drugs using machine learning with XGBoost and regression models

Computers in Biology and Medicine, Год журнала: 2025, Номер 187, С. 109731 - 109731

Опубликована: Янв. 28, 2025

Язык: Английский

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Predictive analysis of vitiligo treatment drugs using degree and neighborhood degree-based topological descriptors

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 12, 2025

Vitiligo is a chronic autoimmune condition that leads to the loss of skin pigmentation in certain areas due destruction melanocytes, which produce pigment. A topological index numerical value obtained from structure chemical graph and useful for studying theoretical characteristics organic molecules. It can also help determine physico-chemical biological aspects various drugs. This article uses novel neighborhood degree-based indices study vitiligo drugs demonstrates strong correlation with properties. Additionally, results are compared those through indices.

Язык: Английский

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Predicting glass transition temperatures for structurally diverse polymers

Colloid & Polymer Science, Год журнала: 2025, Номер unknown

Опубликована: Апрель 8, 2025

Язык: Английский

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Computational Analysis of Benzenoid Systems Using Valency‐Based Entropy Metrics and Topological Indices

Complexity, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

Nanomaterials find application in electronics, drugs, and biology among other disciplines. Benzenoid systems with their homogeneous structures are especially fit for computer study because of predictable geometries. This work investigates computational analysis benzenoid using valency‐based entropy measurements degree‐based topological indices. Knowing these indices helps one to anticipate the reactivity stability related compounds. The main focus is on thermodynamic parameter entropy, which reveals how can modify hydrocarbon improve characteristics. In this work, combined facilitates prediction enhancement system physicochemical features. grasp possible applications nanotechnology medicine.

Язык: Английский

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Hydrogen-centric machine learning approach for analyzing properties of tricyclic anti-depressant drugs

Frontiers in Chemistry, Год журнала: 2025, Номер 13

Опубликована: Июнь 3, 2025

Introduction Tricyclic anti-depressant (TCA) drugs are widely used to treat depression, but traditional methods for evaluating their physicochemical properties can be time-consuming and costly. This study examines how topological indices help predict the of TCA drugs, with a special focus on role hydrogen representation. Methods Two molecular configurations were analyzed: one only explicit other including all atoms. To assess predictive performance, linear regression (LR) support vector (SVR) models employed. Results The results showed that adding atoms strong correlations, especially polarizability, molar refractivity, volume. Among employed, SVR provided more accurate results. Additionally, representation had stronger impact SVR's predictions. Discussion These findings highlight potential using machine learning techniques in quantitative structure-property relationship (QSPR) efficient reliable predictions drug properties.

Язык: Английский

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Topological information entropy and spectral energy analysis in aminal-linked covalent organic frameworks

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Июнь 5, 2025

The development of novel topologies in covalent organic frameworks (COFs) enhances reticular chemistry and materials science, offering a systematic approach to constructing COFs uncovering intricate relationships between their structures properties. inclusion aminal derived linkages enables with greater structural diversity exceptional functionalities. This study examines the graph-structural properties aminal-linked using entropy information hybrid topological descriptors, determining complexity. Additionally, we employ these descriptors as basis for robust statistical regression models, facilitating generation graph energies analysis HOMO-LUMO gaps.

Язык: Английский

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Predicting Antibacterial Drugs Properties Using Graph Topological Indices and Machine Learning

IEEE Access, Год журнала: 2024, Номер 12, С. 181420 - 181435

Опубликована: Янв. 1, 2024

Язык: Английский

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Application of machine learning in developing a quantitative structure–property relationship model for predicting the thermal decomposition temperature of nitrogen-rich energetic ionic salts

RSC Advances, Год журнала: 2024, Номер 14(51), С. 37737 - 37751

Опубликована: Янв. 1, 2024

A reliable QSPR model of thermal decomposition temperature ( T d ) was built and developed using support vector machine (SVM) learning technology to predict the property newly designed nitrogen-rich energetic ionic salts.

Язык: Английский

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