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QSPR Analysis of Drugs for Treatment of Schizophrenia Using Topological Indices
[Image: see text] Schizophrenia is a chronic psychotic disorder characterized primarily by cognitive deficits. Drugs and therapies are helpful in managing the symptoms, mostly with long-term compliance. There is a pressing need to design more efficient drugs with fewer adverse effects. Solubility, m...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10633864/ https://www.ncbi.nlm.nih.gov/pubmed/37970009 http://dx.doi.org/10.1021/acsomega.3c05000 |
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author | Zhang, Xiujun Saif, Muhammad Jawwad Idrees, Nazeran Kanwal, Salma Parveen, Saima Saeed, Fatima |
author_facet | Zhang, Xiujun Saif, Muhammad Jawwad Idrees, Nazeran Kanwal, Salma Parveen, Saima Saeed, Fatima |
author_sort | Zhang, Xiujun |
collection | PubMed |
description | [Image: see text] Schizophrenia is a chronic psychotic disorder characterized primarily by cognitive deficits. Drugs and therapies are helpful in managing the symptoms, mostly with long-term compliance. There is a pressing need to design more efficient drugs with fewer adverse effects. Solubility, metabolic stability, toxicity, permeability, and transporter effects are important parameters in the efficacy of drug design, which in turn depend upon different physical and chemical characteristics of drugs. In recent years, there has been growing interest in developing computational tools for the discovery and development of drugs for schizophrenia. Some of these methods use machine learning algorithms to predict the efficacy and side effects of the potential drugs. Other studies have used computer simulations to understand the molecular mechanisms underlying the disease and identify new targets for drug development. Topological indices are numeric quantities linked to the chemical structure of drugs and predict the properties, reactivity, and stability of drugs through the quantitative structure–property relationship (QSPR). This work is aimed at using statistical techniques to link QSPR correlating properties with connectivity indices using linear regression. The QSPR model gives quite a better estimation of the properties of drugs, such as melting point, boiling point, enthalpy, flash point, molar refractivity, refractive index, complexity, molecular weight, and refractivity. Results are validated by comparing actual values to estimated values for the drugs. |
format | Online Article Text |
id | pubmed-10633864 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-106338642023-11-15 QSPR Analysis of Drugs for Treatment of Schizophrenia Using Topological Indices Zhang, Xiujun Saif, Muhammad Jawwad Idrees, Nazeran Kanwal, Salma Parveen, Saima Saeed, Fatima ACS Omega [Image: see text] Schizophrenia is a chronic psychotic disorder characterized primarily by cognitive deficits. Drugs and therapies are helpful in managing the symptoms, mostly with long-term compliance. There is a pressing need to design more efficient drugs with fewer adverse effects. Solubility, metabolic stability, toxicity, permeability, and transporter effects are important parameters in the efficacy of drug design, which in turn depend upon different physical and chemical characteristics of drugs. In recent years, there has been growing interest in developing computational tools for the discovery and development of drugs for schizophrenia. Some of these methods use machine learning algorithms to predict the efficacy and side effects of the potential drugs. Other studies have used computer simulations to understand the molecular mechanisms underlying the disease and identify new targets for drug development. Topological indices are numeric quantities linked to the chemical structure of drugs and predict the properties, reactivity, and stability of drugs through the quantitative structure–property relationship (QSPR). This work is aimed at using statistical techniques to link QSPR correlating properties with connectivity indices using linear regression. The QSPR model gives quite a better estimation of the properties of drugs, such as melting point, boiling point, enthalpy, flash point, molar refractivity, refractive index, complexity, molecular weight, and refractivity. Results are validated by comparing actual values to estimated values for the drugs. American Chemical Society 2023-10-24 /pmc/articles/PMC10633864/ /pubmed/37970009 http://dx.doi.org/10.1021/acsomega.3c05000 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Zhang, Xiujun Saif, Muhammad Jawwad Idrees, Nazeran Kanwal, Salma Parveen, Saima Saeed, Fatima QSPR Analysis of Drugs for Treatment of Schizophrenia Using Topological Indices |
title | QSPR Analysis of
Drugs for Treatment of Schizophrenia
Using Topological Indices |
title_full | QSPR Analysis of
Drugs for Treatment of Schizophrenia
Using Topological Indices |
title_fullStr | QSPR Analysis of
Drugs for Treatment of Schizophrenia
Using Topological Indices |
title_full_unstemmed | QSPR Analysis of
Drugs for Treatment of Schizophrenia
Using Topological Indices |
title_short | QSPR Analysis of
Drugs for Treatment of Schizophrenia
Using Topological Indices |
title_sort | qspr analysis of
drugs for treatment of schizophrenia
using topological indices |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10633864/ https://www.ncbi.nlm.nih.gov/pubmed/37970009 http://dx.doi.org/10.1021/acsomega.3c05000 |
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