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Includes bibliographical references and index

This article is from Journal of Cheminformatics , volume 6 . Abstract None

This article is from Journal of Cheminformatics , volume 6 . Abstract None

This article is from Journal of Cheminformatics , volume 6 . Abstract None

This article is from Journal of Cheminformatics , volume 6 . Abstract None

Chemoinformatics, an interdisciplinary field combining chemistry, computer science, and information technology, is revolutionising scientific research by using computational methods to analyse, interpret, and predict chemical and biological data. This review explores its pivotal role across drug discovery and natural product research, highlighting key methodologies and challenges. It is critical in drug discovery because it expedites the identification and optimization of drug candidates through techniques such as virtual screening, molecular docking, and quantitative structure-activity relationship (QSAR) studies. It also facilitates the integration of computational chemistry with experimental validation, improving predictions iteratively. Chemoinformatics helps with managing databases, virtually screening bioactive compounds, and figuring out structures through molecular dynamics simulations and metabolomics in the field of natural products research. These tools enable researchers to explore the therapeutic potential of indigenous medicinal plants and other natural sources, facilitating the discovery of novel bioactive molecules with potential pharmaceutical applications. However, challenges such as data quality, computational resources, and ethical considerations persist, particularly in regions with limited infrastructure and expertise. The integration of machine learning and big data analytics promises to further enhance predictive modelling and accelerate discoveries in chemoinformatics.

Chemoinformatics, an interdisciplinary field combining chemistry, computer science, and information technology, is revolutionising scientific research by using computational methods to analyse, interpret, and predict chemical and biological data. This review explores its pivotal role across drug discovery and natural product research, highlighting key methodologies and challenges. It is critical in drug discovery because it expedites the identification and optimization of drug candidates through techniques such as virtual screening, molecular docking, and quantitative structure-activity relationship (QSAR) studies. It also facilitates the integration of computational chemistry with experimental validation, improving predictions iteratively. Chemoinformatics helps with managing databases, virtually screening bioactive compounds, and figuring out structures through molecular dynamics simulations and metabolomics in the field of natural products research. These tools enable researchers to explore the therapeutic potential of indigenous medicinal plants and other natural sources, facilitating the discovery of novel bioactive molecules with potential pharmaceutical applications. However, challenges such as data quality, computational resources, and ethical considerations persist, particularly in regions with limited infrastructure and expertise. The integration of machine learning and big data analytics promises to further enhance predictive modelling and accelerate discoveries in chemoinformatics.

Chemoinformatics, an interdisciplinary field combining chemistry, computer science, and information technology, is revolutionising scientific research by using computational methods to analyse, interpret, and predict chemical and biological data. This review explores its pivotal role across drug discovery and natural product research, highlighting key methodologies and challenges. It is critical in drug discovery because it expedites the identification and optimization of drug candidates through techniques such as virtual screening, molecular docking, and quantitative structure-activity relationship (QSAR) studies. It also facilitates the integration of computational chemistry with experimental validation, improving predictions iteratively. Chemoinformatics helps with managing databases, virtually screening bioactive compounds, and figuring out structures through molecular dynamics simulations and metabolomics in the field of natural products research. These tools enable researchers to explore the therapeutic potential of indigenous medicinal plants and other natural sources, facilitating the discovery of novel bioactive molecules with potential pharmaceutical applications. However, challenges such as data quality, computational resources, and ethical considerations persist, particularly in regions with limited infrastructure and expertise. The integration of machine learning and big data analytics promises to further enhance predictive modelling and accelerate discoveries in chemoinformatics.

Includes bibliographical references and index

Includes bibliographical references and index

This article is from Marine Drugs , volume 12 . Abstract The comprehensive information of small molecules and their biological activities in the PubChem database allows chemoinformatic researchers to access and make use of large-scale biological activity data to improve the precision of drug profiling. A Quantitative Structure–Activity Relationship approach, for classification, was used for the prediction of active/inactive compounds relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1804 compounds from PubChem. Using the best classification models for antibiotic and antitumor activities a data set of marine and microbial natural products from the AntiMarin database were screened—57 and 16 new lead compounds for antibiotic and antitumor drug design were proposed, respectively. All compounds proposed by our approach are classified as non-antibiotic and non-antitumor compounds in the AntiMarin database. Recently several of the lead-like compounds proposed by us were reported as being active in the literature.

This article is from Journal of Cheminformatics , volume 6 . Abstract None

This article is from Journal of Cheminformatics , volume 6 . Abstract None