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The Role of Natural Products in COVID-19
Published in Hanadi Talal Ahmedah, Muhammad Riaz, Sagheer Ahmed, Marius Alexandru Moga, The Covid-19 Pandemic, 2023
Iqra Akhtar, Sumera Javad, Tehreema Iftikhar, Amina Tariq, Hammad Majeed, Asma Ahmad, Muhammad Arfan, M. Zia-Ul-Haq
There are a number of computational tools available now which accelerate the drug discovery and decrease the budget of discovery. These tools help us to understand the fact that a chemical compound can be used in a drug or not. We can also make an estimation of the mechanism of drug action and its possible safe dose. These computational techniques involve Chemoinformatics, Network Pharmacology, Molecular Similarity, Pharmacogenomics De Novo Design, Quantitative Structure-Activity Relation and Molecular Docking, etc. [68–70].
Cancer Informatics
Published in Trevor F. Cox, Medical Statistics for Cancer Studies, 2022
The term Informatics has different connotations and definitions in different countries. For some it would simply be “computer science”. I like the definition of,“The science of information and the practice of information processing”. When applied to various disciplines, we have subjects like Engineering Informatics, Chemoinformatics, Financial Informatics, Bioinformatics and for us, Cancer Informatics which is a subset of Bioinformatics. Big Data and Big Data Analytics are about storing, analysing and extracting information from very large datasets. How large does a large dataset have to be, to be called “Big Data”? The cancer informatics data we will use relate to several thousand genes on, say, fifty patients and would be a small fraction of the size a large bank's total financial transactions over a period of a year. The term, informatics emerged in the 1950's and big data in the 1990's.
Approaches for Identification and Validation of Antimicrobial Compounds of Plant Origin: A Long Way from the Field to the Market
Published in Mahendra Rai, Chistiane M. Feitosa, Eco-Friendly Biobased Products Used in Microbial Diseases, 2022
Lívia Maria Batista Vilela, Carlos André dos Santos-Silva, Ricardo Salas Roldan-Filho, Pollyanna Michelle da Silva, Marx de Oliveira Lima, José Rafael da Silva Araújo, Wilson Dias de Oliveira, Suyane de Deus e Melo, Madson Allan de Luna Aragão, Thiago Henrique Napoleão, Patrícia Maria Guedes Paiva, Ana Christina Brasileiro-Vidal, Ana Maria Benko-Iseppon
Chemoinformatics tools for small molecule analysis play an essential role in many fields, impacting biochemistry, genomics, agronomy and drug discovery. For this, tools are applied to analyze structural similarities, physical-chemical properties, and bioactivity profiles of natural and synthetic compounds to predict their mechanisms of action in biological systems (Backman et al. 2011; Medina-Franco et al. 2021). Drug design based on molecular structure is becoming an essential tool for discovering compounds faster and more economically than the traditional method (Batool et al. 2019), here named bottom-up. The introduction of bioinformatics analysis in these surveys has led to an expansive collection of computational tools that assist in searching and designing proteins and enzymes (Copeland et al. 2012), i.e., a top-down approach.
Galaxy for open-source computational drug discovery solutions
Published in Expert Opinion on Drug Discovery, 2023
Anamika Singh Gaur, Selvaraman Nagamani, Lipsa Priyadarsinee, Hridoy J. Mahanta, Ramakrishnan Parthasarathi, G. Narahari Sastry
The MPDS portals are developed for different diseases namely Tuberculosis (MPDSTB) and Diabetes Mellitus (MPDSDM) [13–16]. The MPDSCOVID-19, MPDSHIV, MPDSMD, MPDSNAFLD web portals are currently under development stage. MPDS integrates various disease specific information such as genes, proteins, literature, pathways, approved drugs and clinical trial drugs, etc. The recent MPDSCOVID-19 portal contains the computational results from our group that encompasses polypharmacology and protein-protein interaction analysis in COVID-19 research. Different chemoinformatics, bioinformatics, and machine learning tools for drug discovery have been incorporated in the same platform. The web portal is structured into four categories: i) data library, ii) data processing, iii) data analysis, and iv) advanced modules. The modules in MPDS comprises of disease-independent and disease-dependent modules. Figure 7 gives an overview of the structure of MPDS modules.
Efficient predictions of cytotoxicity of TiO2-based multi-component nanoparticles using a machine learning-based q-RASAR approach
Published in Nanotoxicology, 2023
Arkaprava Banerjee, Supratik Kar, Souvik Pore, Kunal Roy
The emergence of various Machine Learning (ML) algorithms and their potential applicability in cheminformatics has led to their adoption by researchers from all over the world (Varnek and Baskin 2012). Many informatics and omics researchers believe that ML, a form of Artificial Intelligence (AI), can effectively substitute in-vivo tests shortly thus obviating animal experimentations. The most promising features of ML algorithms lie in their ability to produce fast, reliable, and accurate results with minimal manpower. Among the earliest and most popular ML applications used to predict the activity/property/toxicity of substances are Quantitative Structure-Activity/Property/Toxicity Relationships (QSAR/QSPR/QSTR). Classical QSAR approaches involve a set of independent variables known as descriptors used to predict the response variable by using a simple and interpretable mathematical model represented as an equation (Roy, Kar, and Das 2015). The prediction results generated by the QSAR approach are accepted by regulations such as EU-REACH (https://echa.europa.eu/regulations/reach/understanding-reach) for data gap filling in case of the non-availability of experimental data (García-Fernández 2020).
The impact of chemoinformatics on drug discovery in the pharmaceutical industry
Published in Expert Opinion on Drug Discovery, 2020
Karina Martinez-Mayorga, Abraham Madariaga-Mazon, José L. Medina-Franco, Gerald Maggiora
There are several definitions of chemoinformatics reviewed in the literature [1–4]. The term ‘chemoinformatics’ was coined in 1998 by Frank Brown as the ‘mixing of information resources to transform data into information, and information into knowledge, for the intended purpose of making decisions faster in the arena of drug lead identification and optimization’ [5]. During a scientific conference in 1999, Greg Paris provided a broader definition: ‘Chemoinformatics is a generic term that encompasses the design, creation, organization, management, retrieval, analysis, dissemination, visualization, and use of chemical information’ [6]. More recently, Gasteiger and Funatsu provided an even broader definition: ‘Chemoinformatics is the application of informatics methods to solve chemical problems’ [7]. This latter definition is associated with the terms proposed previously: ‘Chemical informatics’ defined as ‘application of information technology to chemistry’ and ‘chemometrics’ generally understood as the quantitative analysis of chemical data using mathematical and statistical methods [8].