Web Resources for Discovery and Development of New Medicines

Bezhentsev, Vladislav M.; Дружиловский, Д. С.; Ivanov, Sergey; Филимонов, Д. А.; Sastry, G. Narahari; Poroikov, Vladimir

doi:10.1007/s11094-017-1563-x

Cited by 18 publications

(4 citation statements)

References 52 publications

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“…The PASS online web server is widely used for the prediction of the biological activity of compounds, including different pharmacological effects, toxic and adverse effects, mechanisms of actions, and the influence of gene expression. − The biological activity of a molecule is calculated based on the structure activity relationships using a training set of >300 000 biologically active substances (i.e., drugs, drug-like molecules, lead compounds, and toxins) in the PASS server. The activity in each category (i.e., antiviral, antibacterial, and antimycobacterial) is evaluated by the probability of active ( P a ) value.…”

Section: Methodsmentioning

confidence: 99%

Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning Approaches

Nagamani

Sastry

2021

ACS Omega

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The drug-resistant strains of Mycobacterium tuberculosis (M.tb) are evolving at an alarming rate, and this indicates the urgent need for the development of novel antitubercular drugs. However, genetic mutations, complex cell wall system of M.tb, and influx−efflux transporter systems are the major permeability barriers that significantly affect the M.tb drugs activity. Thus, most of the small molecules are ineffective to arrest the M.tb cell growth, even though they are effective at the cellular level. To address the permeability issue, different machine learning models that effectively distinguish permeable and impermeable compounds were developed. The enzyme-based (IC 50 ) and cell-based (minimal inhibitory concentration) data were considered for the classification of M.tb permeable and impermeable compounds. It was assumed that the compounds that have high activity in both enzyme-based and cell-based assays possess the required M.tb cell wall permeability. The XGBoost model was outperformed when compared to the other models generated from different algorithms such as random forest, support vector machine, and nai ̈ve Bayes. The XGBoost model was further validated using the validation data set (21 permeable and 19 impermeable compounds). The obtained machine learning models suggested that various descriptors such as molecular weight, atom type, electrotopological state, hydrogen bond donor/acceptor counts, and extended topochemical atoms of molecules are the major determining factors for both M.tb cell permeability and inhibitory activity. Furthermore, potential antimycobacterial drugs were identified using computational drug repurposing. All the approved drugs from DrugBank were collected and screened using the developed permeability model. The screened compounds were given as input in the PASS server for the identification of possible antimycobacterial compounds. The drugs that were retained after two filters were docked to the active site of 10 different potential antimycobacterial drug targets. The results obtained from this study may improve the understanding of M.tb permeability and activity that may aid in the development of novel antimycobacterial drugs.

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Section: Methodsmentioning

confidence: 99%

Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning Approaches

Nagamani

Sastry

2021

ACS Omega

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“…Целью доклинических исследований лекарственных средств для медицинского применения является получение оценок их безопасности, качества и эффективности с применением научных методов 1 . Традиционно изучение безопасности фармакологически активных веществ 2 осуществляется на экспериментальных животных в соответствии с правилами надлежащей лабораторной практики (Good Laboratory Practice, GLP) 3 . На основании экспертизы пакета документов, содержащего информацию о результатах изучения фармакодинамических, фармакокинетических и токсикологических характеристик фармакологически активных веществ, уполномоченным федеральным органом исполнительной власти выносится решение о выдаче разрешения на проведение клинических исследований 4 .…”

Section: Introductionunclassified

“…Методы компьютерного конструирования лекарственных средств (Computer-Aided Drug Design, CADD), широко применяемые в последние 30 лет для поиска перспективных лекарственно-подобных химических соединений (drug-like compounds) и оптимизации их свойств, стали использоваться на всех стадиях исследований и разработок новых лекарственных препаратов [1]. Предпосылкой для этого явилось накопление свободно доступных в сети Интернет обширных сведений о различных видах биологической активности веществ из самых разных химических классов [2]. В совокупности веб-ресурсы содержат огромный массив гетерогенных химических и биомедицинских данных, которые по своему объему, разнообразию и скорости накопления соответствуют понятию «большие данные» (Big Data) [3].…”

Section: Introductionunclassified

“…Доступные данные после предварительной обработки в соответствии с современными требованиями [4] используются в качестве обучающих выборок для построения количественных и классификационных моделей зависимостей «структура-активность» (structure-activity relationship, SAR) с применением различных методов машинного обучения 1 Федеральный закон от 12.04.2010 № 61-ФЗ «Об обращении лекарственных средств». 2 Здесь и далее термином «фармакологически активное вещество» обозначены органические соединения, изучаемые с целью создания лекарственных препаратов независимо от стадии исследований (тестирование биологической активности в экспериментах in vitro или in vivo, доклинические исследования, клинические испытания), а также разрешенные к медицинскому применению фармацевтические субстанции, побочное действие препаратов на основе которых изучается в процессе широкомасштабного применения в клинике, либо исследуемые с целью расширения первоначальных показаний к применению. 3 Приказ Министерства здравоохранения Российской Федерации от 01.04.2016 № 199н «Об утверждении Правил надлежащей лабораторной практики».…”

Section: Introductionunclassified

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In Silico Estimation of the Safety of Pharmacologically Active Substances Using Machine Learning Methods: A Review

Poroikov,

Dmitriev,

Druzhilovskiy

et al. 2023

Bezopasnost' i risk farmakoterapii

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Scientific relevance. Currently, machine learning (ML) methods are widely used in the research and development of new pharmaceuticals. ML methods are particularly important for assessing the safety of pharmacologically active substances early in the research process because such safety assessments significantly reduce the risk of obtaining negative results in the future.Aim. This study aimed to review the main information and prediction resources that can be used for the assessment of the safety of pharmacologically active substances in silico.Discussion. Novel ML methods can identify the most likely molecular targets for a specific compound to interact with, based on structure–activity relationship analysis. In addition, ML methods can be used to search for potential therapeutic and adverse effects, as well as to study acute and specific toxicity, metabolism, and other pharmacodynamic, pharmacokinetic, and toxicological characteristics of investigational substances. Obtained at early stages of research, this information helps to prioritise areas for experimental testing of biological activity, as well as to identify compounds with a low probability of producing adverse and toxic effects. This review describes free online ML-based information and prediction resources for assessing the safety of pharmacologically active substances using their structural formulas. Special attention is paid to the Russian computational products presented on the Way2Drug platform (https://www.way2drug.com/dr/).Conclusions. Contemporary approaches to the assessment of pharmacologically active substances in silico based on structure–activity relationship analysis using ML methods provide information about various safety characteristics and allow developers to select the most promising candidates for further in-depth preclinical and clinical studies.

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Natural sulphur‐containing steroids: Origin and biological activities

Dembitsky

Savidov

Gloriozova

2018

Vietnam Journal of Chemistry

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The present review describes the distribution and biological activities of natural sulphur containing steroids (SCS). The structures and predicted biological activities of SCS are presented in the proposed review. Comparatively a small amount of SCS were found in living organisms, but a very large variety of these lipid molecules were found in geological samples and marine sediments. About fifty steroids showed antineoplastic and antipsiomatic activities with a confidence of 72 to 94 %, and some of them can be used as antidiuretic, antiseborrheic, and anesthetic agents. The presented materials are interesting both for chemists and pharmacologists, as well as specialists from the field of sports medicine, bodybuilding, and pharmaceutical industry.

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Web Resources for Discovery and Development of New Medicines

Cited by 18 publications

References 52 publications

Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning Approaches

Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning Approaches

In Silico Estimation of the Safety of Pharmacologically Active Substances Using Machine Learning Methods: A Review

Natural sulphur‐containing steroids: Origin and biological activities

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