MetaPASS: A Web Application for Analyzing the Biological Activity Spectrum of Organic Compounds Taking into Account their Biotransformation

Rudik, Anastasia V.; Дмитриев, А. В.; Lagunin, Alexey; Filimonov, Dmitry; Poroikov, Vladimir

doi:10.1002/minf.202000231

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…The aim of this discussion will be to highlight emerging platforms that utilize machine learning-based methods, with a special emphasis placed on applications that are free to the user and available through a public webserver. A complete overview of platforms available through a fee-based licensing model, as well as older free prediction methods and methods for analyzing CYP molecular dynamics, are reviewed elsewhere [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

“…In particular, the design of drugs devoid of major cytochrome inhibition would reduce the burden of drug–drug interactions in patients where polypharmacy is unavoidable. Several reviews exist that focus on platforms that utilize a diverse range of prediction methods [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Many of these reviews focus on outdated tools, as well as platforms that are exclusively available through a fee-based licensing model.…”

Section: Introductionmentioning

confidence: 99%

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Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

Beck

Morningstar

et al. 2021

Pharmaceuticals

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Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug–drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

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Section: Machine Learning-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

Beck

Morningstar

et al. 2021

Pharmaceuticals

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“…MetaTox 46 прогнозирует токсичность лекарственно-подобных химических соединений с учетом их метаболизма [34,35]. MetaPASS 47 прогнозирует профили биологической активности исходной фармацевтической субстанции и ее метаболитов [36]. BBB permeability 48 прогнозирует проникновение лекарственно-подобных химических соединений через гематоэнцефалический барьер [37].…”

Section: прогнозирование метаболизма лекарственно-подобных химических...unclassified

“…BBB permeability 48 прогнозирует проникновение лекарственно-подобных химических соединений через гематоэнцефалический барьер [37]. 36 https://way2drug.com/hERG/ 37 https://www.way2drug.com/ROSC/ 38 https://www.way2drug.com/gusar/acutoxpredict.html/ 39 Реализована также локальная версия программы, которая прогнозирует острую токсичность для мышей при четырех путях введения фармакологически активного вещества. 40 [34,35] и биологической активности (MetaPASS) веществ c учетом их метаболизма [36].…”

Section: прогнозирование метаболизма лекарственно-подобных химических...unclassified

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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Assessment of the Xenobiotics Toxicity Taking into Account Their Metabolism

Filimonov

Дмитриев

Rudik

et al. 2023

Machine Learning and Deep Learning in Computational Toxicology

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MetaPASS: A Web Application for Analyzing the Biological Activity Spectrum of Organic Compounds Taking into Account their Biotransformation

Cited by 5 publications

References 9 publications

Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

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

Assessment of the Xenobiotics Toxicity Taking into Account Their Metabolism

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