Immune Network Technology on the Basis of Random Forest Algorithm for Computer-Aided Drug Design

Samigulina, Galina; Samigulina, Zarina

doi:10.1007/978-3-319-56148-6_4

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…A − 1 Fig. 4 The random forest visually generated a data point decision tree to extract estimations for each sample to determine the best outcomes through voting descriptors to achieve better accuracy than the simulation results for compounds designed for the drug (Samigulina and Zarina 2017).…”

Section: Random Forestmentioning

confidence: 99%

Machine Learning in Drug Discovery: A Review

Dara¹,

Dhamercherla²,

et al. 2021

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This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

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Section: Random Forestmentioning

confidence: 99%

Machine Learning in Drug Discovery: A Review

Dara¹,

Dhamercherla²,

et al. 2021

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“…Random forest algorithms have been used to improve scoring function performance in ligand-protein binding affinity [169]. Random forest approaches have also been used to select molecular descriptors to achieve better accuracy for the compounds designed for drugs used in immune network technology [170]. Multilayer perceptron (MLP) algorithm is another ML approach that has been mainly used to generate compounds automatically for de novo drug design [171].…”

Section: Machine Learning In Drug Design and Drug Discoverymentioning

confidence: 99%

Machine Learning for Antimicrobial Resistance Research and Drug Development

Shankarnarayan¹,

Guthrie²,

Charlebois³

2022

The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions

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Machine learning is a subfield of artificial intelligence which combines sophisticated algorithms and data to develop predictive models with minimal human interference. This chapter focuses on research that trains machine learning models to study antimicrobial resistance and to discover antimicrobial drugs. An emphasis is placed on applying machine learning models to detect drug resistance among bacterial and fungal pathogens. The role of machine learning in antibacterial and antifungal drug discovery and design is explored. Finally, the challenges and prospects of applying machine learning to advance basic research on and treatment of antimicrobial resistance are discussed. Overall, machine learning promises to advance antimicrobial resistance research and to facilitate the development of antibacterial and antifungal drugs.

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“…Математическая модель поведения волков во время охоты имеет следующий вид: Перспективным является иммунносетевой подход, в котором используется механизм молекулярного узнавания [10]. В основе алгоритма лежит понятие формального пептида и определение минимума энергии связи между формальными пептидами ( антиген-антитело) на основе сингулярного разложения матриц.…”

Section: информационная система ведения научных исследований на основunclassified

Information system for molecular design of drug compounds based on a model-based approach

Samigulina¹,

Samigulina²

2019

Bulletin of the National Technical University "KhPI" a Series of "Information and Modeling"

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Статья посвящена разработке информационной системы ведения научных исследований на базе модельно-ориентированного подхода MDA (Model Driven Architecture) и модифицированного алгоритма искусственных иммунных систем (AIS). Предложен модифицированный алгоритм AIS на базе метода оптимизации серых волков (Gray Wolf Optimization, GWO) для решения задачи прогнозирования зависимости " структура-свойство/активность" лекарственных соединений. Осуществлён сравнительный анализ результатов моделирования с использованием пакета прикладных программ Rapid Miner. Ил.: 5. Табл.: 3. Библиогр.: 10 назв. Ключевые слова: молекулярный дизайн, модельно-ориентированный подход (MDA), модифицированный алгоритм искусственных иммунных систем, метод оптимизации серых волков.

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Immune Network Technology on the Basis of Random Forest Algorithm for Computer-Aided Drug Design

Cited by 7 publications

References 27 publications

Machine Learning in Drug Discovery: A Review

Machine Learning in Drug Discovery: A Review

Machine Learning for Antimicrobial Resistance Research and Drug Development

Information system for molecular design of drug compounds based on a model-based approach

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