A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure–activity relationship modeling

Bouhedjar, Khalid; Boukelia, Abdelbasset; Nacereddine, Abdelmalek Khorief; Boucheham, Anouar; Belaidi, Amine; Djerourou, Abdelhafid

doi:10.1111/cbdd.13742

Cited by 9 publications

(3 citation statements)

References 52 publications

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“…For more detailed information about different parameterization techniques, we refer the reader to these papers on molecular fingerprints, 20,32,33 and on DFT-based descriptors. 7,9,11,34…”

Section: Methodsmentioning

confidence: 99%

“…For more detailed information about different parameterization techniques, we refer the reader to these papers on molecular fingerprints, 20,32,33 and on DFT-based descriptors. 7,9,11,34 Machine learning surrogate models Following feature engineering, we wanted to compare different ML models and assess their performance given a predictive task, mapping reaction conditions to yield and make predictions for unseen conditions.…”

Section: Molecular Parameterizationmentioning

confidence: 99%

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The effect of chemical representation on active machine learning towards closed-loop optimization

et al. 2022

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“…For more detailed information about different parameterization techniques, we refer the reader to these papers on molecular fingerprints, 20,32,33 and on DFT-based descriptors. 7,9,11,34…”

Section: Methodsmentioning

confidence: 99%

Section: Molecular Parameterizationmentioning

confidence: 99%

The effect of chemical representation on active machine learning towards closed-loop optimization

et al. 2022

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“…NLP techniques, especially information extraction, are also able to identify the relations between chemical structures and biological activity [195] and further help researchers search for potentially effective chemical compounds, i.e., virtual screening [196], [197], in a huge chemical space. In addition, they are also applied in the prediction of adverse drug reactions, including side effect prediction [198], toxicity prediction [199], [200], and etc., in preclinical research.…”

Section: E Drug Developmentmentioning

confidence: 99%

Natural Language Processing for Smart Healthcare

Zhou¹,

Yang²,

Shi³

et al. 2021

Preprint

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Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language. In this work we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application. We focus on feature extraction and modelling for various NLP tasks encountered in smart healthcare from a technical point of view. In the context of smart healthcare applications employing NLP techniques, the elaboration largely attends to representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss the limitations of current works and identify the directions for future works.

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Big data and machine learning for materials science

et al. 2021

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Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.

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A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure–activity relationship modeling

Cited by 9 publications

References 52 publications

The effect of chemical representation on active machine learning towards closed-loop optimization

The effect of chemical representation on active machine learning towards closed-loop optimization

Natural Language Processing for Smart Healthcare

Big data and machine learning for materials science

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