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Journal articles on the topic 'Chemical entity'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Usié, Anabel, Rui Alves, Francesc Solsona, Miguel Vázquez, and Alfonso Valencia. "CheNER: chemical named entity recognizer." Bioinformatics 30, no. 7 (2013): 1039–40. http://dx.doi.org/10.1093/bioinformatics/btt639.

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2

Grego, Tiago, Catia Pesquita, Hugo P. Bastos, and Francisco M. Couto. "Chemical Entity Recognition and Resolution to ChEBI." ISRN Bioinformatics 2012 (February 15, 2012): 1–9. http://dx.doi.org/10.5402/2012/619427.

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Chemical entities are ubiquitous through the biomedical literature and the development of text-mining systems that can efficiently identify those entities are required. Due to the lack of available corpora and data resources, the community has focused its efforts in the development of gene and protein named entity recognition systems, but with the release of ChEBI and the availability of an annotated corpus, this task can be addressed. We developed a machine-learning-based method for chemical entity recognition and a lexical-similarity-based method for chemical entity resolution and compared t

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3

Fortes, Amanda C., Antonia Amanda C. Almeida, Francisco Jaime B. Mendonça-Júnior, Rivelilson M. Freitas, José Lamartine Soares-Sobrinho, and Monica Felts de La Roca Soares. "Anxiolytic Properties of New Chemical Entity, 5TIO1." Neurochemical Research 38, no. 4 (2013): 726–31. http://dx.doi.org/10.1007/s11064-013-0970-y.

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4

Santosh, P. C., S. N. Pandeya, and Ashish K. Pathak. "ChemInform Abstract: Benzimidazole: A Versatile Chemical Entity." ChemInform 43, no. 14 (2012): no. http://dx.doi.org/10.1002/chin.201214253.

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5

Branch, Sarah K., and Israel Agranat. "“New Drug” Designations for New Therapeutic Entities: New Active Substance, New Chemical Entity, New Biological Entity, New Molecular Entity." Journal of Medicinal Chemistry 57, no. 21 (2014): 8729–65. http://dx.doi.org/10.1021/jm402001w.

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6

Pashankar, Dinesh S., Warren P. Bishop, and Frank A. Mitros. "Chemical Gastropathy: A Distinct Histopathologic Entity in Children." Journal of Pediatric Gastroenterology and Nutrition 35, no. 5 (2002): 653–57. http://dx.doi.org/10.1002/j.1536-4801.2002.tb07923.x.

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ABSTRACTBackgroundChemical gastropathy, also known as chemical or reactive gastritis, is a well‐described histopathologic entity in adults. It is characterized by presence of foveolar hyperplasia, vascular congestion, lamina propria edema, and prominent smooth muscle fibers in the absence of inflammatory cells in the gastric antral mucosa. There are no data in children on this condition.MethodsAll children showing features of chemical gastropathy in antral biopsy specimens were identified from pathology database from 1997 to 2000. Antral biopsy specimens were reviewed to assess the diagnosis o

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7

Bernard, Bruno A. "Adapalene, a New Chemical Entity with Retinoid Activity." Skin Pharmacology and Physiology 6, no. 1 (1993): 61–69. http://dx.doi.org/10.1159/000211165.

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8

Bolt, Hermann M., and Ernst Kiesswetter. "Is multiple chemical sensitivity a clinically defined entity?" Toxicology Letters 128, no. 1-3 (2002): 99–106. http://dx.doi.org/10.1016/s0378-4274(01)00537-9.

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9

Pashankar, Dinesh S., Warren P. Bishop, and Frank A. Mitros. "Chemical Gastropathy: A Distinct Histopathologic Entity in Children." Journal of Pediatric Gastroenterology and Nutrition 35, no. 5 (2002): 653–57. http://dx.doi.org/10.1097/00005176-200211000-00012.

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10

Watanabe, Taiki, Akihiro Tamura, Takashi Ninomiya, Takuya Makino, and Tomoya Iwakura. "Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing." Journal of Natural Language Processing 29, no. 2 (2022): 294–313. http://dx.doi.org/10.5715/jnlp.29.294.

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11

Hema, R., and T. V. Geetha. "Recognition of Chemical Entities using Pattern Matching and Functional Group Classification." International Journal of Intelligent Information Technologies 12, no. 4 (2016): 21–44. http://dx.doi.org/10.4018/ijiit.2016100102.

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The two main challenges in chemical entity recognition are: (i) New chemical compounds are constantly being synthesized infinitely. (ii) High ambiguity in chemical representation in which a chemical entity is being described by different nomenclatures. Therefore, the identification and maintenance of chemical terminologies is a tough task. Since most of the existing text mining methods followed the term-based approaches, the problems of polysemy and synonymy came into the picture. So, a Named Entity Recognition (NER) system based on pattern matching in chemical domain is developed to extract t

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12

von Beeren, Christoph, Sebastian Pohl, and Volker Witte. "On the Use of Adaptive Resemblance Terms in Chemical Ecology." Psyche: A Journal of Entomology 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/635761.

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Many organisms (mimics) show adaptive resemblance to an element of their environment (model) in order to dupe another organism (operator) for their own benefit. We noted that the terms for adaptive resemblance are used inconsistently within chemical ecology and with respect to the usage in general biology. Here we first describe how resemblance terms are used in general biology and then comparatively examine the use in chemical ecology. As a result we suggest the following consistent terminology: “chemical crypsis” occurs when the operator does not detect the mimic as a discrete entity (backgr

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13

Yan, Su, Scott Spangler, and Ying Chen. "Cross Media Entity Extraction and Linkage for Chemical Documents." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1455–60. http://dx.doi.org/10.1609/aaai.v25i1.7832.

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Text and images are two major sources of information in scientific literature. Information from these two media typically reinforce and complement each other, thus simplifying the process for human to extract and comprehend information. However, machines cannot create the links or have the semantic understanding between images and text. We propose to integrate text analysis and image processing techniques to bridge the gap between the two media, and discover knowledge from the combined information sources, which would be otherwise lost by traditional single-media based mining systems. The focu

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14

Rocktäschel, Tim, Michael Weidlich, and Ulf Leser. "ChemSpot: a hybrid system for chemical named entity recognition." Bioinformatics 28, no. 12 (2012): 1633–40. http://dx.doi.org/10.1093/bioinformatics/bts183.

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15

Dai, Hui, Mu Zhu, Guan Yuan, Yaowei Niu, Hongxing Shi, and Boxuan Chen. "Entity Recognition for Chinese Hazardous Chemical Accident Data Based on Rules and a Pre-Trained Model." Applied Sciences 13, no. 1 (2022): 375. http://dx.doi.org/10.3390/app13010375.

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Due to the fragile physicochemical properties of hazardous chemicals, the chances of leakage and explosion during production, transportation, and storage are quite high. In recent years, hazardous chemical accidents have occurred frequently, posing a great threat to people’s lives and property. Hence, it is crucial to analyze hazardous chemical accidents and establish corresponding warning mechanisms and safeguard measures. At present, most hazardous-chemical-accident data exist in text format. However, named entity recognition (NER), as a method to extract useful information from text data, h

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16

Ahemad, Noormahamad Nadaf*1 Vaibhav Gaikwad2 Dhiraj More1 Mustafa Bashir Mujavar1 Pavanraje Dhokate1. "An Advance Drug Delivery Approach: Osmotic Pump." International Journal of Pharmaceutical Sciences 3, no. 3 (2025): 385–97. https://doi.org/10.5281/zenodo.14989518.

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The past few decades were challenging for the pharmaceutical industry to bring the new active pharmaceutical ingredients APIs in market. Due to the multiple challenges the novel drug delivery approach were increased. From the numerous novel drug delivery systems one of the definitive approach is Osmotic drug delivery system. Osmotic drug delivery system is a type of controlled drug release and brings relief from local gastrointestinal or systemic toxicity. The active drug molecule is present in the core compartment of the osmotic pump. Osmotic pump utilized in oral administration and implantat

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17

Yeo, Kee Wei, Jeyasakthy Saniasiaya, and Jeyanthi Kulasegarah. "SPOROTRICHOSIS IN CHILDREN: A FORGOTTEN ENTITY." Journal of Health and Translational Medicine 26, no. 2 (2023): 204–7. http://dx.doi.org/10.22452/jummec.vol26no2.29.

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Sporotrichosis is regarded as the most common subcutaneous fungal mycosis, which occurs following traumatic inoculation of Sporothrix schenckii complex. Albeit rare, sporotrichosis, also known as 'rose gardener's disease', has been reported to involve children whereby active or passive transmission through a vector such as domestic animals has been identified as the main culprit. Herein, we report sporotrichosis in a pair of siblings who presented to the outpatient Otorhinolaryngology clinic in University Malaya Medical Centre with a lower lips skin lesion and submental swelling after kissing

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18

McAfee, Donald A., Jonathan Hadgraft, and Majella E. Lane. "Rotigotine: The first new chemical entity for transdermal drug delivery." European Journal of Pharmaceutics and Biopharmaceutics 88, no. 3 (2014): 586–93. http://dx.doi.org/10.1016/j.ejpb.2014.08.007.

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19

Colburn, Wayne A. "New Chemical Entity Update: Products Approved for Marketing During 1996." Journal of Clinical Pharmacology 37, no. 3 (1997): 258–60. http://dx.doi.org/10.1002/j.1552-4604.1997.tb04789.x.

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20

Baker, Nancy C., Thomas B. Knudsen, and Antony J. Williams. "Abstract Sifter version 8: Focus on the chemical literature." F1000Research 14 (March 4, 2025): 260. https://doi.org/10.12688/f1000research.160617.1.

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Effective research depends on building on the knowledge found in the scientific literature. Designed to streamline literature tasks, the EPA’s Abstract Sifter literature tool, now at version 8, has been continually extended and enhanced since its introduction in 2017[1]. Early enhancements to the tool have primarily focused on core tasks common to all researchers. For example, citation retrieval from PubMed has been made faster and the returned citation threshold increased to 10,000. Features that allow deeper examination of the literature have been introduced as well. A functionality called T

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21

Li, Huimin, Xuanxuan Zhang, and Wei Ma. "Single entity dynamic electrochemistry." SCIENTIA SINICA Chimica 51, no. 3 (2020): 281–93. http://dx.doi.org/10.1360/ssc-2020-0166.

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22

Zhao, Ziwen, Nikolaos Kostopoulos, Sagar Ganguli, and Alina Sekretareva. "Single-Entity Bioelectrochemistry." ECS Meeting Abstracts MA2023-02, no. 52 (2023): 2531. http://dx.doi.org/10.1149/ma2023-02522531mtgabs.

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Interfacing biological catalytic entities, such as whole cells or enzymes, with electrodes, has a wide range of applications, ranging from fundamental studies of the entities themselves to power generation, bioremediation, chemical synthesis, and biosensing. In all cases, the operation of these bioelectrocatalytic systems is based on electron transfer phenomena within protein molecules, whether isolated or residing inside cells or parts of cells, as well as between proteins and electronics. Typically, the electron transfer and catalytic properties of proteins are studied using ensemble averagi

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23

Zucco, Gesualdo M., and Richard L. Doty. "Multiple Chemical Sensitivity." Brain Sciences 12, no. 1 (2021): 46. http://dx.doi.org/10.3390/brainsci12010046.

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Multiple Chemical Sensitivity (MCS), a condition also known as Chemical Sensitivity (CS), Chemical Intolerance (CI), Idiopathic Environmental Illness (IEI) and Toxicant Induced Loss of Tolerance (TILT), is an acquired multifactorial syndrome characterized by a recurrent set of debilitating symptoms. The symptoms of this controversial disorder are reported to be induced by environmental chemicals at doses far below those usually harmful to most persons. They involve a large spectrum of organ systems and typically disappear when the environmental chemicals are removed. However, no clear link has

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24

Biziukova, N. Yu, O. A. Tarasova, A. V. Rudik, D. A. Filimonov, and V. V. Poroikov. "Automatic Recognition of Chemical Entity Mentions in Texts of Scientific Publications." Automatic Documentation and Mathematical Linguistics 54, no. 6 (2020): 306–15. http://dx.doi.org/10.3103/s0005105520060023.

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25

NOGUCHI, Hideyo, and Yoji TOKUMA. "Toxicology Support ADME Studies for Safety Evaluation of New Chemical Entity." Drug Metabolism and Pharmacokinetics 13, no. 2 (1998): 182–89. http://dx.doi.org/10.2133/dmpk.13.182.

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26

Grego, Tiago, and Francisco M. Couto. "Enhancement of Chemical Entity Identification in Text Using Semantic Similarity Validation." PLoS ONE 8, no. 5 (2013): e62984. http://dx.doi.org/10.1371/journal.pone.0062984.

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27

Akkasi, Abbas, Ekrem Varoğlu, and Nazife Dimililer. "ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition." BioMed Research International 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4248026.

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Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilize

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28

Sahoo, Chita Ranjan, Sudhir Kumar Paidesetty, and Rabindra Nath Padhy. "Norharmane as a potential chemical entity for development of anticancer drugs." European Journal of Medicinal Chemistry 162 (January 2019): 752–64. http://dx.doi.org/10.1016/j.ejmech.2018.11.024.

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29

Tao, Dong Ping. "Molecular entity vacancy model." Fluid Phase Equilibria 250, no. 1-2 (2006): 83–92. http://dx.doi.org/10.1016/j.fluid.2006.09.006.

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30

Giannakopoulou, Erofili, Vasiliki Pardali, Efseveia Frakolaki, et al. "Scaffold hybridization strategy towards potent hydroxamate-based inhibitors ofFlaviviridaeviruses andTrypanosomaspecies." MedChemComm 10, no. 6 (2019): 991–1006. http://dx.doi.org/10.1039/c9md00200f.

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31

Salam, Abu, and Gilang Djati Prinantyo. "Optimization of Biobert Model for Medical Entity Recognition Through Bilstm and CNN-Char Integration." INOVTEK Polbeng - Seri Informatika 10, no. 2 (2025): 772–83. https://doi.org/10.35314/bypwas91.

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Biomedical Named Entity Recognition (NER) is essential for extracting structured information from medical texts. However, existing models like BioBERT face challenges when dealing with complex biomedical entities, particularly those with intricate morphological structures. This research enhances the BioBERT model by integrating BiLSTM and character-level CNN (CNN-Char), aiming to improve the recognition of Chemical and Disease entities. The proposed models were trained and evaluated on the BC5CDR dataset sourced from the official BioCreative V CDR Corpus. The modified model achieved an F1-scor

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32

Tiwari, Anuradha, Nand Lal, Kiran Singh, Brijesh Nanvi, and Neelam Pal. "FTIR Characterization and Physico-chemical Studies of Honey Samples in Kanpur Nagar." Journal of Forensic Chemistry and Toxicology 8, no. 2 (2022): 63–69. http://dx.doi.org/10.21088/jfct.2454.9363.8222.6.

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Honey is a valuable food entity that gains a lot of attention due to its potential benefits for health and its uses to sweeten foods. It contains about 60% invert sugar and appreciable amounts of maltose and sucrose. In this study, the Physico-chemical characteristics of pure and adulterated honey samples were investigated. The grade and limitation of adulteration of the honey samples also were checked. The moisture, ash and pH were carried out. The Fourier Transform Infrared Spectroscopy (FTIR) of different samples were analyzed. The result of FTIR spectroscopy provides reliable results.

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33

Yuan, Bo, Chenjia Zhao, Xuyang Sun, and Jing Liu. "Lightweight Liquid Metal Entity." Advanced Functional Materials 30, no. 14 (2020): 1910709. http://dx.doi.org/10.1002/adfm.201910709.

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34

Nastou, Katerina, Mikaela Koutrouli, Sampo Pyysalo, and Lars Juhl Jensen. "Improving dictionary-based named entity recognition with deep learning." Bioinformatics 40, Supplement_2 (2024): ii45—ii52. http://dx.doi.org/10.1093/bioinformatics/btae402.

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Abstract Motivation Dictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized to biomedical databases and ontologies. However, adaptation to different entity types requires new high-quality dictionaries and associated lists of blocked names for each type. The latter are so far created by identifying cases that cause many false positives through manual inspection of individual names, a process that scales poorly. Results In this work, we aim to improve block list s by automatically identifying names to block, based on the context in which they appear

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35

Negi, Sakshi, and Ashutosh Badola. "Role of preclinical pharmacokinetics in New Chemical Entity (NCE) development: A review." Asian Journal of Pharmacy and Pharmacology 4, no. 5 (2018): 563–69. http://dx.doi.org/10.31024/ajpp.2018.4.5.4.

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36

B. McNabb, Carolyn, Bruce R. Russell, Daniele Caprioli, David J. Nutt, Simon Gibbons, and Jeffrey W. Dalley. "Single Chemical Entity Legal Highs: Assessing the Risk for Long Term Harm." Current Drug Abuse Reviews 5, no. 4 (2012): 304–19. http://dx.doi.org/10.2174/1874473711205040005.

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37

Isazawa, Taketomo, and Jacqueline M. Cole. "Single Model for Organic and Inorganic Chemical Named Entity Recognition in ChemDataExtractor." Journal of Chemical Information and Modeling 62, no. 5 (2022): 1207–13. http://dx.doi.org/10.1021/acs.jcim.1c01199.

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38

Akhondi, Saber A., Ewoud Pons, Zubair Afzal, et al. "Chemical entity recognition in patents by combining dictionary-based and statistical approaches." Database 2016 (2016): baw061. http://dx.doi.org/10.1093/database/baw061.

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39

Costa, Salvana Priscylla Manso, Keyla Emanuelle Ramos da Silva, Giovanna Christinne Rocha de Medeiros, et al. "Thermal behavior and compatibility analysis of the new chemical entity LPSF/FZ4." Thermochimica Acta 562 (June 2013): 29–34. http://dx.doi.org/10.1016/j.tca.2013.03.003.

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40

Henson, Alon, Juan Manuel Parrilla Gutierrez, Trevor Hinkley, Soichiro Tsuda, and Leroy Cronin. "Towards heterotic computing with droplets in a fully automated droplet-maker platform." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2046 (2015): 20140221. http://dx.doi.org/10.1098/rsta.2014.0221.

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The control and prediction of complex chemical systems is a difficult problem due to the nature of the interactions, transformations and processes occurring. From self-assembly to catalysis and self-organization, complex chemical systems are often heterogeneous mixtures that at the most extreme exhibit system-level functions, such as those that could be observed in a living cell. In this paper, we outline an approach to understand and explore complex chemical systems using an automated droplet maker to control the composition, size and position of the droplets in a predefined chemical environm

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41

Saxena, Suvinay, Taruna Yadav, Satya Jha, and Pushpinder Singh Khera. "Ribbing disease- radiograph to the rescue in an overlooked cause of painful shin." Indian Journal of Musculoskeletal Radiology 3 (December 20, 2021): 111–16. http://dx.doi.org/10.25259/ijmsr_64_2020.

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Ribbing disease or multiple diaphyseal sclerosis is a rare clinical entity, which is often confused with many other differential diagnoses when it primarily involves the tibia and fibula. An accurate diagnosis alleviates the need for tissue diagnosis and aids in better clinical management and outcome of the patient. The case report highlights the classical imaging findings, appearance on cross-sectional imaging, and the admirable role of radiographs in diagnosing the entity.

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42

Makarov, D. А., N. P. Mayurov, and E. A. Inshakova. "Grounds for administrative liability of legal entities in Russia: Selected issues of public law sciences." Sociology and Law 15, no. 1 (2023): 122–30. http://dx.doi.org/10.35854/2219-6242-2023-1-122-130.

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The paper examines the problems of administrative liability of legal entities in the Russian Federation. The issues related to the administrative liability of legal entities of public law were considered. The correlation between the administrative liability of a legal entity and its officials was found. Aspects of the form of guilt of a legal entity and its correspondence to the forms of guilt of its officials were studied.

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43

EMBER, LOIS. "Private entity should run space station R&D." Chemical & Engineering News 78, no. 3 (2000): 14. http://dx.doi.org/10.1021/cen-v078n003.p014.

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44

Krupkin, Miri, Donna Matzov, Hua Tang, et al. "A vestige of a prebiotic bonding machine is functioning within the contemporary ribosome." Philosophical Transactions of the Royal Society B: Biological Sciences 366, no. 1580 (2011): 2972–78. http://dx.doi.org/10.1098/rstb.2011.0146.

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Based on the presumed capability of a prebiotic pocket-like entity to accommodate substrates whose stereochemistry enables the creation of chemical bonds, it is suggested that a universal symmetrical region identified within all contemporary ribosomes originated from an entity that we term the ‘proto-ribosome’. This ‘proto-ribosome’ could have evolved from an earlier machine that was capable of performing essential tasks in the RNA world, called here the ‘pre-proto-ribosome’, which was adapted for producing proteins.

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45

Ranganathan, S. "The entity model of metallic solutions." Calphad 15, no. 2 (1991): 121–30. http://dx.doi.org/10.1016/0364-5916(91)90011-8.

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46

Sethi, Nikunj, Rohit Mahar, Sanjeev K. Shukla, Akhilesh Kumar, and Neeraj Sinha. "A novel approach for testing the teratogenic potential of chemicals on the platform of metabolomics: studies employing HR-MAS nuclear magnetic resonance spectroscopy." RSC Advances 5, no. 33 (2015): 26027–39. http://dx.doi.org/10.1039/c5ra00671f.

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The objective is to develop a quick, reliable method for testing the teratogenic potential of a new chemical entity (NCE) on the platform of metabonomics, as an alternative to conventional procedures.

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47

Zhang, Yaoyun, Jun Xu, Hui Chen, et al. "Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning." Database 2016 (2016): baw049. http://dx.doi.org/10.1093/database/baw049.

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48

Maclnnes, Rona, Cynthia E. Lumley, and Stuart R. Walker. "New chemical entity output of the international pharmaceutical industry from 1970 to 1992." Clinical Pharmacology & Therapeutics 56, no. 3 (1994): 339–49. http://dx.doi.org/10.1038/clpt.1994.145.

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49

Luo, Ling, Zhihao Yang, Pei Yang, et al. "An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition." Bioinformatics 34, no. 8 (2017): 1381–88. http://dx.doi.org/10.1093/bioinformatics/btx761.

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50

Mori, Kiyotoshi, Kotaro Takasaki, Yoshimitsu Katoh, et al. "Glucose concentration-dependent potentiation of insulin secretion by a new chemical entity, KCP256." European Journal of Pharmacology 528, no. 1-3 (2005): 176–82. http://dx.doi.org/10.1016/j.ejphar.2005.10.033.

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