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Journal articles on the topic 'Cheminformatics database analysis'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Tiwari, Amit Kumar, Dipika Jaspal, Shradha Deshmukh, and Preeti Mulay. "Cheminformatics: A Patentometric Analysis." French-Ukrainian Journal of Chemistry 10, no. 1 (2022): 13–29. http://dx.doi.org/10.17721/fujcv10i1p13-29.

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Cheminformatics has entrenched itself as a core discipline within chemistry, biology, and allied sciences, more particularly in the field of Drug Design Discovery and Development. The article begins with a patent analysis of the progressing field of cheminformatics from 1996 to early 2021 using the Relecura and Lens patent database. It proceeds with a description of patents in various domains and aspects. The eye-catching mind map shows the landscape of cheminformatics patent search. The results reveal the star rating-wise patent counts and the trends in the sub-technological research areas. A

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Vivek-Ananth, R. P., Ajaya Kumar Sahoo, Kavyaa Kumaravel, Karthikeyan Mohanraj, and Areejit Samal. "MeFSAT: a curated natural product database specific to secondary metabolites of medicinal fungi." RSC Advances 11, no. 5 (2021): 2596–607. http://dx.doi.org/10.1039/d0ra10322e.

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Xue, Mengzhu, Shoude Zhang, Chaoqian Cai, et al. "Predicting the Drug Safety for Traditional Chinese Medicine through a Comparative Analysis of Withdrawn Drugs Using Pharmacological Network." Evidence-Based Complementary and Alternative Medicine 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/256782.

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As the major issue to limit the use of drugs, drug safety leads to the attrition or failure in clinical trials of drugs. Therefore, it would be more efficient to minimize therapeutic risks if it could be predicted before large-scale clinical trials. Here, we integrated a network topology analysis with cheminformatics measurements on drug information from the DrugBank database to detect the discrepancies between approved drugs and withdrawn drugs and give drug safety indications. Thus, 47 approved drugs were unfolded with higher similarity measurements to withdrawn ones by the same target and c

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Williams, Tova N., Melaine A. Kuenemann, George A. Van Den Driessche, Antony J. Williams, Denis Fourches, and Harold S. Freeman. "Toward the Rational Design of Sustainable Hair Dyes Using Cheminformatics Approaches: Step 1. Database Development and Analysis." ACS Sustainable Chemistry & Engineering 6, no. 2 (2018): 2344–52. http://dx.doi.org/10.1021/acssuschemeng.7b03795.

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Yu, Tianshi, Tianyang Huang, Leiye Yu, et al. "Exploring the Chemical Space of CYP17A1 Inhibitors Using Cheminformatics and Machine Learning." Molecules 28, no. 4 (2023): 1679. http://dx.doi.org/10.3390/molecules28041679.

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Cytochrome P450 17A1 (CYP17A1) is one of the key enzymes in steroidogenesis that produces dehydroepiandrosterone (DHEA) from cholesterol. Abnormal DHEA production may lead to the progression of severe diseases, such as prostatic and breast cancers. Thus, CYP17A1 is a druggable target for anti-cancer molecule development. In this study, cheminformatic analyses and quantitative structure–activity relationship (QSAR) modeling were applied on a set of 962 CYP17A1 inhibitors (i.e., consisting of 279 steroidal and 683 nonsteroidal inhibitors) compiled from the ChEMBL database. For steroidal inhibito

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Liang, Jihao, Yang Zheng, Xin Tong, Naixue Yang, and Shaoxing Dai. "In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning." Molecules 28, no. 1 (2022): 208. http://dx.doi.org/10.3390/molecules28010208.

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of COVID-19, is spreading rapidly and has caused hundreds of millions of infections and millions of deaths worldwide. Due to the lack of specific vaccines and effective treatments for COVID-19, there is an urgent need to identify effective drugs. Traditional Chinese medicine (TCM) is a valuable resource for identifying novel anti-SARS-CoV-2 drugs based on the important contribution of TCM and its potential benefits in COVID-19 treatment. Herein, we aimed to discover novel anti-SARS-CoV-2 compounds and medicina

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Ebejer, Jean-Paul, Michael H. Charlton, and Paul W. Finn. "Are the physicochemical properties of antibacterial compounds really different from other drugs?" Journal of Cheminformatics 8, no. 1 (2016): 30. https://doi.org/10.1186/s13321-016-0143-5.

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<strong>Background: </strong>It is now widely recognized that there is an urgent need for new antibacterial drugs, with novel mechanisms of action, to combat the rise of multi-drug resistant bacteria. However, few new compounds are reaching the market. Antibacterial drug discovery projects often succeed in identifying potent molecules in biochemical assays but have been beset by difficulties in obtaining antibacterial activity. A commonly held view, based on analysis of marketed antibacterial compounds, is that antibacterial drugs possess very different physicochemical properties to other drug

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Li, Yuze, Zhe Wang, Shengyao Ma, Xiaowen Tang, and Hanting Zhang. "Chemical Space Exploration and Machine Learning-Based Screening of PDE7A Inhibitors." Pharmaceuticals 18, no. 4 (2025): 444. https://doi.org/10.3390/ph18040444.

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Background/Objectives: Phosphodiesterase 7 (PDE7), a member of the PDE superfamily, selectively catalyzes the hydrolysis of cyclic adenosine 3′,5′-monophosphate (cAMP), thereby regulating the intracellular levels of this second messenger and influencing various physiological functions and processes. There are two subtypes of PDE7, PDE7A and PDE7B, which are encoded by distinct genes. PDE7 inhibitors have been shown to exert therapeutic effects on neurological and respiratory diseases. However, FDA-approved drugs based on the PDE7A inhibitor are still absent, highlighting the need for novel com

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Srivastava, Neha, Bhartendu Nath Mishra, and Prachi Srivastava. "In-Silico Identification of Drug Lead Molecule Against Pesticide Exposed-neurodevelopmental Disorders Through Network-Based Computational Model Approach." Current Bioinformatics 14, no. 5 (2019): 460–67. http://dx.doi.org/10.2174/1574893613666181112130346.

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Background: Neurodevelopmental Disorders (NDDs) are impairment of the growth and development of the brain or central nervous system, which occurs at the developmental stage. This can include developmental brain dysfunction, which can manifest as neuropsychiatric problems or impaired motor function, learning, language or non-verbal communication. These include the array of disorder, including: Autism Spectrum Disorders (ASD), Attention Deficit Hyperactivity Disorders (ADHD) etc. There is no particular diagnosis and cure for NDDs. These disorders seem to be result from a combination of genetic,

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Chitsazi, Rezvan, Phillip W. Gingrich, James P. Long, Chong Wu, and Bissan Al-Lazikani. "Abstract 4460: OpencanSARchem: chemistry registration and standardization pipeline for FAIR integration of bioassay data." Cancer Research 85, no. 8_Supplement_1 (2025): 4460. https://doi.org/10.1158/1538-7445.am2025-4460.

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Abstract Introduction: The rapid growth of medicinal chemistry and bioactivity data has driven AI advancements but inconsistencies in chemical representation across databases make bioactivity-focused data integration challenging. Tautomerism, a key challenge, complicates chemical registration due to the existence of multiple interchangeable molecular forms, significantly impacting database uniqueness and separating bioactivity data across tautomeric forms. We previously developed a standalone, fully deployable solution to address these challenges (Dolciami et al. 2022), now a core component of

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Stratton, Chase A., Yvonne Thompson, Konilo Zio, William R. Morrison, and Ebony G. Murrell. "uafR: An R package that automates mass spectrometry data processing." PLOS ONE 19, no. 7 (2024): e0306202. http://dx.doi.org/10.1371/journal.pone.0306202.

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Chemical information has become increasingly ubiquitous and has outstripped the pace of analysis and interpretation. We have developed an R package, uafR, that automates a grueling retrieval process for gas -chromatography coupled mass spectrometry (GC -MS) data and allows anyone interested in chemical comparisons to quickly perform advanced structural similarity matches. Our streamlined cheminformatics workflows allow anyone with basic experience in R to pull out component areas for tentative compound identifications using the best published understanding of molecules across samples (pubchem.

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Anwar, Muhammad Faraz, Ramsha Khalid, Alina Hasanain, et al. "Integrated Cheminformatics-Molecular Docking Approach to Drug Discovery Against Viruses." Infectious Disorders - Drug Targets 20, no. 2 (2020): 150–59. http://dx.doi.org/10.2174/1871526518666181019162359.

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Background: In the current study, we present an integrated in silico cheminformaticsmolecular docking approach to screen and test potential therapeutic compounds against viruses. Fluoroquinolones have been shown to inhibit HCV replication by targeting HCV NS3-helicase. Based on this observation, we hypothesized that natural analogs of fluoroquinolones will have similar or superior inhibitory potential while having potentially fewer adverse effects. Methods: To screen for natural analogs of fluoroquinolones, we devised an integrated in silico Cheminformatics-Molecular Docking approach. We used

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Loging, William, Raul Rodriguez-Esteban, Jon Hill, Tom Freeman, and John Miglietta. "Cheminformatic/bioinformatic analysis of large corporate databases: Application to drug repurposing." Drug Discovery Today: Therapeutic Strategies 8, no. 3-4 (2011): 109–16. http://dx.doi.org/10.1016/j.ddstr.2011.06.004.

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Medina-Franco, José L., and Fernanda I. Saldívar-González. "Cheminformatics to Characterize Pharmacologically Active Natural Products." Biomolecules 10, no. 11 (2020): 1566. http://dx.doi.org/10.3390/biom10111566.

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Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-base

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Kralj, Sebastjan, Marko Jukič, and Urban Bren. "Comparative Analyses of Medicinal Chemistry and Cheminformatics Filters with Accessible Implementation in Konstanz Information Miner (KNIME)." International Journal of Molecular Sciences 23, no. 10 (2022): 5727. http://dx.doi.org/10.3390/ijms23105727.

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High-throughput virtual screening (HTVS) is, in conjunction with rapid advances in computer hardware, becoming a staple in drug design research campaigns and cheminformatics. In this context, virtual compound library design becomes crucial as it generally constitutes the first step where quality filtered databases are essential for the efficient downstream research. Therefore, multiple filters for compound library design were devised and reported in the scientific literature. We collected the most common filters in medicinal chemistry (PAINS, REOS, Aggregators, van de Waterbeemd, Oprea, Ficher

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Karaman, Berin, and Wolfgang Sippl. "Computational Drug Repurposing: Current Trends." Current Medicinal Chemistry 26, no. 28 (2019): 5389–409. http://dx.doi.org/10.2174/0929867325666180530100332.

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: Biomedical discovery has been reshaped upon the exploding digitization of data which can be retrieved from a number of sources, ranging from clinical pharmacology to cheminformatics-driven databases. Now, supercomputing platforms and publicly available resources such as biological, physicochemical, and clinical data, can all be integrated to construct a detailed map of signaling pathways and drug mechanisms of action in relation to drug candidates. Recent advancements in computer-aided data mining have facilitated analyses of ‘big data’ approaches and the discovery of new indications for pre

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Afantitis, Antreas, Andreas Tsoumanis, and Georgia Melagraki. "Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME." Current Medicinal Chemistry 27, no. 38 (2020): 6523–35. http://dx.doi.org/10.2174/0929867327666200727114410.

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Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminforma

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Gago, Federico. "Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs." Marine Drugs 21, no. 2 (2023): 100. http://dx.doi.org/10.3390/md21020100.

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The exploration of biologically relevant chemical space for the discovery of small bioactive molecules present in marine organisms has led not only to important advances in certain therapeutic areas, but also to a better understanding of many life processes. The still largely untapped reservoir of countless metabolites that play biological roles in marine invertebrates and microorganisms opens new avenues and poses new challenges for research. Computational technologies provide the means to (i) organize chemical and biological information in easily searchable and hyperlinked databases and know

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Buntin, Kathrin, Peter Ertl, Dominic Hoepfner, et al. "Deliberations on Natural Products and Future Directions in the Pharmaceutical Industry." CHIMIA International Journal for Chemistry 75, no. 7 (2021): 620–33. http://dx.doi.org/10.2533/chimia.2021.620.

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Natural Products (NPs) are molecular' special equipment ' that impart survival benefits on their producers in nature. Due to their evolved functions to modulate biology these privileged metabolites are substantially represented in the drug market and are continuing to contribute to the discovery of innovative medicines such as the recently approved semi-synthetic derivative of the bacterial alkaloid staurosporin in oncology indications. The innovation of low molecular weight compounds in modern drug discovery is built on rapid progress in chemical, molecular biological, pharmacological and dat

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Pouryahya, Maryam, Jung Hun Oh, James C. Mathews, et al. "Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods." International Journal of Molecular Sciences 23, no. 3 (2022): 1074. http://dx.doi.org/10.3390/ijms23031074.

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The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity

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Pandey, Vijay, Baocheng Wang, Chakrabhavi Dhananjaya Mohan, et al. "Discovery of a small-molecule inhibitor of specific serine residue BAD phosphorylation." Proceedings of the National Academy of Sciences 115, no. 44 (2018): E10505—E10514. http://dx.doi.org/10.1073/pnas.1804897115.

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Human BCL-2–associated death promoter (hBAD) is an apoptosis-regulatory protein mediating survival signals to carcinoma cells upon phosphorylation of Ser99, among other residues. Herein, we screened multiple small-molecule databases queried in a Laplacian-modified naive Bayesian-based cheminformatics platform and identified a Petasis reaction product as a site-specific inhibitor for hBAD phosphorylation. Based on apoptotic efficacy against mammary carcinoma cells, N-cyclopentyl-3-((4-(2,3-dichlorophenyl) piperazin-1-yl) (2-hydroxyphenyl) methyl) benzamide (NPB) was identified as a potential le

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Elmassry, Moamen M., Sunghwan Kim та Ben Busby. "Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes". PLOS ONE 16, № 1 (2021): e0244876. http://dx.doi.org/10.1371/journal.pone.0244876.

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Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level

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Yang, Zhirui, Mingquan Wu, Xin Zhou, Jin Luo, Yi Liu, and Lin Li. "Network pharmacology study on the mechanism of Curcumae Rhizoma in the treatment of non-small cell lung cancer." Medicine 104, no. 19 (2025): e42366. https://doi.org/10.1097/md.0000000000042366.

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Non-small cell lung cancer (NSCLC) poses a significant threat to public health worldwide. Curcumae Rhizoma (CR) has potent therapeutic potential in different cancers. However, the mechanism of CR treating NSCLC remains unclear. In this study, a network pharmacology-based strategy is followed to address the issue. The targets related to CR or NSCLC were obtained from multiple online public databases. Compound-target network was constructed using Cytoscape. Protein–protein interaction (PPI) was analyzed by STRING. Key transcription factors were explored in TRRUST. Gene ontology (GO) function and

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Yasir, Muhammad, Jinyoung Park, Eun-Taek Han, Won Sun Park, Jin-Hee Han, and Wanjoo Chun. "Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation." Molecules 29, no. 6 (2024): 1363. http://dx.doi.org/10.3390/molecules29061363.

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The increasing utilization of artificial intelligence algorithms in drug development has proven to be highly efficient and effective. One area where deep learning-based approaches have made significant contributions is in drug repositioning, enabling the identification of new therapeutic applications for existing drugs. In the present study, a trained deep-learning model was employed to screen a library of FDA-approved drugs to discover novel inhibitors targeting JAK2. To accomplish this, reference datasets containing active and decoy compounds specific to JAK2 were obtained from the DUD-E dat

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O'Hagan, Steve, and Douglas Bruce Kell. "Consensus rank orderings of molecular fingerprints illustrate the most genuine similarities between marketed drugs and small endogenous human metabolites, but highlight exogenous natural products as the most important ‘natural’ drug transporter substrates." ADMET and DMPK 5, no. 2 (2017): 85. http://dx.doi.org/10.5599/admet.5.2.376.

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<p class="ADMETabstracttext">We compare several molecular fingerprint encodings for marketed, small molecule drugs, and assess how their <span style="text-decoration: underline;">rank order</span> varies with the fingerprint in terms of the Tanimoto similarity to the most similar endogenous human metabolite as taken from Recon2. For the great majority of drugs, the rank order varies <span style="text-decoration: underline;">very greatly</span> depending on the encoding used, and also somewhat when the Tanimoto similarity (TS) is replaced by the Tversky similarity.

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Hew, Khai‐Lin, Chze‐Yin Tan, and Yeun‐Mun Choo. "The Malaysian Natural Product Database: A Structure Repository of Malaysia's Natural Compounds." Chemistry & Biodiversity, June 16, 2025. https://doi.org/10.1002/cbdv.202501026.

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ABSTRACTThe Malaysian Natural Product (MyNP) Database is a specialized resource designed to support natural product research, drug discovery, and cheminformatics. Developed through extensive data collection from SciFinder searches and manual curation of journal publications, MyNP comprises 1999 unique natural product structures. The database features a detailed classification system, with alkaloids (32%), sesquiterpenoids (10%), and flavonoids (8%) representing the most prominent chemical classes. It also includes key molecular descriptors such as two‐dimensional structures, CAS numbers, IUPAC

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Gonzalez-Ponce, Karla, Carolina Horta Andrade, Fiona Hunter, et al. "School of cheminformatics in Latin America." Journal of Cheminformatics 15, no. 1 (2023). http://dx.doi.org/10.1186/s13321-023-00758-0.

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AbstractWe report the major highlights of the School of Cheminformatics in Latin America, Mexico City, November 24–25, 2022. Six lectures, one workshop, and one roundtable with four editors were presented during an online public event with speakers from academia, big pharma, and public research institutions. One thousand one hundred eighty-one students and academics from seventy-nine countries registered for the meeting. As part of the meeting, advances in enumeration and visualization of chemical space, applications in natural product-based drug discovery, drug discovery for neglected disease

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Plehiers, Pieter P., Guy B. Marin, Christian V. Stevens, and Kevin M. Van Geem. "Automated reaction database and reaction network analysis: extraction of reaction templates using cheminformatics." Journal of Cheminformatics 10, no. 1 (2018). http://dx.doi.org/10.1186/s13321-018-0269-8.

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Willett, Peter. "Commentary: the first twelve years of the Journal of chemoinformatics." Journal of Cheminformatics 14, no. 1 (2022). http://dx.doi.org/10.1186/s13321-022-00617-4.

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AbstractThis commentary provides an overview of the publications in, and the citations to, the first twelve volumes of the Journal of Cheminformatics, covering the period 2009–2020. The analysis is based on the 622 articles that have appeared in the journal during that time and that have been indexed in the Clarivate Web of Science Core Collection database. It is clear that the journal has established itself as one of the most important publications in the field of cheminformatics: it attracts citations not only from other journals in its specialist field but also from biological and chemical

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Xie, Liwei, Jingwei Zhou, Ziying Lin, Shengjun Wang, Zhihong Liu, and Bingdong Liu. "Exploring Anti-osteoporosis Medicinal Herbs using Cheminformatics and Deep Learning Approaches." Combinatorial Chemistry & High Throughput Screening 25 (September 5, 2022). http://dx.doi.org/10.2174/1386207325666220905155923.

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Background: Osteoporosis is a prevalent disease for the aged population. Chinese herb-derived natural compounds have anti-osteoporosis effects. Due to the complexity of chemical ingredients and natural products, it is necessary to develop a high-throughput approach with the integration of cheminformatics and deep-learning methods to explore their mechanistic action, especially herb/drug-gene interaction networks. Methods: Ten medicinal herbs for clinical osteoporosis treatment were selected. Chemical ingredients of top 10 herbs were retrieved from TCMIO database, and their predicted targets we

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Leonis, G., and G. Melagraki. "Open Source cheminformatics software including KNIME analytics." April 1, 2022. https://doi.org/10.1007/978-3-319-27282-5_57.

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In this chapter, we present a brief description of compound datasets and programs developed to serve chemoinformatics as well as, more specifically, nanoinformatics purposes. Emphasis has been placed on publicly available tools and particularly on KNIME (Konstanz Information Miner), the most widely used freely available platform for data processing and analysis. Among a multitude of studies that have demonstrated the usefulness of chemoinformatics tools to chemical and medicinal applications, herein we present indicative cases of five successful KNIME-based approaches. The first two studi

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Mak, Lora, David Marcus, Andrew Howlett, et al. "Metrabase: a cheminformatics and bioinformatics database for small molecule transporter data analysis and (Q)SAR modeling." Journal of Cheminformatics 7, no. 1 (2015). http://dx.doi.org/10.1186/s13321-015-0083-5.

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Feng, Liya, Sha Zhu, Jian Ma, et al. "Small molecule drug discovery for glioblastoma treatment based on bioinformatics and cheminformatics approaches." Frontiers in Pharmacology 15 (April 12, 2024). http://dx.doi.org/10.3389/fphar.2024.1389440.

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Background: Glioblastoma (GBM) is a common and highly aggressive brain tumor with a poor prognosis for patients. It is urgently needed to identify potential small molecule drugs that specifically target key genes associated with GBM development and prognosis.Methods: Differentially expressed genes (DEGs) between GBM and normal tissues were obtained by data mining the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Gene function annotation was performed to investigate the potential functions of the DEGs. A protein-protein interaction (PPI) network was constructed to

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Talukder, Md Enamul Kabir, Md Aktaruzzaman, Noimul Hasan Siddiquee, et al. "Cheminformatics-based identification of phosphorylated RET tyrosine kinase inhibitors for human cancer." Frontiers in Chemistry 12 (July 17, 2024). http://dx.doi.org/10.3389/fchem.2024.1407331.

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BackgroundRearranged during transfection (RET), an oncogenic protein, is associated with various cancers, including non-small-cell lung cancer (NSCLC), papillary thyroid cancer (PTC), pancreatic cancer, medullary thyroid cancer (MTC), breast cancer, and colorectal cancer. Dysregulation of RET contributes to cancer development, highlighting the importance of identifying lead compounds targeting this protein due to its pivotal role in cancer progression. Therefore, this study aims to discover effective lead compounds targeting RET across different cancer types and evaluate their potential to inh

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Ahmad, Fadel Klaib, Zainol Zurinahni, Hashimah Ahamed Nurul, Ahmad Rosma, and Hussin Wahidah. "Application of Exact String Matching Algorithms towards SMILES Representation of Chemical Structure." October 27, 2007. https://doi.org/10.5281/zenodo.1078645.

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Bioinformatics and Cheminformatics use computer as disciplines providing tools for acquisition, storage, processing, analysis, integrate data and for the development of potential applications of biological and chemical data. A chemical database is one of the databases that exclusively designed to store chemical information. NMRShiftDB is one of the main databases that used to represent the chemical structures in 2D or 3D structures. SMILES format is one of many ways to write a chemical structure in a linear format. In this study we extracted Antimicrobial Structures in SMILES format from NMRSh

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Banerjee, Arkaprava, Kunal Roy, and Paola Gramatica. "A bibliometric analysis of the Cheminformatics/QSAR literature (2000–2023) for predictive modeling in data science using the SCOPUS database." Molecular Diversity, December 5, 2024. https://doi.org/10.1007/s11030-024-11056-8.

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Schaub, Jonas, Julian Zander, Achim Zielesny, and Christoph Steinbeck. "Scaffold Generator: a Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK)." Journal of Cheminformatics 14, no. 1 (2022). http://dx.doi.org/10.1186/s13321-022-00656-x.

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AbstractThe concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For displa

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Dai, Shao-Xing, Wen-Xing Li, Fei-Fei Han, et al. "In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database." Scientific Reports 6, no. 1 (2016). http://dx.doi.org/10.1038/srep25462.

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Abstract There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on t

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Roell, Kyle, Lauren E. Koval, Rebecca Boyles, et al. "Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research." Frontiers in Toxicology 4 (June 22, 2022). http://dx.doi.org/10.3389/ftox.2022.893924.

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Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And

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Agea, M. Isabel, Ivan Čmelo, Wim Dehaen, et al. "Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library." Molecular Informatics, July 9, 2024. http://dx.doi.org/10.1002/minf.202300316.

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AbstractComputational exploration of chemical space is crucial in modern cheminformatics research for accelerating the discovery of new biologically active compounds. In this study, we present a detailed analysis of the chemical library of potential glucocorticoid receptor (GR) ligands generated by the molecular generator, Molpher. To generate the targeted GR library and construct the classification models, structures from the ChEMBL database as well as from the internal IMG library, which was experimentally screened for biological activity in the primary luciferase reporter cell assay, were u

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Boldini, Davide, Davide Ballabio, Viviana Consonni, Roberto Todeschini, Francesca Grisoni, and Stephan A. Sieber. "Effectiveness of molecular fingerprints for exploring the chemical space of natural products." Journal of Cheminformatics 16, no. 1 (2024). http://dx.doi.org/10.1186/s13321-024-00830-3.

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AbstractNatural products are a diverse class of compounds with promising biological properties, such as high potency and excellent selectivity. However, they have different structural motifs than typical drug-like compounds, e.g., a wider range of molecular weight, multiple stereocenters and higher fraction of sp3-hybridized carbons. This makes the encoding of natural products via molecular fingerprints difficult, thus restricting their use in cheminformatics studies. To tackle this issue, we explored over 30 years of research to systematically evaluate which molecular fingerprint provides the

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Wei, Bin, Gang-Ao Hu, Zhen-Yi Zhou, et al. "Global analysis of the biosynthetic chemical space of marine prokaryotes." Microbiome 11, no. 1 (2023). http://dx.doi.org/10.1186/s40168-023-01573-3.

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Abstract Background Marine prokaryotes are a rich source of novel bioactive secondary metabolites for drug discovery. Recent genome mining studies have revealed their great potential to bio-synthesize novel secondary metabolites. However, the exact biosynthetic chemical space encoded by the marine prokaryotes has yet to be systematically evaluated. Results We first investigated the secondary metabolic potential of marine prokaryotes by analyzing the diversity and novelty of the biosynthetic gene clusters (BGCs) in 7541 prokaryotic genomes from cultivated and single cells, along with 26,363 new

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Terlouw, Barbara R., Sophie P. J. M. Vromans, and Marnix H. Medema. "PIKAChU: a Python-based informatics kit for analysing chemical units." Journal of Cheminformatics 14, no. 1 (2022). http://dx.doi.org/10.1186/s13321-022-00616-5.

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AbstractAs efforts to computationally describe and simulate the biochemical world become more commonplace, computer programs that are capable of in silico chemistry play an increasingly important role in biochemical research. While such programs exist, they are often dependency-heavy, difficult to navigate, or not written in Python, the programming language of choice for bioinformaticians. Here, we introduce PIKAChU (Python-based Informatics Kit for Analysing CHemical Units): a cheminformatics toolbox with few dependencies implemented in Python. PIKAChU builds comprehensive molecular graphs fr

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Neidiger, Charlotte, Tarek Saier, Kai Kühn, et al. "Implementation of an open chemistry knowledge base with a Semantic Wiki." Journal of Cheminformatics 17, no. 1 (2025). https://doi.org/10.1186/s13321-025-01037-w.

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Abstract In this work, a concept for an open chemistry knowledge base was developed to integrate chemical research results into a collaboratively usable platform. To achieve this, we enhanced Semantic MediaWiki (SMW) to support the collection and structured summary of chemical data contained in publications. We implemented tools for capturing chemical structures in machine-readable formats and designed data forms along with a data model to ensure standardized input and organization of research results. These enhancements allow for effective data comparison and contextual analysis within an exp

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Moreira-Filho, José T., Dhruv Ranganath, Mike Conway, Charles Schmitt, Nicole Kleinstreuer, and Kamel Mansouri. "Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow." Journal of Cheminformatics 16, no. 1 (2024). http://dx.doi.org/10.1186/s13321-024-00894-1.

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AbstractWith the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages. To address these challenges, we developed

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Kunnakkattu, Ibrahim Roshan, Preeti Choudhary, Lukas Pravda, et al. "PDBe CCDUtils: an RDKit-based toolkit for handling and analysing small molecules in the Protein Data Bank." Journal of Cheminformatics 15, no. 1 (2023). http://dx.doi.org/10.1186/s13321-023-00786-w.

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AbstractWhile the Protein Data Bank (PDB) contains a wealth of structural information on ligands bound to macromolecules, their analysis can be challenging due to the large amount and diversity of data. Here, we present PDBe CCDUtils, a versatile toolkit for processing and analysing small molecules from the PDB in PDBx/mmCIF format. PDBe CCDUtils provides streamlined access to all the metadata for small molecules in the PDB and offers a set of convenient methods to compute various properties using RDKit, such as 2D depictions, 3D conformers, physicochemical properties, scaffolds, common fragme

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Saldívar-González, Fernanda I., B. Angélica Pilón-Jiménez, and José L. Medina-Franco. "Chemical space of naturally occurring compounds." Physical Sciences Reviews 4, no. 5 (2018). http://dx.doi.org/10.1515/psr-2018-0103.

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AbstractThe chemical space of naturally occurring compounds is vast and diverse. Other than biologics, naturally occurring small molecules include a large variety of compounds covering natural products from different sources such as plant, marine, and fungi, to name a few, and several food chemicals. The systematic exploration of the chemical space of naturally occurring compounds have significant implications in many areas of research including but not limited to drug discovery, nutrition, bio- and chemical diversity analysis. The exploration of the coverage and diversity of the chemical spac

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Sucularlı, Ceren, Birsen Tozkoparan, and Sevim Peri Aytaç. "In silico Activity and Target Prediction Analyses of Three Triazolothiadiazine Derivatives." Acta Medica, June 16, 2022. http://dx.doi.org/10.32552/2022.actamedica.737.

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Objective: Polypharmacology, interaction of one drug with multiple targets, emerged as an effective approach in drug discovery and development. Bioinformatics and cheminformatics methods are essential tools for determination of polypharmacological profiles of newly synthesized or known compounds and drugs. Previously, three novel triazolothiadiazine derivatives; 1h, 3c and 3h, have been shown to induce apoptosis and cause cell cycle arrest on liver cancer cells. The aim of this study is to find possible action mechanisms and potential targets for these three triazolothiadiazine derivatives, an

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Manelfi, Candida, Valerio Tazzari, Filippo Lunghini, et al. "“DompeKeys”: a set of novel substructure-based descriptors for efficient chemical space mapping, development and structural interpretation of machine learning models, and indexing of large databases." Journal of Cheminformatics 16, no. 1 (2024). http://dx.doi.org/10.1186/s13321-024-00813-4.

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AbstractThe conversion of chemical structures into computer-readable descriptors, able to capture key structural aspects, is of pivotal importance in the field of cheminformatics and computer-aided drug design. Molecular fingerprints represent a widely employed class of descriptors; however, their generation process is time-consuming for large databases and is susceptible to bias. Therefore, descriptors able to accurately detect predefined structural fragments and devoid of lengthy generation procedures would be highly desirable. To meet additional needs, such descriptors should also be interp

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Hastings, Janna, Martin Glauer, Adel Memariani, Fabian Neuhaus, and Till Mossakowski. "Learning chemistry: exploring the suitability of machine learning for the task of structure-based chemical ontology classification." Journal of Cheminformatics 13, no. 1 (2021). http://dx.doi.org/10.1186/s13321-021-00500-8.

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AbstractChemical data is increasingly openly available in databases such as PubChem, which contains approximately 110 million compound entries as of February 2021. With the availability of data at such scale, the burden has shifted to organisation, analysis and interpretation. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of the large chemical space. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts. However, ChEBI is manually maintained and as such cannot easily scale to the ful

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