Skip to main content

Articles

Page 6 of 25

  1. We present machine learning (ML) models for hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) strengths. Quantum chemical (QC) free energies in solution for 1:1 hydrogen-bonded complex formation to th...

    Authors: Christoph A. Bauer, Gisbert Schneider and Andreas H. Göller
    Citation: Journal of Cheminformatics 2019 11:59
  2. The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The...

    Authors: Domenico Gadaleta, Kristijan Vuković, Cosimo Toma, Giovanna J. Lavado, Agnes L. Karmaus, Kamel Mansouri, Nicole C. Kleinstreuer, Emilio Benfenati and Alessandra Roncaglioni
    Citation: Journal of Cheminformatics 2019 11:58
  3. PubChem is a chemical data repository that provides comprehensive information on various chemical entities. It contains a wealth of chemical information from hundreds of data sources. Programmatic access to th...

    Authors: Sunghwan Kim, Paul A. Thiessen, Tiejun Cheng, Jian Zhang, Asta Gindulyte and Evan E. Bolton
    Citation: Journal of Cheminformatics 2019 11:56
  4. This study aims at improving upon existing activity predictions methods by augmenting chemical structure fingerprints with bio-activity based fingerprints derived from high-throughput screening (HTS) data (HTS...

    Authors: Oliver Laufkötter, Noé Sturm, Jürgen Bajorath, Hongming Chen and Ola Engkvist
    Citation: Journal of Cheminformatics 2019 11:54
  5. Performance of structure-based molecular docking largely depends on the accuracy of scoring functions. One important type of scoring functions are knowledge-based potentials derived from known three-dimensiona...

    Authors: Pin Chen, Yaobin Ke, Yutong Lu, Yunfei Du, Jiahui Li, Hui Yan, Huiying Zhao, Yaoqi Zhou and Yuedong Yang
    Citation: Journal of Cheminformatics 2019 11:52
  6. Bulk water molecular dynamics simulations based on a series of atomistic water potentials (TIP3P, TIP4P/Ew, SPC/E and OPC) are compared using new techniques from the field of topological data analysis. The top...

    Authors: Lee Steinberg, John Russo and Jeremy Frey
    Citation: Journal of Cheminformatics 2019 11:48

    The Correction to this article has been published in Journal of Cheminformatics 2019 11:51

  7. To better leverage the accumulated bioactivity data in the ChEMBL database, we have developed Bioactivity-explorer, a web application for interactive visualization and exploration of the large-scale bioactivit...

    Authors: Lu Liang, Chunfeng Ma, Tengfei Du, Yufei Zhao, Xiaoyong Zhao, Mengmeng Liu, Zhonghua Wang and Jianping Lin
    Citation: Journal of Cheminformatics 2019 11:47
  8. Analysis of compound–protein interactions (CPIs) has become a crucial prerequisite for drug discovery and drug repositioning. In vitro experiments are commonly used in identifying CPIs, but it is not feasible ...

    Authors: Munhwan Lee, Hyeyeon Kim, Hyunwhan Joe and Hong-Gee Kim
    Citation: Journal of Cheminformatics 2019 11:46
  9. The existing connections between large databases of chemicals, proteins, metabolites and assays offer valuable resources for research in fields ranging from drug design to metabolomics. Transparent search acro...

    Authors: Miroslav Kratochvíl, Jiří Vondrášek and Jakub Galgonek
    Citation: Journal of Cheminformatics 2019 11:45

    The Correction to this article has been published in Journal of Cheminformatics 2020 12:13

  10. Developing predictive and transparent approaches to the analysis of metabolite profiles across patient cohorts is of critical importance for understanding the events that trigger or modulate traits of interest...

    Authors: Jeremy R. Ash, Melaine A. Kuenemann, Daniel Rotroff, Alison Motsinger-Reif and Denis Fourches
    Citation: Journal of Cheminformatics 2019 11:43
  11. Shared tasks and community challenges represent key instruments to promote research, collaboration and determine the state of the art of biomedical and chemical text mining technologies. Traditionally, such ta...

    Authors: Martin Pérez-Pérez, Gael Pérez-Rodríguez, Aitor Blanco-Míguez, Florentino Fdez-Riverola, Alfonso Valencia, Martin Krallinger and Anália Lourenço
    Citation: Journal of Cheminformatics 2019 11:42
  12. The application of convolutional neural networks (ConvNets) to harness high-content screening images or 2D compound representations is gaining increasing attention in drug discovery. However, existing applicat...

    Authors: Isidro Cortés-Ciriano and Andreas Bender
    Citation: Journal of Cheminformatics 2019 11:41
  13. Traditional quantitative structure-activity relationship models usually neglect the molecular alterations happening in the exposed systems (the mechanism of action, MOA), that mediate between structural proper...

    Authors: Angela Serra, Serli Önlü, Pietro Coretto and Dario Greco
    Citation: Journal of Cheminformatics 2019 11:38
  14. The Ertl algorithm for automated functional groups (FG) detection and extraction of organic molecules is implemented on the basis of the Chemistry Development Kit (CDK). A distinct impact of the chosen CDK aro...

    Authors: Sebastian Fritsch, Stefan Neumann, Jonas Schaub, Christoph Steinbeck and Achim Zielesny
    Citation: Journal of Cheminformatics 2019 11:37
  15. Despite the increasing knowledge in both the chemical and biological domains the assimilation and exploration of heterogeneous datasets, encoding information about the chemical, bioactivity and phenotypic prop...

    Authors: Chad H. G. Allen, Lewis H. Mervin, Samar Y. Mahmoud and Andreas Bender
    Citation: Journal of Cheminformatics 2019 11:36
  16. Over the last 5 years deep learning has progressed tremendously in both image recognition and natural language processing. Now it is increasingly applied to other data rich fields. In drug discovery, recurrent...

    Authors: Xuhan Liu, Kai Ye, Herman W. T. van Vlijmen, Adriaan P. IJzerman and Gerard J. P. van Westen
    Citation: Journal of Cheminformatics 2019 11:35
  17. Computational techniques such as structure-based virtual screening require carefully prepared 3D models of potential small-molecule ligands. Though powerful, existing commercial programs for virtual-library pr...

    Authors: Patrick J. Ropp, Jacob O. Spiegel, Jennifer L. Walker, Harrison Green, Guillermo A. Morales, Katherine A. Milliken, John J. Ringe and Jacob D. Durrant
    Citation: Journal of Cheminformatics 2019 11:34
  18. We describe a file format that is designed to represent mixtures of compounds in a way that is fully machine readable. This Mixfile format is intended to fill the same role for substances that are composed of mul...

    Authors: Alex M. Clark, Leah R. McEwen, Peter Gedeck and Barry A. Bunin
    Citation: Journal of Cheminformatics 2019 11:33
  19. Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generati...

    Authors: Lucian Chan, Geoffrey R. Hutchison and Garrett M. Morris
    Citation: Journal of Cheminformatics 2019 11:32
  20. It was highlighted that the original article [1] contained an error in the Funding section. This Correction article states the correct and incorrect versions of the Funding section.

    Authors: Domenico Gadaleta, Anna Lombardo, Cosimo Toma and Emilio Benfenati
    Citation: Journal of Cheminformatics 2019 11:31

    The original article was published in Journal of Cheminformatics 2018 10:60

  21. Covalent DNA modifications, such as 5-methylcytosine (5mC), are increasingly the focus of numerous research programs. In eukaryotes, both 5mC and 5-hydroxymethylcytosine (5hmC) are now recognized as stable epi...

    Authors: Ankur Jai Sood, Coby Viner and Michael M. Hoffman
    Citation: Journal of Cheminformatics 2019 11:30
  22. Owing to the increase in freely available software and data for cheminformatics and structural bioinformatics, research for computer-aided drug design (CADD) is more and more built on modular, reproducible, an...

    Authors: Dominique Sydow, Andrea Morger, Maximilian Driller and Andrea Volkamer
    Citation: Journal of Cheminformatics 2019 11:29
  23. Because drug–drug interactions (DDIs) may cause adverse drug reactions or contribute to complex-disease treatments, it is important to identify DDIs before multiple-drug medications are prescribed. As the alte...

    Authors: Jian-Yu Shi, Kui-Tao Mao, Hui Yu and Siu-Ming Yiu
    Citation: Journal of Cheminformatics 2019 11:28
  24. Several QSAR methodology developments have shown promise in recent years. These include the consensus approach to generate the final prediction of a model, utilizing new, advanced machine learning algorithms a...

    Authors: Kristijan Vukovic, Domenico Gadaleta and Emilio Benfenati
    Citation: Journal of Cheminformatics 2019 11:27
  25. The process of ligand binding to a biological target can be represented as the equilibrium between the relevant solvated and bound states of the ligand. This which is the basis of structure-based, rigorous met...

    Authors: Willem Jespers, Mauricio Esguerra, Johan Åqvist and Hugo Gutiérrez-de-Terán
    Citation: Journal of Cheminformatics 2019 11:26
  26. Random peptide libraries that cover large search spaces are often used for the discovery of new binders, even when the target is unknown. To ensure an accurate population representation, there is a tendency to...

    Authors: Daniela Kalafatovic, Goran Mauša, Toni Todorovski and Ernest Giralt
    Citation: Journal of Cheminformatics 2019 11:25
  27. Docking is commonly used in drug discovery to predict how ligand binds to protein target. Best programs are generally able to generate a correct solution, yet often fail to identify it. In the case of drug-lik...

    Authors: Célien Jacquemard, Malgorzata N. Drwal, Jérémy Desaphy and Esther Kellenberger
    Citation: Journal of Cheminformatics 2019 11:24
  28. Scientific research is increasingly characterised by the volume of documents and data that it produces, from experimental plans and raw data to reports and papers. Researchers frequently struggle to manage and...

    Authors: Samantha Kanza, Nicholas Gibbins and Jeremy G. Frey
    Citation: Journal of Cheminformatics 2019 11:23
  29. Efficient representations of drugs provide important support for healthcare analytics, such as drug–drug interaction (DDI) prediction and drug–drug similarity (DDS) computation. However, incomplete annotated d...

    Authors: Ying Shen, Kaiqi Yuan, Min Yang, Buzhou Tang, Yaliang Li, Nan Du and Kai Lei
    Citation: Journal of Cheminformatics 2019 11:22
  30. Recent applications of recurrent neural networks (RNN) enable training models that sample the chemical space. In this study we train RNN with molecular string representations (SMILES) with a subset of the enum...

    Authors: Josep Arús-Pous, Thomas Blaschke, Silas Ulander, Jean-Louis Reymond, Hongming Chen and Ola Engkvist
    Citation: Journal of Cheminformatics 2019 11:20
  31. Drug discovery typically involves investigation of a set of compounds (e.g. drug screening hits) in terms of target, disease, and bioactivity. CSgator is a comprehensive analytic tool for set-wise interpretati...

    Authors: Sera Park, Yeajee Kwon, Hyesoo Jung, Sukyung Jang, Haeseung Lee and Wankyu Kim
    Citation: Journal of Cheminformatics 2019 11:17
  32. Mass spectrometry imaging (MSI) using laser ablation (LA) inductively coupled plasma (ICP) is an innovative and exciting methodology to perform highly sensitive elemental analyses. LA-ICP-MSI of metals, trace ...

    Authors: Ralf Weiskirchen, Sabine Weiskirchen, Philipp Kim and Robert Winkler
    Citation: Journal of Cheminformatics 2019 11:16
  33. Sodium-dependent glucose co-transporter 1 (SGLT1) is a solute carrier responsible for active glucose absorption. SGLT1 is present in both the renal tubules and small intestine. In contrast, the closely related...

    Authors: Lindsey Burggraaff, Paul Oranje, Robin Gouka, Pieter van der Pijl, Marian Geldof, Herman W. T. van Vlijmen, Adriaan P. IJzerman and Gerard J. P. van Westen
    Citation: Journal of Cheminformatics 2019 11:15
  34. Small-molecule protonation can promote or discourage protein binding by altering hydrogen-bond, electrostatic, and van-der-Waals interactions. To improve virtual-screen pose and affinity predictions, researche...

    Authors: Patrick J. Ropp, Jesse C. Kaminsky, Sara Yablonski and Jacob D. Durrant
    Citation: Journal of Cheminformatics 2019 11:14
  35. Proteinogenic and non-proteinogenic amino acids, fatty acids or glycans are some of the main building blocks of nonribsosomal peptides (NRPs) and as such may give insight into the origin, biosynthesis and bioa...

    Authors: Emma Ricart, Valérie Leclère, Areski Flissi, Markus Mueller, Maude Pupin and Frédérique Lisacek
    Citation: Journal of Cheminformatics 2019 11:13
  36. OCaml is a functional programming language with strong static types, Hindley–Milner type inference and garbage collection. In this article, we share our experience in prototyping chemoinformatics and structura...

    Authors: Francois Berenger, Kam Y. J. Zhang and Yoshihiro Yamanishi
    Citation: Journal of Cheminformatics 2019 11:10
BMC is part of Springer Nature

Annual Journal Metrics

  • Speed (average)
    47 days to first decision for reviewed manuscripts only
    32 days to first decision for all manuscripts
    124 days from submission to acceptance
    16 days from acceptance to publication

    Usage 
    760,938 downloads
    1567 Altmetric mentions

    Citations
    Click here to see citation distribution in recent years