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  1. The chemfp project has had four main goals: (1) promote the FPS format as a text-based exchange format for dense binary cheminformatics fingerprints, (2) develop a high-performance implementation of the BitBou...

    Authors: Andrew Dalke
    Citation: Journal of Cheminformatics 2019 11:76

    The Correction to this article has been published in Journal of Cheminformatics 2020 12:59

  2. Metabolic profiling has been shown to be useful to improve our understanding of complex metabolic processes. Shared data are key to the analysis and validation of metabolic profiling and untargeted spectral an...

    Authors: Julien Wist
    Citation: Journal of Cheminformatics 2019 11:75
  3. Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generativ...

    Authors: Oleksii Prykhodko, Simon Viet Johansson, Panagiotis-Christos Kotsias, Josep Arús-Pous, Esben Jannik Bjerrum, Ola Engkvist and Hongming Chen
    Citation: Journal of Cheminformatics 2019 11:74
  4. Drug repurposing offers a promising alternative to dramatically shorten the process of traditional de novo development of a drug. These efforts leverage the fact that a single molecule can act on multiple targ...

    Authors: Fan Wang, Feng-Xu Wu, Cheng-Zhang Li, Chen-Yang Jia, Sun-Wen Su, Ge-Fei Hao and Guang-Fu Yang
    Citation: Journal of Cheminformatics 2019 11:73
  5. Recurrent Neural Networks (RNNs) trained with a set of molecules represented as unique (canonical) SMILES strings, have shown the capacity to create large chemical spaces of valid and meaningful structures. He...

    Authors: Josep Arús-Pous, Simon Viet Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen and Ola Engkvist
    Citation: Journal of Cheminformatics 2019 11:71
  6. With the advancements in deep learning, deep generative models combined with graph neural networks have been successfully employed for data-driven molecular graph generation. Early methods based on the non-aut...

    Authors: Youngchun Kwon, Jiho Yoo, Youn-Suk Choi, Won-Joon Son, Dongseon Lee and Seokho Kang
    Citation: Journal of Cheminformatics 2019 11:70
  7. The QM9 dataset has become the golden standard for Machine Learning (ML) predictions of various chemical properties. QM9 is based on the GDB, which is a combinatorial exploration of the chemical space. ML mole...

    Authors: Marta Glavatskikh, Jules Leguy, Gilles Hunault, Thomas Cauchy and Benoit Da Mota
    Citation: Journal of Cheminformatics 2019 11:69
  8. The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound....

    Authors: Noureddin Sadawi, Ivan Olier, Joaquin Vanschoren, Jan N. van Rijn, Jeremy Besnard, Richard Bickerton, Crina Grosan, Larisa Soldatova and Ross D. King
    Citation: Journal of Cheminformatics 2019 11:68
  9. Molecular descriptor (2D) and three dimensional (3D) shape based similarity methods are widely used in ligand based virtual drug design. In the present study pairwise structure comparisons among a set of 4858 ...

    Authors: Anna Lovrics, Veronika F. S. Pape, Dániel Szisz, Adrián Kalászi, Petra Heffeter, Csaba Magyar and Gergely Szakács
    Citation: Journal of Cheminformatics 2019 11:67
  10. Drugs have become an essential part of our lives due to their ability to improve people’s health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have und...

    Authors: David Ruano-Ordás, Lindsey Burggraaff, Rongfang Liu, Cas van der Horst, Laura H. Heitman, Michael T. M. Emmerich, Jose R. Mendez, Iryna Yevseyeva and Gerard J. P. van Westen
    Citation: Journal of Cheminformatics 2019 11:66
  11. Recently Bosc et al. (J Cheminform 11(1): 4, 2019), published an article describing a case study that directly compares conformal predictions with traditional QSAR methods for large-scale predictions of target...

    Authors: Damjan Krstajic
    Citation: Journal of Cheminformatics 2019 11:65

    The original article was published in Journal of Cheminformatics 2019 11:4

    The Letter to the Editor to this article has been published in Journal of Cheminformatics 2019 11:64

  12. In response to Krstajic’s letter to the editor concerning our published paper, we here take the opportunity to reply, to re-iterate that no errors in our work were identified, to provide further details, and t...

    Authors: Nicolas Bosc, Francis Atkinson, Eloy Félix, Anna Gaulton, Anne Hersey and Andrew R. Leach
    Citation: Journal of Cheminformatics 2019 11:64

    The original article was published in Journal of Cheminformatics 2019 11:65

    The Research article to this article has been published in Journal of Cheminformatics 2019 11:4

  13. Currently, the submission guidelines for the Journal of Cheminformatics say it will “only publish research or software that is entirely reproducible by third parties.” They go on to specify that being reproduc...

    Authors: Robert D. Clark
    Citation: Journal of Cheminformatics 2019 11:62
  14. Scaffold analysis of compound data sets has reemerged as a chemically interpretable alternative to machine learning for chemical space and structure–activity relationships analysis. In this context, analog ser...

    Authors: J. Jesús Naveja, B. Angélica Pilón-Jiménez, Jürgen Bajorath and José L. Medina-Franco
    Citation: Journal of Cheminformatics 2019 11:61
  15. The logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affect...

    Authors: Kamel Mansouri, Neal F. Cariello, Alexandru Korotcov, Valery Tkachenko, Chris M. Grulke, Catherine S. Sprankle, David Allen, Warren M. Casey, Nicole C. Kleinstreuer and Antony J. Williams
    Citation: Journal of Cheminformatics 2019 11:60
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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

  22. 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
  23. 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
  24. 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

  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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

  36. 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
  37. 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
  38. 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
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