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  1. The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilizat...

    Authors: Nalini Schaduangrat, Samuel Lampa, Saw Simeon, Matthew Paul Gleeson, Ola Spjuth and Chanin Nantasenamat

    Citation: Journal of Cheminformatics 2020 12:9

    Content type: Review

    Published on:

  2. The increasing number of organic and inorganic structures promotes the development of the “Big Data” in chemistry and material science, and raises the need for cross-platform and web-based methods to search, v...

    Authors: Pin Chen, Yu Wang, Hui Yan, Sen Gao, Zexin Xu, Yangzhong Li, Qing Mo, Junkang Huang, Jun Tao, GeChuanqi Pan, Jiahui Li and Yunfei Du

    Citation: Journal of Cheminformatics 2020 12:7

    Content type: Software

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  3. Computer-aided research on the relationship between molecular structures of natural compounds (NC) and their biological activities have been carried out extensively because the molecular structures of new drug...

    Authors: Myungwon Seo, Hyun Kil Shin, Yoochan Myung, Sungbo Hwang and Kyoung Tai No

    Citation: Journal of Cheminformatics 2020 12:6

    Content type: Methodology

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  4. Drug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate...

    Authors: Gergely Zahoránszky-Kőhalmi, Timothy Sheils and Tudor I. Oprea

    Citation: Journal of Cheminformatics 2020 12:5

    Content type: Research article

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  5. Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To improv...

    Authors: Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Tomasz Danel and Michał Warchoł

    Citation: Journal of Cheminformatics 2020 12:2

    Content type: Research article

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  6. Neural Message Passing for graphs is a promising and relatively recent approach for applying Machine Learning to networked data. As molecules can be described intrinsically as a molecular graph, it makes sense...

    Authors: M. Withnall, E. Lindelöf, O. Engkvist and H. Chen

    Citation: Journal of Cheminformatics 2020 12:1

    Content type: Research article

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  7. The problem of drug side effects is one of the most crucial issues in pharmacological development. As there are many limitations in current experimental and clinical methods for detecting side effects, a lot o...

    Authors: Xujun Liang, Pengfei Zhang, Jun Li, Ying Fu, Lingzhi Qu, Yongheng Chen and Zhuchu Chen

    Citation: Journal of Cheminformatics 2019 11:79

    Content type: Research article

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  8. We developed ChemScanner, a software that can be used for the extraction of chemical information from ChemDraw binary (CDX) or ChemDraw XML-based (CDXML) files and to retrieve the ChemDraw scheme from DOC, DOCX o...

    Authors: An Nguyen, Yu-Chieh Huang, Pierre Tremouilhac, Nicole Jung and Stefan Bräse

    Citation: Journal of Cheminformatics 2019 11:77

    Content type: Software

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  9. 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

    Content type: Methodology

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  10. 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

    Content type: Research article

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  11. 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

    Content type: Research article

    Published on:

  12. 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

    Content type: Research article

    Published on:

  13. 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

    Content type: Research article

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  14. 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

    Content type: Research article

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  15. 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

    Content type: Research article

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  16. 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

    Content type: Research article

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  17. 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

    Content type: Research article

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  18. 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

    Content type: Research article

    Published on:

  19. 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

    Content type: Letter to the Editor

    Published on:

    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

  20. 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

    Content type: Letter to the Editor

    Published on:

    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

  21. 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

    Content type: Commentary

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  22. 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

    Content type: Methodology

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  23. 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

    Content type: Research article

    Published on:

  24. 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

    Content type: Research article

    Published on:

  25. 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

    Content type: Research article

    Published on:

  26. 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

    Content type: Database

    Published on:

  27. 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

    Content type: Research article

    Published on:

  28. 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

    Content type: Research article

    Published on:

  29. 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

    Content type: Research article

    Published on:

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

  30. 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

    Content type: Software

    Published on:

  31. 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

    Content type: Research article

    Published on:

  32. 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

    Content type: Software

    Published on:

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

  33. 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

    Content type: Research article

    Published on:

  34. 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

    Content type: Research article

    Published on:

  35. 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

    Content type: Research article

    Published on:

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