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  1. MFsim is an open Java all-in-one rich-client computing environment for mesoscopic simulation with Jdpd as its default simulation kernel for Molecular Fragment (Dissipative Particle) Dynamics. The new environme...

    Authors: Karina van den Broek, Mirco Daniel, Matthias Epple, Jan-Mathis Hein, Hubert Kuhn, Stefan Neumann, Andreas Truszkowski and Achim Zielesny
    Citation: Journal of Cheminformatics 2020 12:29
  2. Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. ...

    Authors: Noé Sturm, Andreas Mayr, Thanh Le Van, Vladimir Chupakhin, Hugo Ceulemans, Joerg Wegner, Jose-Felipe Golib-Dzib, Nina Jeliazkova, Yves Vandriessche, Stanislav Böhm, Vojtech Cima, Jan Martinovic, Nigel Greene, Tom Vander Aa, Thomas J. Ashby, Sepp Hochreiter…
    Citation: Journal of Cheminformatics 2020 12:26
  3. Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they mak...

    Authors: Andrea Morger, Miriam Mathea, Janosch H. Achenbach, Antje Wolf, Roland Buesen, Klaus-Juergen Schleifer, Robert Landsiedel and Andrea Volkamer
    Citation: Journal of Cheminformatics 2020 12:24
  4. We report on a new cheminformatics enumeration technology—SIME, synthetic insight-based macrolide enumerator—a new and improved software technology. SIME can enumerate fully assembled macrolides with synthetic...

    Authors: Phyo Phyo Kyaw Zin, Gavin Williams and Denis Fourches
    Citation: Journal of Cheminformatics 2020 12:23
  5. Recurrent neural networks have been widely used to generate millions of de novo molecules in defined chemical spaces. Reported deep generative models are exclusively based on LSTM and/or GRU units and frequent...

    Authors: Ruud van Deursen, Peter Ertl, Igor V. Tetko and Guillaume Godin
    Citation: Journal of Cheminformatics 2020 12:22
  6. Over the last few decades, chemists have become skilled at designing compounds that avoid cytochrome P (CYP) 450 mediated metabolism. Typical screening assays are performed in liver microsomal fractions and it...

    Authors: Pranav Shah, Vishal B. Siramshetty, Alexey V. Zakharov, Noel T. Southall, Xin Xu and Dac-Trung Nguyen
    Citation: Journal of Cheminformatics 2020 12:21
  7. Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the ...

    Authors: Chia-Hsiu Chen, Kenichi Tanaka, Masaaki Kotera and Kimito Funatsu
    Citation: Journal of Cheminformatics 2020 12:19
  8. Training neural networks with small and imbalanced datasets often leads to overfitting and disregard of the minority class. For predictive toxicology, however, models with a good balance between sensitivity an...

    Authors: Jennifer Hemmerich, Ece Asilar and Gerhard F. Ecker
    Citation: Journal of Cheminformatics 2020 12:18
  9. We present SMILES-embeddings derived from the internal encoder state of a Transformer [1] model trained to canonize SMILES as a Seq2Seq problem. Using a CharNN [2] architecture upon the embeddings results in high...

    Authors: Pavel Karpov, Guillaume Godin and Igor V. Tetko
    Citation: Journal of Cheminformatics 2020 12:17
  10. Breast cancer resistance protein (BCRP/ABCG2), an ATP-binding cassette (ABC) efflux transporter, plays a critical role in multi-drug resistance (MDR) to anti-cancer drugs and drug–drug interactions. The predic...

    Authors: Dejun Jiang, Tailong Lei, Zhe Wang, Chao Shen, Dongsheng Cao and Tingjun Hou
    Citation: Journal of Cheminformatics 2020 12:16
  11. Efficient and accurate prediction of molecular properties, such as lipophilicity and solubility, is highly desirable for rational compound design in chemical and pharmaceutical industries. To this end, we buil...

    Authors: Bowen Tang, Skyler T. Kramer, Meijuan Fang, Yingkun Qiu, Zhen Wu and Dong Xu
    Citation: Journal of Cheminformatics 2020 12:15
  12. It was highlighted that the original article [1] contained an error in the last paragraph of the section ‘Structure search using SPARQL’, specifically in the radius of the used fingerprint. This Correction art...

    Authors: Miroslav Kratochvíl, Jiří Vondrášek and Jakub Galgonek
    Citation: Journal of Cheminformatics 2020 12:13

    The original article was published in Journal of Cheminformatics 2019 11:45

  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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

  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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

  32. 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

  33. 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
  34. 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
  35. 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
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