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  1. In this work, we provide further development of the junction tree variational autoencoder (JT VAE) architecture in terms of implementation and application of the internal feature space of the model. Pretrainin...

    Authors: Vladimir Kondratyev, Marian Dryzhakov, Timur Gimadiev and Dmitriy Slutskiy
    Citation: Journal of Cheminformatics 2023 15:11
  2. This article documents enu, a freely-downloadable, open-source and stand-alone program written in C++ for the enumeration of the constitutional isomers and stereoisomers of a molecular formula. The program relies...

    Authors: Salomé R. Rieder, Marina P. Oliveira, Sereina Riniker and Philippe H. Hünenberger
    Citation: Journal of Cheminformatics 2023 15:10
  3. The field of high-resolution mass spectrometry (HRMS) and ancillary hyphenated techniques comprise a rapidly expanding and evolving area. As popularity of HRMS instruments grows, there is a concurrent need for...

    Authors: Dane R. Letourneau, Dennis D. August and Dietrich A. Volmer
    Citation: Journal of Cheminformatics 2023 15:7
  4. Modern computer-assisted synthesis planning tools provide strong support for this problem. However, they are still limited by computational complexity. This limitation may be overcome by scoring the synthetic ...

    Authors: Grzegorz Skoraczyński, Mateusz Kitlas, Błażej Miasojedow and Anna Gambin
    Citation: Journal of Cheminformatics 2023 15:6
  5. Ubiquitin-specific-processing protease 7 (USP7) is a promising target protein for cancer therapy, and great attention has been given to the identification of USP7 inhibitors. Traditional virtual screening meth...

    Authors: Wen-feng Shen, He-wei Tang, Jia-bo Li, Xiang Li and Si Chen
    Citation: Journal of Cheminformatics 2023 15:5
  6. Activity cliffs (AC) are formed by pairs of structural analogues that are active against the same target but have a large difference in potency. While much of our knowledge about ACs has originated from the an...

    Authors: Shunsuke Tamura, Tomoyuki Miyao and Jürgen Bajorath
    Citation: Journal of Cheminformatics 2023 15:4
  7. With the ongoing rapid growth of publicly available ligand–protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However, not all data...

    Authors: O. J. M. Béquignon, B. J. Bongers, W. Jespers, A. P. IJzerman, B. van der Water and G. J. P. van Westen
    Citation: Journal of Cheminformatics 2023 15:3
  8. Explainable artificial intelligence (XAI) methods have shown increasing applicability in chemistry. In this context, visualization techniques can highlight regions of a molecule to reveal their influence over ...

    Authors: Henry Heberle, Linlin Zhao, Sebastian Schmidt, Thomas Wolf and Julian Heinrich
    Citation: Journal of Cheminformatics 2023 15:2
  9. Developing and implementing computational algorithms for the extraction of specific substructures from molecular graphs (in silico molecule fragmentation) is an iterative process. It involves repeated sequence...

    Authors: Felix Bänsch, Jonas Schaub, Betül Sevindik, Samuel Behr, Julian Zander, Christoph Steinbeck and Achim Zielesny
    Citation: Journal of Cheminformatics 2023 15:1
  10. Traditional Chinese Medicine (TCM) has been widely used in the treatment of various diseases for millennia. In the modernization process of TCM, TCM ingredient databases are playing more and more important rol...

    Authors: Liu-Xia Zhang, Jie Dong, Hui Wei, Shao-Hua Shi, Ai-Ping Lu, Gui-Ming Deng and Dong-Sheng Cao
    Citation: Journal of Cheminformatics 2022 14:89
  11. This article demonstrates how to create Chemical Space Networks (CSNs) using a Python RDKit and NetworkX workflow. CSNs are a type of network visualization that depict compounds as nodes connected by edges, de...

    Authors: Vincent F. Scalfani, Vishank D. Patel and Avery M. Fernandez
    Citation: Journal of Cheminformatics 2022 14:87
  12. A de novo molecular design workflow can be used together with technologies such as reinforcement learning to navigate the chemical space. A bottleneck in the workflow that remains to be solved is how to integr...

    Authors: Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos, Esben Jannik Bjerrum, Markus Heinonen, Atanas Patronov, Samuel Kaski and Ola Engkvist
    Citation: Journal of Cheminformatics 2022 14:86
  13. Homologous series are groups of related compounds that share the same core structure attached to a motif that repeats to different degrees. Compounds forming homologous series are of interest in multiple domai...

    Authors: Adelene Lai, Jonas Schaub, Christoph Steinbeck and Emma L. Schymanski
    Citation: Journal of Cheminformatics 2022 14:85
  14. Deep learning (DL) and machine learning contribute significantly to basic biology research and drug discovery in the past few decades. Recent advances in DL-based generative models have led to superior develop...

    Authors: Mingyang Wang, Jike Wang, Gaoqi Weng, Yu Kang, Peichen Pan, Dan Li, Yafeng Deng, Honglin Li, Chang-Yu Hsieh and Tingjun Hou
    Citation: Journal of Cheminformatics 2022 14:84
  15. In this paper, a reinforcement learning model is proposed that can maximize the predicted binding affinity between a generated molecule and target proteins. The model used to generate molecules in the proposed...

    Authors: Hwanhee Kim, Soohyun Ko, Byung Ju Kim, Sung Jin Ryu and Jaegyoon Ahn
    Citation: Journal of Cheminformatics 2022 14:83
  16. We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15–17, 2022. Fifteen lectures were presented during a virtual public event with speakers f...

    Authors: Jürgen Bajorath, Ana L. Chávez-Hernández, Miquel Duran-Frigola, Eli Fernández-de Gortari, Johann Gasteiger, Edgar López-López, Gerald M. Maggiora, José L. Medina-Franco, Oscar Méndez-Lucio, Jordi Mestres, Ramón Alain Miranda-Quintana, Tudor I. Oprea, Fabien Plisson, Fernando D. Prieto-Martínez, Raquel Rodríguez-Pérez, Paola Rondón-Villarreal…
    Citation: Journal of Cheminformatics 2022 14:82
  17. The joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and result in adverse consequence to the patients. Accurate identification of DDI types can not only provide hints to avoid th...

    Authors: Shenggeng Lin, Weizhi Chen, Gengwang Chen, Songchi Zhou, Dong-Qing Wei and Yi Xiong
    Citation: Journal of Cheminformatics 2022 14:81
  18. While in the last years there has been a dramatic increase in the number of available bioassay datasets, many of them suffer from extremely imbalanced distribution between active and inactive compounds. Thus, ...

    Authors: Davide Boldini, Lukas Friedrich, Daniel Kuhn and Stephan A. Sieber
    Citation: Journal of Cheminformatics 2022 14:80
  19. The 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 hig...

    Authors: Jonas Schaub, Julian Zander, Achim Zielesny and Christoph Steinbeck
    Citation: Journal of Cheminformatics 2022 14:79
  20. Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material’s target properti...

    Authors: Sherif Abdulkader Tawfik and Salvy P. Russo
    Citation: Journal of Cheminformatics 2022 14:78
  21. As an important member of ion channels family, the voltage-gated sodium channel (VGSC/Nav) is associated with a variety of diseases, including epilepsy, migraine, ataxia, etc., and has always been a hot target fo...

    Authors: Gaoang Wang, Jiahui Yu, Hongyan Du, Chao Shen, Xujun Zhang, Yifei Liu, Yangyang Zhang, Dongsheng Cao, Peichen Pan and Tingjun Hou
    Citation: Journal of Cheminformatics 2022 14:75
  22. G protein-coupled receptors are involved in many biological processes, relaying the extracellular signal inside the cell. Signaling is regulated by the interactions between receptors and their ligands, it can ...

    Authors: Luca Chiesa and Esther Kellenberger
    Citation: Journal of Cheminformatics 2022 14:74
  23. We report a novel approach for grading chemical structure drawings for remote teaching, integrated into the Moodle platform. Typically, existing online platforms use a binary grading system, which often fails ...

    Authors: Louis Plyer, Gilles Marcou, Céline Perves, Rachel Schurhammer and Alexandre Varnek
    Citation: Journal of Cheminformatics 2022 14:72
  24. Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an a...

    Authors: Jong Youl Choi, Pei Zhang, Kshitij Mehta, Andrew Blanchard and Massimiliano Lupo Pasini
    Citation: Journal of Cheminformatics 2022 14:70
  25. Collecting labeled data for many important tasks in chemoinformatics is time consuming and requires expensive experiments. In recent years, machine learning has been used to learn rich representations of molec...

    Authors: Ani Tevosyan, Lusine Khondkaryan, Hrant Khachatrian, Gohar Tadevosyan, Lilit Apresyan, Nelly Babayan, Helga Stopper and Zaven Navoyan
    Citation: Journal of Cheminformatics 2022 14:69
  26. A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is us...

    Authors: Morgan Thomas, Noel M. O’Boyle, Andreas Bender and Chris de Graaf
    Citation: Journal of Cheminformatics 2022 14:68
  27. Virtual screening has significantly improved the success rate of early stage drug discovery. Recent virtual screening methods have improved owing to advances in machine learning and chemical information. Among...

    Authors: Sangjin Ahn, Si Eun Lee and Mi-hyun Kim
    Citation: Journal of Cheminformatics 2022 14:67

    The Correction to this article has been published in Journal of Cheminformatics 2022 14:76

  28. TUCAN is a canonical serialization format that is independent of domain-specific concepts of structure and bonding. The atomic number is the only chemical feature that is used to derive the TUCAN format. Other...

    Authors: Jan C. Brammer, Gerd Blanke, Claudia Kellner, Alexander Hoffmann, Sonja Herres-Pawlis and Ulrich Schatzschneider
    Citation: Journal of Cheminformatics 2022 14:66
  29. Deep learning has demonstrated promising results in de novo drug design. Often, the general pipeline consists of training a generative model (G) to learn the building rules of valid molecules, then using a bia...

    Authors: Mohamed-Amine Chadi, Hajar Mousannif and Ahmed Aamouche
    Citation: Journal of Cheminformatics 2022 14:65
  30. The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authen...

    Authors: Yasemin Yesiltepe, Niranjan Govind, Thomas O. Metz and Ryan S. Renslow
    Citation: Journal of Cheminformatics 2022 14:64
  31. Extraction of chemical formulas from images was not in the top priority of Computer Vision tasks for a while. The complexity both on the input and prediction sides has made this task challenging for the conven...

    Authors: Fidan Musazade, Narmin Jamalova and Jamaladdin Hasanov
    Citation: Journal of Cheminformatics 2022 14:61
  32. Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial i...

    Authors: Xinqiao Wang, Chuansheng Yao, Yun Zhang, Jiahui Yu, Haoran Qiao, Chengyun Zhang, Yejian Wu, Renren Bai and Hongliang Duan
    Citation: Journal of Cheminformatics 2022 14:60
  33. The related problems of chemical reaction optimization and reaction scope search concern the discovery of reaction pathways and conditions that provide the best percentage yield of a target product. The space ...

    Authors: Rubaiyat Mohammad Khondaker, Stephen Gow, Samantha Kanza, Jeremy G Frey and Mahesan Niranjan
    Citation: Journal of Cheminformatics 2022 14:59
  34. Detecting macromolecular (e.g., protein) cavities where small molecules bind is an early step in computer-aided drug discovery. Multiple pocket-detection algorithms have been developed over the past several de...

    Authors: Yuri Kochnev and Jacob D. Durrant
    Citation: Journal of Cheminformatics 2022 14:58
  35. Management of nanomaterials and nanosafety data needs to operate under the FAIR (findability, accessibility, interoperability, and reusability) principles and this requires a unique, global identifier for each...

    Authors: Jeaphianne van Rijn, Antreas Afantitis, Mustafa Culha, Maria Dusinska, Thomas E. Exner, Nina Jeliazkova, Eleonora Marta Longhin, Iseult Lynch, Georgia Melagraki, Penny Nymark, Anastasios G. Papadiamantis, David A. Winkler, Hulya Yilmaz and Egon Willighagen
    Citation: Journal of Cheminformatics 2022 14:57
  36. Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc...

    Authors: Yang Yu, Zhe Wang, Lingling Wang, Sheng Tian, Tingjun Hou and Huiyong Sun
    Citation: Journal of Cheminformatics 2022 14:56
  37. Application of chemical named entity recognition (CNER) algorithms allows retrieval of information from texts about chemical compound identifiers and creates associations with physical–chemical properties and ...

    Authors: O. A. Tarasova, A. V. Rudik, N. Yu. Biziukova, D. A. Filimonov and V. V. Poroikov
    Citation: Journal of Cheminformatics 2022 14:55
  38. Machine learning (ML) models require an extensive, user-driven selection of molecular descriptors in order to learn from chemical structures to predict actives and inactives with a high reliability. In additio...

    Authors: Aljoša Smajić, Melanie Grandits and Gerhard F. Ecker
    Citation: Journal of Cheminformatics 2022 14:54
  39. Recently, graph neural networks (GNNs) have revolutionized the field of chemical property prediction and achieved state-of-the-art results on benchmark data sets. Compared with the traditional descriptor- and ...

    Authors: Yue Kong, Xiaoman Zhao, Ruizi Liu, Zhenwu Yang, Hongyan Yin, Bowen Zhao, Jinling Wang, Bingjie Qin and Aixia Yan
    Citation: Journal of Cheminformatics 2022 14:52