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  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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

  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. In virtual screening for drug discovery, hit enrichment curves are widely used to assess the performance of ranking algorithms with regard to their ability to identify early enrichment. Unfortunately, research...

    Authors: Jeremy R Ash and Jacqueline M Hughes-Oliver
    Citation: Journal of Cheminformatics 2022 14:50
  26. Polypharmacy refers to the administration of multiple drugs on a daily basis. It has demonstrated effectiveness in treating many complex diseases , but it has a higher risk of adverse drug reactions. Hence, th...

    Authors: Nina Lukashina, Elena Kartysheva, Ola Spjuth, Elizaveta Virko and Aleksei Shpilman
    Citation: Journal of Cheminformatics 2022 14:49
  27. Comparing chemical structures to infer protein targets and functions is a common approach, but basing comparisons on chemical similarity alone can be misleading. Here we present a methodology for predicting ta...

    Authors: Akshai P. Sreenivasan, Philip J Harrison, Wesley Schaal, Damian J. Matuszewski, Kim Kultima and Ola Spjuth
    Citation: Journal of Cheminformatics 2022 14:47
  28. UDP-glucuronosyltransferases (UGTs) have gained increasing attention as they play important roles in the phase II metabolism of drugs. Due to the time-consuming process and high cost of experimental approaches...

    Authors: Mengting Huang, Chaofeng Lou, Zengrui Wu, Weihua Li, Philip W. Lee, Yun Tang and Guixia Liu
    Citation: Journal of Cheminformatics 2022 14:46
  29. Bitterness is an aversive cue elicited by thousands of chemically diverse compounds. Bitter taste may prevent consumption of foods and jeopardize drug compliance. The G protein-coupled receptors for bitter tas...

    Authors: Eitan Margulis, Yuli Slavutsky, Tatjana Lang, Maik Behrens, Yuval Benjamini and Masha Y. Niv
    Citation: Journal of Cheminformatics 2022 14:45
  30. Blood–brain barrier is a pivotal factor to be considered in the process of central nervous system (CNS) drug development, and it is of great significance to rapidly explore the blood–brain barrier permeability...

    Authors: Xiaochu Tong, Dingyan Wang, Xiaoyu Ding, Xiaoqin Tan, Qun Ren, Geng Chen, Yu Rong, Tingyang Xu, Junzhou Huang, Hualiang Jiang, Mingyue Zheng and Xutong Li
    Citation: Journal of Cheminformatics 2022 14:44
  31. Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this proc...

    Authors: Maryam Abbasi, Beatriz P. Santos, Tiago C. Pereira, Raul Sofia, Nelson R. C. Monteiro, Carlos J. V. Simões, Rui M. M. Brito, Bernardete Ribeiro, José L. Oliveira and Joel P. Arrais
    Citation: Journal of Cheminformatics 2022 14:40

    The Correction to this article has been published in Journal of Cheminformatics 2022 14:53

  32. Predicting transition state geometries is one of the most challenging tasks in computational chemistry, which often requires expert-based knowledge and permanent human intervention. This short communication re...

    Authors: Bienfait K. Isamura and Kevin A. Lobb
    Citation: Journal of Cheminformatics 2022 14:39
  33. As an alternative to one drug-one target approaches, systems biology methods can provide a deeper insight into the holistic effects of drugs. Network-based approaches are tools of systems biology, that can rep...

    Authors: Barbara Füzi, Rahuman S. Malik-Sheriff, Emma J. Manners, Henning Hermjakob and Gerhard F. Ecker
    Citation: Journal of Cheminformatics 2022 14:37
  34. The translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over...

    Authors: Henning Otto Brinkhaus, Achim Zielesny, Christoph Steinbeck and Kohulan Rajan
    Citation: Journal of Cheminformatics 2022 14:36
  35. As efforts to computationally describe and simulate the biochemical world become more commonplace, computer programs that are capable of in silico chemistry play an increasingly important role in biochemical r...

    Authors: Barbara R. Terlouw, Sophie P. J. M. Vromans and Marnix H. Medema
    Citation: Journal of Cheminformatics 2022 14:34
  36. Retention time information is used for metabolite annotation in metabolomic experiments. But its usefulness is hindered by the availability of experimental retention time data in metabolomic databases, and by ...

    Authors: Constantino A. García, Alberto Gil-de-la-Fuente, Coral Barbas and Abraham Otero
    Citation: Journal of Cheminformatics 2022 14:33
  37. Recently, imputation techniques have been adapted to predict activity values among sparse bioactivity matrices, showing improvements in predictive performance over traditional QSAR models. These models are abl...

    Authors: Moritz Walter, Luke N. Allen, Antonio de la Vega de León, Samuel J. Webb and Valerie J. Gillet
    Citation: Journal of Cheminformatics 2022 14:32