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  1. The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical class...

    Authors: Gabriel Idakwo, Sundar Thangapandian, Joseph Luttrell, Yan Li, Nan Wang, Zhaoxian Zhou, Huixiao Hong, Bei Yang, Chaoyang Zhang and Ping Gong
    Citation: Journal of Cheminformatics 2020 12:66
  2. The automatic recognition of chemical structure diagrams from the literature is an indispensable component of workflows to re-discover information about chemicals and to make it available in open-access databa...

    Authors: Kohulan Rajan, Achim Zielesny and Christoph Steinbeck
    Citation: Journal of Cheminformatics 2020 12:65
  3. Virtual compound libraries are increasingly being used in computer-assisted drug discovery applications and have led to numerous successful cases. This paper aims to examine the fundamental concepts of library...

    Authors: Fernanda I. Saldívar-González, C. Sebastian Huerta-García and José L. Medina-Franco
    Citation: Journal of Cheminformatics 2020 12:64
  4. Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues.

    Authors: Laeeq Ahmed, Hiba Alogheli, Staffan Arvidsson McShane, Jonathan Alvarsson, Arvid Berg, Anders Larsson, Wesley Schaal, Erwin Laure and Ola Spjuth
    Citation: Journal of Cheminformatics 2020 12:62
  5. The maximum common property similarity (MCPhd) method is presented using descriptors as a new approach to determine the similarity between two chemical compounds or molecular graphs. This method uses the conce...

    Authors: Aurelio Antelo-Collado, Ramón Carrasco-Velar, Nicolás García-Pedrajas and Gonzalo Cerruela-García
    Citation: Journal of Cheminformatics 2020 12:61
  6. Structural information about chemical compounds is typically conveyed as 2D images of molecular structures in scientific documents. Unfortunately, these depictions are not a machine-readable representation of ...

    Authors: Kohulan Rajan, Henning Otto Brinkhaus, Achim Zielesny and Christoph Steinbeck
    Citation: Journal of Cheminformatics 2020 12:60
  7. Recently, deep learning has been successfully applied to molecular graph generation. Nevertheless, mitigating the computational complexity, which increases with the number of nodes in a graph, has been a major...

    Authors: Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Kyoham Shin and Seokho Kang
    Citation: Journal of Cheminformatics 2020 12:58
  8. Named Entity Linking systems are a powerful aid to the manual curation of digital libraries, which is getting increasingly costly and inefficient due to the information overload. Models based on the Personaliz...

    Authors: Pedro Ruas, Andre Lamurias and Francisco M. Couto
    Citation: Journal of Cheminformatics 2020 12:57
  9. The objective of this work is to design a molecular generator capable of exploring known as well as unfamiliar areas of the chemical space. Our method must be flexible to adapt to very different problems. Ther...

    Authors: Jules Leguy, Thomas Cauchy, Marta Glavatskikh, Béatrice Duval and Benoit Da Mota
    Citation: Journal of Cheminformatics 2020 12:55
  10. This paper is a tutorial developed for the data analysis platform Galaxy. The purpose of Galaxy is to make high-throughput computational data analysis, such as molecular dynamics, a structured, reproducible an...

    Authors: Simon A. Bray, Tharindu Senapathi, Christopher B. Barnett and Björn A. Grüning
    Citation: Journal of Cheminformatics 2020 12:54
  11. We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolut...

    Authors: Yash Khemchandani, Stephen O’Hagan, Soumitra Samanta, Neil Swainston, Timothy J. Roberts, Danushka Bollegala and Douglas B. Kell
    Citation: Journal of Cheminformatics 2020 12:53
  12. In computer-assisted synthesis planning (CASP) programs, providing as many chemical synthetic routes as possible is essential for considering optimal and alternative routes in a chemical reaction network. As t...

    Authors: Ryosuke Shibukawa, Shoichi Ishida, Kazuki Yoshizoe, Kunihiro Wasa, Kiyosei Takasu, Yasushi Okuno, Kei Terayama and Koji Tsuda
    Citation: Journal of Cheminformatics 2020 12:52
  13. The ChEMBL database is one of a number of public databases that contain bioactivity data on small molecule compounds curated from diverse sources. Incoming compounds are typically not standardised according to...

    Authors: A. Patrícia Bento, Anne Hersey, Eloy Félix, Greg Landrum, Anna Gaulton, Francis Atkinson, Louisa J. Bellis, Marleen De Veij and Andrew R. Leach
    Citation: Journal of Cheminformatics 2020 12:51
  14. Machine translation of chemical nomenclature has considerable application prospect in chemical text data processing between languages. However, rule based machine translation tools have to face significant com...

    Authors: Tingjun Xu, Weiming Chen, Junhong Zhou, Jingfang Dai, Yingyong Li and Yingli Zhao
    Citation: Journal of Cheminformatics 2020 12:50
  15. Root mean square displacement (RMSD) calculations play a fundamental role in the comparison of different conformers of the same ligand. This is particularly important in the evaluation of protein-ligand dockin...

    Authors: Rocco Meli and Philip C. Biggin
    Citation: Journal of Cheminformatics 2020 12:49
  16. We propose new invariant (the product of the corresponding primes for the ring size of each bond of an atom) as a simple unambiguous ring invariant of an atom that allows distinguishing symmetry classes in the...

    Authors: Dmytro G. Krotko
    Citation: Journal of Cheminformatics 2020 12:48
  17. Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in opti...

    Authors: Tamer N. Jarada, Jon G. Rokne and Reda Alhajj
    Citation: Journal of Cheminformatics 2020 12:46
  18. Mass spectrometry imaging (MSI) has become a mature, widespread analytical technique to perform non-targeted spatial metabolomics. However, the compounds used to promote desorption and ionization of the analyt...

    Authors: Gerard Baquer, Lluc Sementé, María García-Altares, Young Jin Lee, Pierre Chaurand, Xavier Correig and Pere Ràfols
    Citation: Journal of Cheminformatics 2020 12:45
  19. In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. How...

    Authors: Maha A. Thafar, Rawan S. Olayan, Haitham Ashoor, Somayah Albaradei, Vladimir B. Bajic, Xin Gao, Takashi Gojobori and Magbubah Essack
    Citation: Journal of Cheminformatics 2020 12:44
  20. With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant...

    Authors: Xuanyi Li, Yinqiu Xu, Hequan Yao and Kejiang Lin
    Citation: Journal of Cheminformatics 2020 12:42
  21. Affinity fingerprints report the activity of small molecules across a set of assays, and thus permit to gather information about the bioactivities of structurally dissimilar compounds, where models based on ch...

    Authors: Isidro Cortés-Ciriano, Ctibor Škuta, Andreas Bender and Daniel Svozil
    Citation: Journal of Cheminformatics 2020 12:41
  22. Here, we introduce the ChemicalToolbox, a publicly available web server for performing cheminformatics analysis. The ChemicalToolbox provides an intuitive, graphical interface for common tools for downloading,...

    Authors: Simon A. Bray, Xavier Lucas, Anup Kumar and Björn A. Grüning
    Citation: Journal of Cheminformatics 2020 12:40
  23. An affinity fingerprint is the vector consisting of compound’s affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based...

    Authors: C. Škuta, I. Cortés-Ciriano, W. Dehaen, P. Kříž, G. J. P. van Westen, I. V. Tetko, A. Bender and D. Svozil
    Citation: Journal of Cheminformatics 2020 12:39
  24. Molecular generative models trained with small sets of molecules represented as SMILES strings can generate large regions of the chemical space. Unfortunately, due to the sequential nature of SMILES strings, t...

    Authors: Josep Arús-Pous, Atanas Patronov, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen and Ola Engkvist
    Citation: Journal of Cheminformatics 2020 12:38
  25. For kinase inhibitors, X-ray crystallography has revealed different types of binding modes. Currently, more than 2000 kinase inhibitors with known binding modes are available, which makes it possible to derive...

    Authors: Raquel Rodríguez-Pérez, Filip Miljković and Jürgen Bajorath
    Citation: Journal of Cheminformatics 2020 12:36
  26. The development of drugs is often hampered due to off-target interactions leading to adverse effects. Therefore, computational methods to assess the selectivity of ligands are of high interest. Currently, sele...

    Authors: Lindsey Burggraaff, Herman W. T. van Vlijmen, Adriaan P. IJzerman and Gerard J. P. van Westen
    Citation: Journal of Cheminformatics 2020 12:33
  27. Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of...

    Authors: Ryosuke Kojima, Shoichi Ishida, Masateru Ohta, Hiroaki Iwata, Teruki Honma and Yasushi Okuno
    Citation: Journal of Cheminformatics 2020 12:32
  28. The spectroscopy of diatomic molecules is an important research area in chemical physics due to its relevance in astrochemistry, combustion chemistry, and ultracold physics. However, there is currently no data...

    Authors: Xiangyue Liu, Stefan Truppe, Gerard Meijer and Jesús Pérez-Ríos
    Citation: Journal of Cheminformatics 2020 12:31
  29. Aromatic rings are important residues for biological interactions and appear to a large extent as part of protein–drug and protein–protein interactions. They are relevant for both protein stability and molecul...

    Authors: Esteban Lanzarotti, Lucas A. Defelipe, Marcelo A. Marti and Adrián G. Turjanski
    Citation: Journal of Cheminformatics 2020 12:30
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
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