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Special Issue "Chemoinformatics"

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Molecular Diversity".

Deadline for manuscript submissions: closed (15 October 2015)

Special Issue Editor

Guest Editor
Prof. Dr. Peter Willett

Information School, The University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
Website | E-Mail
Interests: bibliometrics, bibliometric methods for the evaluation of research productivity; chemoinformatics, in particular the use of clustering, graph theory, and machine learning methods for the processing of databases of chemical and biological structures

Special Issue Information

Dear Colleagues,

Chemistry is, and has been for many years, one of the most information-rich academic disciplines. The very first journal devoted to chemistry was published as early as 1778, and the literature has grown steadily since then.  Much of the information in chemistry relates to the structures—in both two and three dimensions—of individual chemical molecules; for example, the world’s largest chemical database, the Chemical Registry, produced by Chemical Abstracts Service, now contains the structures of over 90 million distinct molecules, and there are additional millions in other public databases and in the corporate files of pharmaceutical, agrochemical, and biotechnology companies. This wealth of information has spurred the development of a specialist discipline, that of chemoinformatics, which “encompasses the design, creation, organization, storage, management, retrieval, analysis, dissemination, visualization and use of chemical information” [1].

The structure of a molecule is a prime factor in determining its physical, chemical, and biological properties, and chemoinformatics draws on techniques from areas such as graph theory, multivariate statistics, and machine learning to provide sophisticated data mining facilities to correlate such properties with structure. This Special Issue of Molecules welcomes contributions on all aspects of chemoinformatics, such as (but by no means limited to):

  • Virtual Screening (e.g., docking and pharmacophore analysis, similarity and clustering methods, machine learning);
  • Computational methods for lead identification and optimization (e.g., modeling and structure-activity methods, ADMET prediction, de novo design);
  • High-throughput screening (e.g., assay quality control, design of screening collections);
  • New algorithms and technologies (e.g., distributed processing, cloud computing, open source chemoinformatics software, visualization);
  • Emerging applications (e.g., polypharmacology, chemical text mining, target drugability)
  • Case studies of the implementation of any of the above.

The issue welcomes original research articles, work in progress, surveys, reviews, and viewpoint articles.

[1] Warr, W.A. Balancing the needs of the recruiters and the aims of the educators (1999). Paper presented at the 218th American Chemical Society National Meeting, New Orleans, USA.

Prof. Dr. Peter Willett
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • activity cliffs
  • CADD
  • CAMD
  • chemical data mining
  • chemical text mining
  • computer-aided drug discovery
  • computer-aided molecular design
  • computer-aided synthesis design
  • docking
  • ligand-base virtual screening
  • molecular diversity analysis
  • molecular similarity
  • pharmacophore mapping
  • property prediction
  • similarity searching
  • structure-based virtual screening
  • substructure searching

Related Special Issue

Published Papers (12 papers)

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Editorial

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Open AccessEditorial Special Issue: Chemoinformatics
Molecules 2016, 21(4), 535; doi:10.3390/molecules21040535
Received: 19 April 2016 / Accepted: 20 April 2016 / Published: 22 April 2016
PDF Full-text (146 KB) | HTML Full-text | XML Full-text
Abstract
Chemoinformatics techniques were originally developed for the construction and searching of large archives of chemical structures but they were soon applied to problems in drug discovery and are now playing an increasingly important role in many additional areas of chemistry. This Special Issue
[...] Read more.
Chemoinformatics techniques were originally developed for the construction and searching of large archives of chemical structures but they were soon applied to problems in drug discovery and are now playing an increasingly important role in many additional areas of chemistry. This Special Issue contains seven original research articles and four review articles that provide an introduction to several aspects of this rapidly developing field. Full article
(This article belongs to the Special Issue Chemoinformatics)

Research

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Open AccessArticle Nanopore Event-Transduction Signal Stabilization for Wide pH Range under Extreme Chaotrope Conditions
Molecules 2016, 21(3), 346; doi:10.3390/molecules21030346
Received: 10 February 2016 / Revised: 2 March 2016 / Accepted: 2 March 2016 / Published: 14 March 2016
Cited by 1 | PDF Full-text (3290 KB) | HTML Full-text | XML Full-text
Abstract
Operation of an α-hemolysin nanopore transduction detector is found to be surprisingly robust over a critical range of pH (6–9), including physiological pH = 7.4 and polymerase chain reaction (PCR) pH = 8.4, and extreme chaotrope concentration, including 5 M urea. The engineered
[...] Read more.
Operation of an α-hemolysin nanopore transduction detector is found to be surprisingly robust over a critical range of pH (6–9), including physiological pH = 7.4 and polymerase chain reaction (PCR) pH = 8.4, and extreme chaotrope concentration, including 5 M urea. The engineered transducer molecule that is captured in the standard α-hemolysin nanopore detector, to transform it into a transduction detector, appears to play a central role in this stabilization process by stabilizing the channel against gating during its capture. This enables the nanopore transduction detector to operate as a single molecule “nanoscope” in a wide range of conditions, where tracking on molecular state is possible in a variety of different environmental conditions. In the case of streptavidin biosensing, results are shown for detector operation when in the presence of extreme (5 M) urea concentration. Complications involving degenerate states are encountered at higher chaotrope concentrations, but since the degeneracy is only of order two, this is easily absorbed into the classification task as in prior work. This allows useful detector operation over a wide range of conditions relevant to biochemistry, biomedical engineering, and biotechnology. Full article
(This article belongs to the Special Issue Chemoinformatics)
Open AccessArticle Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds
Molecules 2016, 21(1), 1; doi:10.3390/molecules21010001
Received: 29 October 2015 / Revised: 9 December 2015 / Accepted: 15 December 2015 / Published: 23 December 2015
Cited by 7 | PDF Full-text (633 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The article describes a classification system termed “extended functional groups” (EFG), which are an extension of a set previously used by the CheckMol software, that covers in addition heterocyclic compound classes and periodic table groups. The functional groups are defined as SMARTS patterns
[...] Read more.
The article describes a classification system termed “extended functional groups” (EFG), which are an extension of a set previously used by the CheckMol software, that covers in addition heterocyclic compound classes and periodic table groups. The functional groups are defined as SMARTS patterns and are available as part of the ToxAlerts tool (http://ochem.eu/alerts) of the On-line CHEmical database and Modeling (OCHEM) environment platform. The article describes the motivation and the main ideas behind this extension and demonstrates that EFG can be efficiently used to develop and interpret structure-activity relationship models. Full article
(This article belongs to the Special Issue Chemoinformatics)
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Open AccessArticle Extremely Randomized Machine Learning Methods for Compound Activity Prediction
Molecules 2015, 20(11), 20107-20117; doi:10.3390/molecules201119679
Received: 14 August 2015 / Revised: 14 August 2015 / Accepted: 27 October 2015 / Published: 9 November 2015
Cited by 1 | PDF Full-text (948 KB) | HTML Full-text | XML Full-text
Abstract
Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of
[...] Read more.
Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure. Full article
(This article belongs to the Special Issue Chemoinformatics)
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Open AccessArticle A Quantum-Based Similarity Method in Virtual Screening
Molecules 2015, 20(10), 18107-18127; doi:10.3390/molecules201018107
Received: 26 August 2015 / Revised: 22 September 2015 / Accepted: 23 September 2015 / Published: 2 October 2015
Cited by 5 | PDF Full-text (741 KB) | HTML Full-text | XML Full-text
Abstract
One of the most widely-used techniques for ligand-based virtual screening is similarity searching. This study adopted the concepts of quantum mechanics to present as state-of-the-art similarity method of molecules inspired from quantum theory. The representation of molecular compounds in mathematical quantum space plays
[...] Read more.
One of the most widely-used techniques for ligand-based virtual screening is similarity searching. This study adopted the concepts of quantum mechanics to present as state-of-the-art similarity method of molecules inspired from quantum theory. The representation of molecular compounds in mathematical quantum space plays a vital role in the development of quantum-based similarity approach. One of the key concepts of quantum theory is the use of complex numbers. Hence, this study proposed three various techniques to embed and to re-represent the molecular compounds to correspond with complex numbers format. The quantum-based similarity method that developed in this study depending on complex pure Hilbert space of molecules called Standard Quantum-Based (SQB). The recall of retrieved active molecules were at top 1% and top 5%, and significant test is used to evaluate our proposed methods. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints. Simulated virtual screening experiment show that the effectiveness of SQB method was significantly increased due to the role of representational power of molecular compounds in complex numbers forms compared to Tanimoto benchmark similarity measure. Full article
(This article belongs to the Special Issue Chemoinformatics)
Open AccessArticle A Database of Force-Field Parameters, Dynamics, and Properties of Antimicrobial Compounds
Molecules 2015, 20(8), 13997-14021; doi:10.3390/molecules200813997
Received: 11 June 2015 / Accepted: 28 July 2015 / Published: 3 August 2015
Cited by 11 | PDF Full-text (2521 KB) | HTML Full-text | XML Full-text
Abstract
We present an on-line database of all-atom force-field parameters and molecular properties of compounds with antimicrobial activity (mostly antibiotics and some beta-lactamase inhibitors). For each compound, we provide the General Amber Force Field parameters for the major species at physiological pH, together with
[...] Read more.
We present an on-line database of all-atom force-field parameters and molecular properties of compounds with antimicrobial activity (mostly antibiotics and some beta-lactamase inhibitors). For each compound, we provide the General Amber Force Field parameters for the major species at physiological pH, together with an analysis of properties of interest as extracted from µs-long molecular dynamics simulations in explicit water solution. The properties include number and population of structural clusters, molecular flexibility, hydrophobic and hydrophilic molecular surfaces, the statistics of intraand inter-molecular H-bonds, as well as structural and dynamical properties of solvent molecules within first and second solvation shells. In addition, the database contains several key molecular parameters, such as energy of the frontier molecular orbitals, vibrational properties, rotational constants, atomic partial charges and electric dipole moment, computed by Density Functional Theory. The present database (to our knowledge the first extensive one including dynamical properties) is part of a wider project aiming to build-up a database containing structural, physico-chemical and dynamical properties of medicinal compounds using different force-field parameters with increasing level of complexity and reliability. The database is freely accessible at http://www.dsf.unica.it/translocation/db/. Full article
(This article belongs to the Special Issue Chemoinformatics)
Open AccessArticle Three-Dimensional Compound Comparison Methods and Their Application in Drug Discovery
Molecules 2015, 20(7), 12841-12862; doi:10.3390/molecules200712841
Received: 6 May 2015 / Revised: 7 July 2015 / Accepted: 13 July 2015 / Published: 16 July 2015
Cited by 9 | PDF Full-text (1693 KB) | HTML Full-text | XML Full-text
Abstract
Virtual screening has been widely used in the drug discovery process. Ligand-based virtual screening (LBVS) methods compare a library of compounds with a known active ligand. Two notable advantages of LBVS methods are that they do not require structural information of a target
[...] Read more.
Virtual screening has been widely used in the drug discovery process. Ligand-based virtual screening (LBVS) methods compare a library of compounds with a known active ligand. Two notable advantages of LBVS methods are that they do not require structural information of a target receptor and that they are faster than structure-based methods. LBVS methods can be classified based on the complexity of ligand structure information utilized: one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D). Unlike 1D and 2D methods, 3D methods can have enhanced performance since they treat the conformational flexibility of compounds. In this paper, a number of 3D methods will be reviewed. In addition, four representative 3D methods were benchmarked to understand their performance in virtual screening. Specifically, we tested overall performance in key aspects including the ability to find dissimilar active compounds, and computational speed. Full article
(This article belongs to the Special Issue Chemoinformatics)
Open AccessArticle Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest
Molecules 2015, 20(6), 10947-10962; doi:10.3390/molecules200610947
Received: 13 March 2015 / Revised: 4 June 2015 / Accepted: 9 June 2015 / Published: 12 June 2015
Cited by 9 | PDF Full-text (962 KB) | HTML Full-text | XML Full-text
Abstract
Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support
[...] Read more.
Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality. Full article
(This article belongs to the Special Issue Chemoinformatics)
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Review

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Open AccessReview Chemoinformatics: Achievements and Challenges, a Personal View
Molecules 2016, 21(2), 151; doi:10.3390/molecules21020151
Received: 20 November 2015 / Revised: 14 January 2016 / Accepted: 20 January 2016 / Published: 27 January 2016
Cited by 10 | PDF Full-text (1065 KB) | HTML Full-text | XML Full-text
Abstract
Chemoinformatics provides computer methods for learning from chemical data and for modeling tasks a chemist is facing. The field has evolved in the past 50 years and has substantially shaped how chemical research is performed by providing access to chemical information on a
[...] Read more.
Chemoinformatics provides computer methods for learning from chemical data and for modeling tasks a chemist is facing. The field has evolved in the past 50 years and has substantially shaped how chemical research is performed by providing access to chemical information on a scale unattainable by traditional methods. Many physical, chemical and biological data have been predicted from structural data. For the early phases of drug design, methods have been developed that are used in all major pharmaceutical companies. However, all domains of chemistry can benefit from chemoinformatics methods; many areas that are not yet well developed, but could substantially gain from the use of chemoinformatics methods. The quality of data is of crucial importance for successful results. Computer-assisted structure elucidation and computer-assisted synthesis design have been attempted in the early years of chemoinformatics. Because of the importance of these fields to the chemist, new approaches should be made with better hardware and software techniques. Society’s concern about the impact of chemicals on human health and the environment could be met by the development of methods for toxicity prediction and risk assessment. In conjunction with bioinformatics, our understanding of the events in living organisms could be deepened and, thus, novel strategies for curing diseases developed. With so many challenging tasks awaiting solutions, the future is bright for chemoinformatics. Full article
(This article belongs to the Special Issue Chemoinformatics)
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Open AccessFeature PaperReview Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases
Molecules 2015, 20(12), 22799-22832; doi:10.3390/molecules201219880
Received: 19 November 2015 / Revised: 3 December 2015 / Accepted: 9 December 2015 / Published: 19 December 2015
Cited by 14 | PDF Full-text (6349 KB) | HTML Full-text | XML Full-text
Abstract
Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions
[...] Read more.
Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies. Full article
(This article belongs to the Special Issue Chemoinformatics)
Open AccessReview Recent Advances in Developing Inhibitors for Hypoxia-Inducible Factor Prolyl Hydroxylases and Their Therapeutic Implications
Molecules 2015, 20(11), 20551-20568; doi:10.3390/molecules201119717
Received: 14 October 2015 / Revised: 10 November 2015 / Accepted: 11 November 2015 / Published: 19 November 2015
Cited by 15 | PDF Full-text (3316 KB) | HTML Full-text | XML Full-text
Abstract
Hypoxia-inducible factor (HIF) prolyl hydroxylases (PHDs) are members of the 2-oxoglutarate dependent non-heme iron dioxygenases. Due to their physiological roles in regulation of HIF-1α stability, many efforts have been focused on searching for selective PHD inhibitors to control HIF-1α levels for therapeutic applications.
[...] Read more.
Hypoxia-inducible factor (HIF) prolyl hydroxylases (PHDs) are members of the 2-oxoglutarate dependent non-heme iron dioxygenases. Due to their physiological roles in regulation of HIF-1α stability, many efforts have been focused on searching for selective PHD inhibitors to control HIF-1α levels for therapeutic applications. In this review, we first describe the structure of PHD2 as a molecular basis for structure-based drug design (SBDD) and various experimental methods developed for measuring PHD activity. We further discuss the current status of the development of PHD inhibitors enabled by combining SBDD approaches with high-throughput screening. Finally, we highlight the clinical implications of small molecule PHD inhibitors. Full article
(This article belongs to the Special Issue Chemoinformatics)

Other

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Open AccessOpinion Chemoinformatics in the New Era: From Molecular Dynamics to Systems Dynamics
Molecules 2016, 21(3), 71; doi:10.3390/molecules21030071
Received: 23 November 2015 / Revised: 22 December 2015 / Accepted: 5 January 2016 / Published: 3 March 2016
Cited by 1 | PDF Full-text (310 KB) | HTML Full-text | XML Full-text
Abstract
Chemoinformatics, due to its power in gathering information at the molecular level, has a wide array of important applications to biology, including fundamental biochemical studies and drug discovery and optimization. As modern “omics” based profiling and network based modeling and simulation techniques grow
[...] Read more.
Chemoinformatics, due to its power in gathering information at the molecular level, has a wide array of important applications to biology, including fundamental biochemical studies and drug discovery and optimization. As modern “omics” based profiling and network based modeling and simulation techniques grow in sophistication, chemoinformatics now faces a great opportunity to include systems-level control mechanisms as one of its pillar components to extend and refine its various applications. This viewpoint article, through the example of computer aided targeting of the PI3K/Akt/mTOR pathway, outlines major steps of integrating systems dynamics simulations into molecular dynamics simulations to facilitate a higher level of chemoinformatics that would revolutionize drug lead optimization, personalized therapy, and possibly other applications. Full article
(This article belongs to the Special Issue Chemoinformatics)
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