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Special Issue "Computational Chemical Biology"

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

Deadline for manuscript submissions: 30 June 2019

Special Issue Editors

Guest Editor
Assoc. Prof. Dr. J. B. Brown

Life Science Informatics Research Unit, Laboratory for Molecular Biosciences, Kyoto University Graduate School of Medicine, Kyoto, Sakyo, Japan
Website | E-Mail
Interests: computational chemical biology; computational drug discovery; clinical informatics; translational life science informatics
Guest Editor
Prof. Dr. Jürgen Bajorath

Department of Life Science Informatics, B‐IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich‐Wilhelms‐Universität, Bonn, Germany
Website | E-Mail
Interests: Chemoinformatics; medicinal chemistry; chemical biology; drug design; drug discovery

Special Issue Information

Dear Colleagues,

The biological screening of compound collections continues to provide many new active chemical entities for further consideration. Large-magnitude screening campaigns typically require computational support for data analysis and the selection of preferred hits for follow-up studies. Here, the requirements for lead-like molecules in medicinal chemistry and probe-like compounds in chemical biology differ. Furthermore, the experimental studies must often be further extended through computational means, for example to identify analogues of interesting active compounds or map currently known targets. To these ends, computational chemical biology is tasked with delivering interpretation and prediction tools with significant potential to complement experimental investigations. In this Special Issue, we are seeking contributions focusing on new computational methodologies, practical solutions, and perspectives with immediate relevance for chemical biology. Papers on diverse sub-topics such as, for example, molecular structure–selectivity analysis, single- and multi-target assay data exploration, or bioactivity modelling through artificial intelligence approaches including, but not limited to, machine learning are welcome.

Manuscripts are ideally written to include both experimental and computational viewpoints. In addition, papers reporting new computational concepts for chemical biology are highly desired.

Assoc. Prof. Dr. J. B. Brown
Prof. Dr. Jürgen Bajorath
Guest Editors

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 semimonthly 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

  • Bioactivity assay analysis
  • Computational structure–activity relationship
  • High-throughput screening
  • Pattern recognition/machine learning

Published Papers (2 papers)

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Research

Open AccessArticle
An In Silico Approach for Assessment of the Membrane Transporter Activities of Phenols: A Case Study Based on Computational Models of Transport Activity for the Transporter Bilitranslocase
Molecules 2019, 24(5), 837; https://doi.org/10.3390/molecules24050837
Received: 1 February 2019 / Revised: 19 February 2019 / Accepted: 26 February 2019 / Published: 27 February 2019
PDF Full-text (2211 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Phenols are the most abundant naturally accessible antioxidants present in a human normal diet. Since numerous beneficial applications of phenols as preventive agents in various diseases were revealed, the evaluation of phenols bioavailability is of high interest of researchers, consumers and drug manufacturers. [...] Read more.
Phenols are the most abundant naturally accessible antioxidants present in a human normal diet. Since numerous beneficial applications of phenols as preventive agents in various diseases were revealed, the evaluation of phenols bioavailability is of high interest of researchers, consumers and drug manufacturers. The hydrophilic nature of phenols makes a cell membrane penetration difficult, which imply an alternative way of uptake via membrane transporters. However, the structural and functional data of membrane transporters are limited, thus the in silico modelling is really challenging and urgent tool in elucidation of transporter ligands. Focus of this research was a particular transporter bilitranslocase (BTL). BTL has a broad tissue expression (vascular endothelium, absorptive and excretory epithelia) and can transport wide variety of poly-aromatic compounds. With available BTL data (pKi [mmol/L] for 120 organic compounds) a robust and reliable QSAR models for BTL transport activity were developed and extrapolated on 300 phenolic compounds. For all compounds the transporter profiles were assessed and results show that dietary phenols and some drug candidates are likely to interact with BTL. Moreover, synopsis of predictions from BTL models and hits/predictions of 20 transporters from Metrabase and Chembench platforms were revealed. With such joint transporter analyses a new insights for elucidation of BTL functional role were acquired. Regarding limitation of models for virtual profiling of transporter interactions the computational approach reported in this study could be applied for further development of reliable in silico models for any transporter, if in vitro experimental data are available. Full article
(This article belongs to the Special Issue Computational Chemical Biology)
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Open AccessFeature PaperArticle
Data-Driven Exploration of Selectivity and Off-Target Activities of Designated Chemical Probes
Molecules 2018, 23(10), 2434; https://doi.org/10.3390/molecules23102434
Received: 31 August 2018 / Revised: 19 September 2018 / Accepted: 21 September 2018 / Published: 23 September 2018
Cited by 1 | PDF Full-text (3225 KB) | HTML Full-text | XML Full-text
Abstract
Chemical probes are of central relevance for chemical biology. To unambiguously explore the role of target proteins in triggering or mediating biological functions, small molecules used as probes should ideally be target-specific; at least, they should have sufficiently high selectivity for a primary [...] Read more.
Chemical probes are of central relevance for chemical biology. To unambiguously explore the role of target proteins in triggering or mediating biological functions, small molecules used as probes should ideally be target-specific; at least, they should have sufficiently high selectivity for a primary target. We present a thorough analysis of currently available activity data for designated chemical probes to address several key questions: How well defined are chemical probes? What is their level of selectivity? Is there evidence for additional activities? Are some probes “better” than others? Therefore, highly curated chemical probes were collected and their selectivity was analyzed on the basis of publicly available compound activity data. Different selectivity patterns were observed, which distinguished designated high-quality probes. Full article
(This article belongs to the Special Issue Computational Chemical Biology)
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