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Advances in Cheminformatics and Nanoinformatics
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Advances in Cheminformatics and Nanoinformatics

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 4031

Special Issue Editors

Dr. Antreas Afantitis

E-Mail Website
Guest Editor
NovaMechanics Ltd, Cheminformatics & Nanoinformatics Excellence, Digeni Akrita 51, 1070 Nicosia, Cyprus
Interests: cheminformatics; nanoinformatics; virtual screening; QSAR; computer aided drug design; computational toxicology; computational nanotoxicology
Special Issues, Collections and Topics in MDPI journals
Dr. Georgia Melagraki

E-Mail Website
Co-Guest Editor
Hellenic Military Academy, P.O. Box 16673 Vari, Greece
Interests: cheminformatics; nanoinformatics; machine learning; in silico drug discovery; in silico toxicology; risk assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We particularly take an interest in manuscripts that combine experimental and cheminformatics/nanoinformatics approaches to delineate computational toxicology and nanosafety issues. Reviews that summarize results are also welcome. Papers dealing with the development of computational methodologies and concepts are of great interest.

Potential topics include, but are not limited to, the following:

  • nano-QSAR (nanomaterials modelling/QNAR)
  • Nanoinformatics with an emphasis on toxicology
  • The prediction of toxicity, metabolism, fate, and physico-chemical properties
  • Data mining for the identification of new leads with reduced toxicity
  • Big Data in toxicology: integration, management, and analysis
  • QSAR/QSPR with an emphasis on toxicology
  • In Silico lead identification and the optimization of toxicity
  • Web applications for computational toxicology problems (web services, apps, etc.)

Dr. Antreas Afantitis
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 submissions that pass pre-check are 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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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.

Published Papers (1 paper)

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Research

15 pages, 3291 KiB  
Article
Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR Models
by Peng Zhu, Xuejing Kang, Yongsheng Zhao, Ullah Latif and Hongzhong Zhang
Int. J. Mol. Sci. 2019, 20(9), 2186; https://doi.org/10.3390/ijms20092186 - 02 May 2019
Cited by 34 | Viewed by 3538
Abstract
Limited information on the potential toxicity of ionic liquids (ILs) becomes the bottleneck that creates a barrier in their large-scale application. In this work, two quantitative structure-activity relationships (QSAR) models were used to evaluate the toxicity of ILs toward the acetylcholinesterase enzyme using [...] Read more.
Limited information on the potential toxicity of ionic liquids (ILs) becomes the bottleneck that creates a barrier in their large-scale application. In this work, two quantitative structure-activity relationships (QSAR) models were used to evaluate the toxicity of ILs toward the acetylcholinesterase enzyme using multiple linear regression (MLR) and extreme learning machine (ELM) algorithms. The structures of 57 cations and 21 anions were optimized using quantum chemistry calculations. The electrostatic potential surface area (SEP) and the screening charge density distribution area (Sσ) descriptors were calculated and used for prediction of IL toxicity. Performance and predictive aptitude between MLR and ELM models were analyzed. Highest squared correlation coefficient (R2), and also lowest average absolute relative deviation (AARD%) and root-mean-square error (RMSE) were observed for training set, test set, and total set for the ELM model. These findings validated the superior performance of ELM over the MLR toxicity prediction model. Full article
(This article belongs to the Special Issue Advances in Cheminformatics and Nanoinformatics)
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