Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.4
4.2
Journals
Molecules
Special Issues
QSAR and QSPR: Recent Developments and Applications, 4th Edition
molecules-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Molecules Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

QSAR and QSPR: Recent Developments and Applications, 4th Edition

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 3257

Special Issue Editors

Dr. Kok Hwa Lim

E-Mail Website
Guest Editor
Food, Chemical and Biotechnology Cluster, Singapore Institute of Technology, Singapore City, Singapore
Interests: computational chemistry and material sciences; heterogeneous catalytic reactions and surface sciences; green chemistry and processes; process safety; QSAR analysis of biological activity
Special Issues, Collections and Topics in MDPI journals
Dr. Bono Lučić

E-Mail Website
Guest Editor
Rudjer Bošković Institute, NMR Centre, Zagreb, Croatia
Interests: QSAR; QSPR; modelling in chemistry and molecular biophysics; development of molecular descriptors; model selection methods; model validation algorithms; classification modelling; antioxidant activity modelling; cheminformatics; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

QSAR modeling is an integral part of rational drug design (RDD). In addition to the prediction of biological activities, QSAR models help to identify the parameters responsible for the biological response that is essential for lead compound optimization. In addition, recent developments in molecular docking have been successful at providing information such as the relative orientation of drug molecules binding to their targeted receptor and leading to the optimization of the lead compound to achieve more potent and selective analogs. Despite the successful application of QSAR to predict biological activities, few QSAR studies have reported on the biological activities of metal complexes, probably due to the lack of specific metal ligand parameters. Recently, the successful use of density functional theory (DFT) and/or machine learning to calculate chemical descriptors of metal complexes has opened up a new era for QSAR studies on metal complexes. This Special Issue of Molecules will consider submissions related to the QSAR of biological activities. For example, the prediction of biological activities of metal complexes or molecular entities may be carried out using physicochemical, steric, topological, and ab initio quantum chemical, pharmacophore mapping, and molecular docking descriptors.

Dr. Kok Hwa Lim
Dr. Bono Lučić
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 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. 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 2700 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

  • ab initio
  • semi-empirical quantum chemical methods
  • topological
  • physicochemical
  • electronic descriptors
  • metal complexes
  • pharmacophore mapping
  • molecular docking
  • lead compound optimization

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Related Special Issues

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 2880 KiB  
Article
QSAR Modeling and Biological Testing of Some 15-LOX Inhibitors in a Series of Homo- and Heterocyclic Compounds
by Veronika Khairullina, Yuliya Martynova, Matvey Kanevsky, Irina Kanevskaya, Yurii Zimin and Leonid Maksimov
Molecules 2024, 29(23), 5540; https://doi.org/10.3390/molecules29235540 - 23 Nov 2024
Viewed by 434
Abstract
This paper examines the quantitative structure–inhibitory activity relationship of 15-lipoxygenase (15-LOX) in sets of 100 homo- and heterocyclic compounds using GUSAR 2019 software. Statistically significant valid models were built to predict the IC50 parameter. A combination of MNA and QNA descriptors with three [...] Read more.
This paper examines the quantitative structure–inhibitory activity relationship of 15-lipoxygenase (15-LOX) in sets of 100 homo- and heterocyclic compounds using GUSAR 2019 software. Statistically significant valid models were built to predict the IC50 parameter. A combination of MNA and QNA descriptors with three whole molecular descriptors (topological length, topological volume and lipophilicity) was used to develop 18 statistically significant, valid consensus QSAR models. These compounds showed varying degrees of inhibition of the catalytic activity of 15-LOX: the range of variation in the pIC50 value was 3.873. The satisfactory coincidence between the theoretically calculated and experimentally determined pIC50 values for compounds TS1, TS2 and 1–8 suggests the potential use of models M1–M18 for the virtual screening of virtual libraries and databases to find new potentially efficient inhibitors of 15-LOX. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 4th Edition)
►▼ Show Figures

Graphical abstract

12 pages, 4152 KiB  
Article
Exploring Molecular Heteroencoders with Latent Space Arithmetic: Atomic Descriptors and Molecular Operators
by Xinyue Gao, Natalia Baimacheva and Joao Aires-de-Sousa
Molecules 2024, 29(16), 3969; https://doi.org/10.3390/molecules29163969 - 22 Aug 2024
Viewed by 883
Abstract
A variational heteroencoder based on recurrent neural networks, trained with SMILES linear notations of molecular structures, was used to derive the following atomic descriptors: delta latent space vectors (DLSVs) obtained from the original SMILES of the whole molecule and the SMILES of the [...] Read more.
A variational heteroencoder based on recurrent neural networks, trained with SMILES linear notations of molecular structures, was used to derive the following atomic descriptors: delta latent space vectors (DLSVs) obtained from the original SMILES of the whole molecule and the SMILES of the same molecule with the target atom replaced. Different replacements were explored, namely, changing the atomic element, replacement with a character of the model vocabulary not used in the training set, or the removal of the target atom from the SMILES. Unsupervised mapping of the DLSV descriptors with t-distributed stochastic neighbor embedding (t-SNE) revealed a remarkable clustering according to the atomic element, hybridization, atomic type, and aromaticity. Atomic DLSV descriptors were used to train machine learning (ML) models to predict 19F NMR chemical shifts. An R2 of up to 0.89 and mean absolute errors of up to 5.5 ppm were obtained for an independent test set of 1046 molecules with random forests or a gradient-boosting regressor. Intermediate representations from a Transformer model yielded comparable results. Furthermore, DLSVs were applied as molecular operators in the latent space: the DLSV of a halogenation (H→F substitution) was summed to the LSVs of 4135 new molecules with no fluorine atom and decoded into SMILES, yielding 99% of valid SMILES, with 75% of the SMILES incorporating fluorine and 56% of the structures incorporating fluorine with no other structural change. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 4th Edition)
►▼ Show Figures

Graphical abstract

27 pages, 21609 KiB  
Article
Quantitative Structure–Activity Relationship in the Series of 5-Ethyluridine, N2-Guanine, and 6-Oxopurine Derivatives with Pronounced Anti-Herpetic Activity
by Veronika Khairullina and Yuliya Martynova
Molecules 2023, 28(23), 7715; https://doi.org/10.3390/molecules28237715 - 22 Nov 2023
Cited by 1 | Viewed by 1136
Abstract
A quantitative analysis of the relationship between the structure and inhibitory activity against the herpes simplex virus thymidine kinase (HSV-TK) was performed for the series of 5-ethyluridine, N2-guanine, and 6-oxopurines derivatives with pronounced anti-herpetic activity (IC50 = 0.09 ÷ 160,000 μmol/L) using [...] Read more.
A quantitative analysis of the relationship between the structure and inhibitory activity against the herpes simplex virus thymidine kinase (HSV-TK) was performed for the series of 5-ethyluridine, N2-guanine, and 6-oxopurines derivatives with pronounced anti-herpetic activity (IC50 = 0.09 ÷ 160,000 μmol/L) using the GUSAR 2019 software. On the basis of the MNA and QNA descriptors and whole-molecule descriptors using the self-consistent regression, 12 statistically significant consensus models for predicting numerical pIC50 values were constructed. These models demonstrated high predictive accuracy for the training and test sets. Molecular fragments of HSV-1 and HSV-2 TK inhibitors that enhance or diminish the anti-herpetic activity are considered. Virtual screening of the ChEMBL database using the developed QSAR models revealed 42 new effective HSV-1 and HSV-2 TK inhibitors. These compounds are promising for further research. The obtained data open up new opportunities for developing novel effective inhibitors of TK. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 4th Edition)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: The use of QSPR towards a better understanding of protein folding
Authors: Bono Lučić
Affiliation: The Ruđer Bošković Institute, Bijenička 54, 10000 Zagreb, Croatia

Back to TopTop