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QSAR and Chemoinformatics in Molecular Modeling and Drug Design 4.0
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QSAR and Chemoinformatics in Molecular Modeling and Drug Design 4.0

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 2022) | Viewed by 11616

Special Issue Editor

Prof. Dr. Hanoch Senderowitz

E-Mail Website
Guest Editor
Department of Chemistry, Bar-Ilan University, Ramat-Gan 5290002, Israel
Interests: chemoinformatics; quantitative structure activity relationship (QSAR); molecular modeling; computer aided molecular design; computational chemistry; materials informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Chemoinformatics is a multidisciplinary area of research primarily engaged with the collection, deposition, retrieval, and analysis of information in order to address chemistry-related problems. The analysis of chemistry-related data can take many forms, one of the most important being quantitative structure–activity relationship (QSAR). QSAR can be broadly defined as the use of mathematical models to find correlations between molecular activities (defined in the broadest possible sense) and a set of structure-based descriptors. Starting with early studies by Hansch and co-workers, the field has rapidly evolved by introducing many significant advances, including data curation, descriptor calculation, regression algorithms, and evaluation metrics. Over the years, QSAR models have been widely and successfully used in many research areas, including chemistry, biology, toxicology, and material sciences, to both analyze the factors affecting molecular properties and design new compounds.

The purpose of this Special Issue is to provide an overview of the state of the art in current chemoinformatics methodologies, with an emphasis on QSAR, and to describe how these methodologies are used in molecular modeling and drug design. We welcome original research articles, review articles, and short communications on one or more of the following topics:

  • development, implementation, and application of chemoinformatics databases;
  • development and application of new chemoinformatics tools;
  • development and application of new molecular descriptors;
  • construction and visualization of and navigation through the chemical space;
  • development of new QSAR algorithms and workflows; and
  • application of chemoinformatics and QSAR methodologies in molecular modeling and drug design.

We hope that this Special Issue will serve as an entry point for newcomers into the exciting world of chemoinformatics/QSAR as well as a valuable reference for more experienced practitioners in the field.

Prof. Dr. Hanoch Senderowitz
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.

Related Special Issues

Published Papers (6 papers)

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Research

26 pages, 2735 KiB  
Article
Assessing How Residual Errors of Scoring Functions Correlate to Ligand Structural Features
by Dmitry A. Shulga, Arslan R. Shaimardanov, Nikita N. Ivanov and Vladimir A. Palyulin
Int. J. Mol. Sci. 2022, 23(23), 15018; https://doi.org/10.3390/ijms232315018 - 30 Nov 2022
Cited by 3 | Viewed by 1769
Abstract
Scoring functions (SFs) are ubiquitous tools for early stage drug discovery. However, their accuracy currently remains quite moderate. Despite a number of successful target-specific SFs appearing recently, up until now, no ideas on how to systematically improve the general scope of SFs have [...] Read more.
Scoring functions (SFs) are ubiquitous tools for early stage drug discovery. However, their accuracy currently remains quite moderate. Despite a number of successful target-specific SFs appearing recently, up until now, no ideas on how to systematically improve the general scope of SFs have been formulated. In this work, we hypothesized that the specific features of ligands, corresponding to interactions well appreciated by medicinal chemists (e.g., hydrogen bonds, hydrophobic and aromatic interactions), might be responsible, in part, for the remaining SF errors. The latter provides direction to efforts aimed at the rational and systematic improvement of SF accuracy. In this proof-of-concept work, we took a CASF-2016 coreset of 285 ligands as a basis for comparison and calculated the values of scores for a representative panel of SFs (including AutoDock 4.2, AutoDock Vina, X-Score, NNScore2.0, ΔVina RF20, and DSX). The residual error of linear correlation of each SF value, with the experimental values of affinity and activity, was then analyzed in terms of its correlation with the presence of the fragments responsible for certain medicinal chemistry defined interactions. We showed that, despite the fact that SFs generally perform reasonably, there is room for improvement in terms of better parameterization of interactions involving certain fragments in ligands. Thus, this approach opens a potential way for the systematic improvement of SFs without their significant complication. However, the straightforward application of the proposed approach is limited by the scarcity of reliable available data for ligand–receptor complexes, which is a common problem in the field. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 4.0)
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16 pages, 27998 KiB  
Article
Hierarchical Clustering and Target-Independent QSAR for Antileishmanial Oxazole and Oxadiazole Derivatives
by Henrique R. Teles, Leonardo L. G. Ferreira, Marilia Valli, Fernando Coelho and Adriano D. Andricopulo
Int. J. Mol. Sci. 2022, 23(16), 8898; https://doi.org/10.3390/ijms23168898 - 10 Aug 2022
Cited by 2 | Viewed by 1259
Abstract
Leishmaniasis is a neglected tropical disease that kills more than 20,000 people each year. The chemotherapy available for the treatment of the disease is limited, and novel approaches to discover novel drugs are urgently needed. Herein, 2D- and 4D-quantitative structure–activity relationship (QSAR) models [...] Read more.
Leishmaniasis is a neglected tropical disease that kills more than 20,000 people each year. The chemotherapy available for the treatment of the disease is limited, and novel approaches to discover novel drugs are urgently needed. Herein, 2D- and 4D-quantitative structure–activity relationship (QSAR) models were developed for a series of oxazole and oxadiazole derivatives that are active against Leishmania infantum, the causative agent of visceral leishmaniasis. A clustering strategy based on structural similarity was applied with molecular fingerprints to divide the complete set of compounds into two groups. Hierarchical clustering was followed by the development of 2D- (R2 = 0.90, R2pred = 0.82) and 4D-QSAR models (R2 = 0.80, R2pred = 0.64), which showed improved statistical robustness and predictive ability. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 4.0)
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17 pages, 6769 KiB  
Article
Binding Studies and Lead Generation of Pteridin-7(8H)-one Derivatives Targeting FLT3
by Suparna Ghosh and Seung Joo Cho
Int. J. Mol. Sci. 2022, 23(14), 7696; https://doi.org/10.3390/ijms23147696 - 12 Jul 2022
Cited by 2 | Viewed by 1545
Abstract
Ligand modification by substituting chemical groups within the binding pocket is a popular strategy for kinase drug development. In this study, a series of pteridin-7(8H)-one derivatives targeting wild-type FMS-like tyrosine kinase-3 (FLT3) and its D835Y mutant (FL3D835Y) were studied [...] Read more.
Ligand modification by substituting chemical groups within the binding pocket is a popular strategy for kinase drug development. In this study, a series of pteridin-7(8H)-one derivatives targeting wild-type FMS-like tyrosine kinase-3 (FLT3) and its D835Y mutant (FL3D835Y) were studied using a combination of molecular modeling techniques, such as docking, molecular dynamics (MD), binding energy calculation, and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies. We determined the protein–ligand binding affinity by employing molecular mechanics Poisson–Boltzmann/generalized Born surface area (MM-PB/GBSA), fast pulling ligand (FPL) simulation, linear interaction energy (LIE), umbrella sampling (US), and free energy perturbation (FEP) scoring functions. The structure–activity relationship (SAR) study was conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and the results were emphasized as a SAR scheme. In both the CoMFA and CoMSIA models, satisfactory correlation statistics were obtained between the observed and predicted inhibitory activity. The MD and SAR models were co-utilized to design several new compounds, and their inhibitory activities were anticipated using the CoMSIA model. The designed compounds with higher predicted pIC50 values than the most active compound were carried out for binding free energy evaluation to wild-type and mutant receptors using MM-PB/GBSA, LIE, and FEP methods. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 4.0)
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19 pages, 8534 KiB  
Article
HATS5m as an Example of GETAWAY Molecular Descriptor in Assessing the Similarity/Diversity of the Structural Features of 4-Thiazolidinone
by Mariusz Zapadka, Przemysław Dekowski and Bogumiła Kupcewicz
Int. J. Mol. Sci. 2022, 23(12), 6576; https://doi.org/10.3390/ijms23126576 - 12 Jun 2022
Viewed by 1618
Abstract
Among the various methods for drug design, the approach using molecular descriptors for quantitative structure–activity relationships (QSAR) bears promise for the prediction of innovative molecular structures with bespoke pharmacological activity. Despite the growing number of successful potential applications, the QSAR models often remain [...] Read more.
Among the various methods for drug design, the approach using molecular descriptors for quantitative structure–activity relationships (QSAR) bears promise for the prediction of innovative molecular structures with bespoke pharmacological activity. Despite the growing number of successful potential applications, the QSAR models often remain hard to interpret. The difficulty arises from the use of advanced chemometric or machine learning methods on the one hand, and the complexity of molecular descriptors on the other hand. Thus, there is a need to interpret molecular descriptors for identifying the features of molecules crucial for desirable activity. For example, the development of structure–activity modeling of different molecule endpoints confirmed the usefulness of H-GETAWAY (H-GEometry, Topology, and Atom-Weights AssemblY) descriptors in molecular sciences. However, compared with other 3D molecular descriptors, H-GETAWAY interpretation is much more complicated. The present study provides insights into the interpretation of the HATS5m descriptor (H-GETAWAY) concerning the molecular structures of the 4-thiazolidinone derivatives with antitrypanosomal activity. According to the published study, an increase in antitrypanosomal activity is associated with both a decrease and an increase in HATS5m (leverage-weighted autocorrelation with lag 5, weighted by atomic masses) values. The substructure-based method explored how the changes in molecular features affect the HATS5m value. Based on this approach, we proposed substituents that translate into low and high HATS5m. The detailed interpretation of H-GETAWAY descriptors requires the consideration of three elements: weighting scheme, leverages, and the Dirac delta function. Particular attention should be paid to the impact of chemical compounds’ size and shape and the leverage values of individual atoms. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 4.0)
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30 pages, 11997 KiB  
Article
Design, Synthesis, Molecular Docking Analysis and Biological Evaluations of 4-[(Quinolin-4-yl)amino]benzamide Derivatives as Novel Anti-Influenza Virus Agents
by Chao Zhang, Yun-Sang Tang, Chu-Ren Meng, Jing Xu, De-Liang Zhang, Jian Wang, Er-Fang Huang, Pang-Chui Shaw and Chun Hu
Int. J. Mol. Sci. 2022, 23(11), 6307; https://doi.org/10.3390/ijms23116307 - 4 Jun 2022
Cited by 4 | Viewed by 2390
Abstract
In this study, a series of 4-[(quinolin-4-yl)amino]benzamide derivatives as the novel anti-influenza agents were designed and synthesized. Cytotoxicity assay, cytopathic effect assay and plaque inhibition assay were performed to evaluate the anti-influenza virus A/WSN/33 (H1N1) activity of the target compounds. The target compound [...] Read more.
In this study, a series of 4-[(quinolin-4-yl)amino]benzamide derivatives as the novel anti-influenza agents were designed and synthesized. Cytotoxicity assay, cytopathic effect assay and plaque inhibition assay were performed to evaluate the anti-influenza virus A/WSN/33 (H1N1) activity of the target compounds. The target compound G07 demonstrated significant anti-influenza virus A/WSN/33 (H1N1) activity both in cytopathic effect assay (EC50 = 11.38 ± 1.89 µM) and plaque inhibition assay (IC50 = 0.23 ± 0.15 µM). G07 also exhibited significant anti-influenza virus activities against other three different influenza virus strains A/PR/8 (H1N1), A/HK/68 (H3N2) and influenza B virus. According to the result of ribonucleoprotein reconstitution assay, G07 could interact well with ribonucleoprotein with an inhibition rate of 80.65% at 100 µM. Furthermore, G07 exhibited significant activity target PA−PB1 subunit of RNA polymerase according to the PA−PB1 inhibitory activity prediction by the best pharmacophore Hypo1. In addition, G07 was well drug-likeness based on the results of Lipinski’s rule and ADMET prediction. All the results proved that 4-[(quinolin-4-yl)amino]benzamide derivatives could generate potential candidates in discovery of anti-influenza virus agents. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 4.0)
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11 pages, 1467 KiB  
Article
Molecular Similarity Perception Based on Machine-Learning Models
by Enrico Gandini, Gilles Marcou, Fanny Bonachera, Alexandre Varnek, Stefano Pieraccini and Maurizio Sironi
Int. J. Mol. Sci. 2022, 23(11), 6114; https://doi.org/10.3390/ijms23116114 - 30 May 2022
Viewed by 2356
Abstract
Molecular similarity is an impressively broad topic with many implications in several areas of chemistry. Its roots lie in the paradigm that ‘similar molecules have similar properties’. For this reason, methods for determining molecular similarity find wide application in pharmaceutical companies, e.g., in [...] Read more.
Molecular similarity is an impressively broad topic with many implications in several areas of chemistry. Its roots lie in the paradigm that ‘similar molecules have similar properties’. For this reason, methods for determining molecular similarity find wide application in pharmaceutical companies, e.g., in the context of structure-activity relationships. The similarity evaluation is also used in the field of chemical legislation, specifically in the procedure to judge if a new molecule can obtain the status of orphan drug with the consequent financial benefits. For this procedure, the European Medicines Agency uses experts’ judgments. It is clear that the perception of the similarity depends on the observer, so the development of models to reproduce the human perception is useful. In this paper, we built models using both 2D fingerprints and 3D descriptors, i.e., molecular shape and pharmacophore descriptors. The proposed models were also evaluated by constructing a dataset of pairs of molecules which was submitted to a group of experts for the similarity judgment. The proposed machine-learning models can be useful to reduce or assist human efforts in future evaluations. For this reason, the new molecules dataset and an online tool for molecular similarity estimation have been made freely available. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 4.0)
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