Submit to IJMS Review for IJMS Propose a Special Issue

Journal Menu

Journal Browser

QSAR and Chemoinformatics in Molecular Modeling and Drug Design 2.0

Special Issue Editors
Special Issue Information
Keywords
Related Special Issues
Published Papers

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 (28 February 2021) | Viewed by 27952

Share This Special Issue

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:

(1) development, implementation, and application of chemoinformatics databases;

(2) development and application of new chemoinformatics tools;

(3) development and application of new molecular descriptors;

(4) construction and visualization of and navigation through the chemical space;

(5) development of new QSAR algorithms and workflows; and

(6) 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.

Keywords

Chemoinformatics
Machine learning
Data mining
Quantitative structure activity relationship (QSAR)
Quantitative structure property relationship (QSPR)
Computer aided drug design (CADD)
Molecular descriptors
Databases
Chemical space
Data visualization

Related Special Issues

Published Papers (9 papers)

Download All Papers

Research

25 pages, 7016 KiB

Open AccessArticle

In Silico Screening of Novel α1-GABA_A Receptor PAMs towards Schizophrenia Based on Combined Modeling Studies of Imidazo [1,2-a]-Pyridines

by Xiaojiao Zheng, Chenchen Wang, Na Zhai, Xiaogang Luo, Genyan Liu and Xiulian Ju

Int. J. Mol. Sci. 2021, 22(17), 9645; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22179645 - 06 Sep 2021

Cited by 11 | Viewed by 3170

Abstract

The ionotropic GABA_A receptor (GABA_AR) has been proven to be an important target of atypical antipsychotics. A novel series of imidazo [1,2-a]-pyridine derivatives, as selective positive allosteric modulators (PAMs) of α1-containing GABA_ARs with potent antipsychotic activities, have been reported recently. To better clarify the pharmacological essentiality of these PAMs and explore novel antipsychotics hits, three-dimensional quantitative structure–activity relationships (3D-QSAR), molecular docking, pharmacophore modeling, and molecular dynamics (MD) were performed on 33 imidazo [1,2-a]-pyridines. The constructed 3D-QSAR models exhibited good predictive abilities. The dockings results and MD simulations demonstrated that hydrogen bonds, π–π stackings, and hydrophobic interactions play essential roles in the binding of these novel PAMs in the GABA_AR binding pocket. Four hit compounds (DS01–04) were then screened out by the combination of the constructed models and computations, including the pharmacophore model, Topomer Search, molecular dockings, ADME/T predictions, and MD simulations. The compounds DS03 and DS04, with higher docking scores and better predicted activities, were also found to be relatively stable in the binding pocket by MD simulations. These results might provide a significant theoretical direction or information for the rational design and development of novel α1-GABA_AR PAMs with antipsychotic activities. Full article

(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 2.0)

► Show Figures

Graphical abstract

21 pages, 7623 KiB

Open AccessArticle

Exploration of Novel Xanthine Oxidase Inhibitors Based on 1,6-Dihydropyrimidine-5-Carboxylic Acids by an Integrated in Silico Study

by Na Zhai, Chenchen Wang, Fengshou Wu, Liwei Xiong, Xiaogang Luo, Xiulian Ju and Genyan Liu

Int. J. Mol. Sci. 2021, 22(15), 8122; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22158122 - 29 Jul 2021

Cited by 11 | Viewed by 1940

Abstract

Xanthine oxidase (XO) is an important target for the effective treatment of hyperuricemia-associated diseases. A series of novel 2-substituted 6-oxo-1,6-dihydropyrimidine-5-carboxylic acids (ODCs) as XO inhibitors (XOIs) with remarkable activities have been reported recently. To better understand the key pharmacological characteristics of these XOIs and explore more hit compounds, in the present study, the three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking, pharmacophore modeling, and molecular dynamics (MD) studies were performed on 46 ODCs. The constructed 3D-QSAR models exhibited reliable predictability with satisfactory validation parameters, including q² = 0.897, R² = 0.983, r_pred² = 0.948 in a CoMFA model, and q² = 0.922, R² = 0.990, r_pred² = 0.840 in a CoMSIA model. Docking and MD simulations further gave insights into the binding modes of these ODCs with the XO protein. The results indicated that key residues Glu802, Arg880, Asn768, Thr1010, Phe914, and Phe1009 could interact with ODCs by hydrogen bonds, π-π stackings, or hydrophobic interactions, which might be significant for the activity of these XOIs. Four potential hits were virtually screened out using the constructed pharmacophore model in combination with molecular dockings and ADME predictions. The four hits were also found to be relatively stable in the binding pocket by MD simulations. The results in this study might provide effective information for the design and development of novel XOIs. Full article

(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 2.0)

► Show Figures

Figure 1

16 pages, 11788 KiB

Open AccessArticle

Docking-Based 3D-QSAR Studies for 1,3,4-oxadiazol-2-one Derivatives as FAAH Inhibitors

by Agata Zięba, Tuomo Laitinen, Jayendra Z. Patel, Antti Poso and Agnieszka A. Kaczor

Int. J. Mol. Sci. 2021, 22(11), 6108; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22116108 - 06 Jun 2021

Cited by 8 | Viewed by 3329

Abstract

This work aimed to construct 3D-QSAR CoMFA and CoMSIA models for a series of 31 FAAH inhibitors, containing the 1,3,4-oxadiazol-2-one moiety. The obtained models were characterized by good statistical parameters: CoMFA Q² = 0.61, R² = 0.98; CoMSIA Q² = 0.64, R² = 0.93. The CoMFA model field contributions were 54.1% and 45.9% for steric and electrostatic fields, respectively. In the CoMSIA model, electrostatic, steric, hydrogen bond donor, and hydrogen acceptor properties were equal to 34.6%, 23.9%, 23.4%, and 18.0%, respectively. These models were validated by applying the leave-one-out technique, the seven-element test set (CoMFA r²_test-set = 0.91; CoMSIA r²_test-set = 0.91), a progressive scrambling test, and external validation criteria developed by Golbraikh and Tropsha (CoMFA r²₀ = 0.98, k = 0.95; CoMSIA r²₀ = 0.98, k = 0.89). As the statistical significance of the obtained model was confirmed, the results of the CoMFA and CoMSIA field calculation were mapped onto the enzyme binding site. It gave us the opportunity to discuss the structure–activity relationship based on the ligand–enzyme interactions. In particular, examination of the electrostatic properties of the established CoMFA model revealed fields that correspond to the regions where electropositive substituents are not desired, e.g., in the neighborhood of the 1,3,4-oxadiazol-2-one moiety. This highlights the importance of heterocycle, a highly electronegative moiety in this area of each ligand. Examination of hydrogen bond donor and acceptor properties contour maps revealed several spots where the implementation of another hydrogen-bond-donating moiety will positively impact molecules’ binding affinity, e.g., in the neighborhood of the 1,3,4-oxadiazol-2-one ring. On the other hand, there is a large isopleth that refers to the favorable H-bond properties close to the terminal phenoxy group of a ligand, which means that, generally speaking, H-bond acceptors are desired in this area. Full article

(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 2.0)

► Show Figures

Graphical abstract

15 pages, 1745 KiB

Open AccessArticle

Application of Supervised SOM Algorithms in Predicting the Hepatotoxic Potential of Drugs

by Viktor Drgan and Benjamin Bajželj

Int. J. Mol. Sci. 2021, 22(9), 4443; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22094443 - 24 Apr 2021

Cited by 5 | Viewed by 1933

Abstract

The hepatotoxic potential of drugs is one of the main reasons why a number of drugs never reach the market or have to be withdrawn from the market. Therefore, the evaluation of the hepatotoxic potential of drugs is an important part of the drug development process. The aim of this work was to evaluate the relative abilities of different supervised self-organizing algorithms in classifying the hepatotoxic potential of drugs. Two modifications of standard counter-propagation training algorithms were proposed to achieve good separation of clusters on the self-organizing map. A series of optimizations were performed using genetic algorithm to select models developed with counter-propagation neural networks, X-Y fused networks, and the two newly proposed algorithms. The cluster separations achieved by the different algorithms were evaluated using a simple measure presented in this paper. Both proposed algorithms showed a better formation of clusters compared to the standard counter-propagation algorithm. The X-Y fused neural network confirmed its high ability to form well-separated clusters. Nevertheless, one of the proposed algorithms came close to its clustering results, which also resulted in a similar number of selected models. Full article

(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 2.0)

► Show Figures

Figure 1

23 pages, 2555 KiB

Open AccessArticle

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

by Amit Kumar Halder and M. Natália D. S. Cordeiro

Int. J. Mol. Sci. 2021, 22(8), 3944; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22083944 - 11 Apr 2021

Cited by 10 | Viewed by 3267

Abstract

AKT, is a serine/threonine protein kinase comprising three isoforms—namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT’ inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors. Full article

(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 2.0)

► Show Figures

Figure 1

24 pages, 793 KiB

Open AccessArticle

An Inverse QSAR Method Based on a Two-Layered Model and Integer Programming

by Yu Shi, Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi and Tatsuya Akutsu

Int. J. Mol. Sci. 2021, 22(6), 2847; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22062847 - 11 Mar 2021

Cited by 4 | Viewed by 2440

Abstract

A novel framework for inverse quantitative structure–activity relationships (inverse QSAR) has recently been proposed and developed using both artificial neural networks and mixed integer linear programming. However, classes of chemical graphs treated by the framework are limited. In order to deal with an arbitrary graph in the framework, we introduce a new model, called a two-layered model, and develop a corresponding method. In this model, each chemical graph is regarded as two parts: the exterior and the interior. The exterior consists of maximal acyclic induced subgraphs with bounded height, the interior is the connected subgraph obtained by ignoring the exterior, and the feature vector consists of the frequency of adjacent atom pairs in the interior and the frequency of chemical acyclic graphs in the exterior. Our method is more flexible than the existing method in the sense that any type of graphs can be inferred. We compared the proposed method with an existing method using several data sets obtained from PubChem database. The new method could infer more general chemical graphs with up to 50 non-hydrogen atoms. The proposed inverse QSAR method can be applied to the inference of more general chemical graphs than before. Full article

(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 2.0)

► Show Figures

Figure 1

27 pages, 9495 KiB

Open AccessArticle

Molecular Docking and QSAR Studies as Computational Tools Exploring the Rescue Ability of F508del CFTR Correctors

by Giada Righetti, Monica Casale, Nara Liessi, Bruno Tasso, Annalisa Salis, Michele Tonelli, Enrico Millo, Nicoletta Pedemonte, Paola Fossa and Elena Cichero

Int. J. Mol. Sci. 2020, 21(21), 8084; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21218084 - 29 Oct 2020

Cited by 10 | Viewed by 2653

Abstract

Cystic fibrosis (CF) is the autosomal recessive disorder most recurrent in Caucasian populations. Different mutations involving the cystic fibrosis transmembrane regulator protein (CFTR) gene, which encodes the CFTR channel, are involved in CF. A number of life-prolonging therapies have been conceived and deeply investigated to combat this disease. Among them, the administration of the so-called CFTR modulators, such as correctors and potentiators, have led to quite beneficial effects. Recently, based on QSAR (quantitative structure activity relationship) studies, we reported the rational design and synthesis of compound 2, an aminoarylthiazole-VX-809 hybrid derivative exhibiting promising F508del-CFTR corrector ability. Herein, we explored the docking mode of the prototype VX-809 as well as of the aforementioned correctors in order to derive useful guidelines for the rational design of further analogues. In addition, we refined our previous QSAR analysis taking into account our first series of in-house hybrids. This allowed us to optimize the QSAR model based on the chemical structure and the potency profile of hybrids as F508del-CFTR correctors, identifying novel molecular descriptors explaining the SAR of the dataset. This study is expected to speed up the discovery process of novel potent CFTR modulators. Full article

(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 2.0)

► Show Figures

Graphical abstract

20 pages, 4074 KiB

Open AccessArticle

A Toxicity Prediction Tool for Potential Agonist/Antagonist Activities in Molecular Initiating Events Based on Chemical Structures

by Kota Kurosaki, Raymond Wu and Yoshihiro Uesawa

Int. J. Mol. Sci. 2020, 21(21), 7853; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21217853 - 23 Oct 2020

Cited by 22 | Viewed by 4136

Abstract

Because the health effects of many compounds are unknown, regulatory toxicology must often rely on the development of quantitative structure–activity relationship (QSAR) models to efficiently discover molecular initiating events (MIEs) in the adverse-outcome pathway (AOP) framework. However, the QSAR models used in numerous toxicity prediction studies are publicly unavailable, and thus, they are challenging to use in practical applications. Approaches that simultaneously identify the various toxic responses induced by a compound are also scarce. The present study develops Toxicity Predictor, a web application tool that comprehensively identifies potential MIEs. Using various chemicals in the Toxicology in the 21st Century (Tox21) 10K library, we identified potential endocrine-disrupting chemicals (EDCs) using a machine-learning approach. Based on the optimized three-dimensional (3D) molecular structures and XGBoost algorithm, we established molecular descriptors for QSAR models. Their predictive performances and applicability domain were evaluated and applied to Toxicity Predictor. The prediction performance of the constructed models matched that of the top model in the Tox21 Data Challenge 2014. These advanced prediction results for MIEs are freely available on the Internet. Full article

(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 2.0)

► Show Figures

Graphical abstract

13 pages, 1361 KiB

Open AccessArticle

Computer-Aided Estimation of Biological Activity Profiles of Drug-Like Compounds Taking into Account Their Metabolism in Human Body

by Dmitry A. Filimonov, Anastassia V. Rudik, Alexander V. Dmitriev and Vladimir V. Poroikov

Int. J. Mol. Sci. 2020, 21(20), 7492; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21207492 - 11 Oct 2020

Cited by 16 | Viewed by 3879

Abstract

Most pharmaceutical substances interact with several or even many molecular targets in the organism, determining the complex profiles of their biological activity. Moreover, due to biotransformation in the human body, they form one or several metabolites with different biological activity profiles. Therefore, the development and rational use of novel drugs requires the analysis of their biological activity profiles, taking into account metabolism in the human body. In silico methods are currently widely used for estimating new drug-like compounds’ interactions with pharmacological targets and predicting their metabolic transformations. In this study, we consider the estimation of the biological activity profiles of organic compounds, taking into account the action of both the parent molecule and its metabolites in the human body. We used an external dataset that consists of 864 parent compounds with known metabolites. It is shown that the complex assessment of active pharmaceutical ingredients’ interactions with the human organism increases the quality of computer-aided estimates. The toxic and adverse effects showed the most significant difference: reaching 0.16 for recall and 0.14 for precision. Full article

(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 2.0)

► Show Figures