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QSAR and Chemoinformatics in Molecular Modeling and Drug Design 3.0
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QSAR and Chemoinformatics in Molecular Modeling and Drug Design 3.0

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 (31 January 2022) | Viewed by 8759

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

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Published Papers (22 papers)

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Editorial

Jump to: Research, Review

9 pages, 717 KiB  
Editorial
The Literature of Chemoinformatics: 1978–2018
by Peter Willett
Int. J. Mol. Sci. 2020, 21(15), 5576; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21155576 - 04 Aug 2020
Cited by 9 | Viewed by 4144
Abstract
This article presents a study of the literature of chemoinformatics, updating and building upon an analogous bibliometric investigation that was published in 2008. Data on outputs in the field, and citations to those outputs, were obtained by means of topic searches of the [...] Read more.
This article presents a study of the literature of chemoinformatics, updating and building upon an analogous bibliometric investigation that was published in 2008. Data on outputs in the field, and citations to those outputs, were obtained by means of topic searches of the Web of Science Core Collection. The searches demonstrate that chemoinformatics is by now a well-defined sub-discipline of chemistry, and one that forms an essential part of the chemical educational curriculum. There are three core journals for the subject: The Journal of Chemical Information and Modeling, the Journal of Cheminformatics, and Molecular Informatics, and, having established itself, chemoinformatics is now starting to export knowledge to disciplines outside of chemistry. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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Research

Jump to: Editorial, Review

19 pages, 3808 KiB  
Article
A Deep Learning-Based Quantitative Structure–Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance
by Yasunari Matsuzaka and Yoshihiro Uesawa
Int. J. Mol. Sci. 2022, 23(4), 2141; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms23042141 - 15 Feb 2022
Cited by 6 | Viewed by 1916
Abstract
Molecular design and evaluation for drug development and chemical safety assessment have been advanced by quantitative structure–activity relationship (QSAR) using artificial intelligence techniques, such as deep learning (DL). Previously, we have reported the high performance of prediction models molecular initiation events (MIEs) on [...] Read more.
Molecular design and evaluation for drug development and chemical safety assessment have been advanced by quantitative structure–activity relationship (QSAR) using artificial intelligence techniques, such as deep learning (DL). Previously, we have reported the high performance of prediction models molecular initiation events (MIEs) on the adverse toxicological outcome using a DL-based QSAR method, called DeepSnap-DL. This method can extract feature values from images generated on a three-dimensional (3D)-chemical structure as a novel QSAR analytical system. However, there is room for improvement of this system’s time-consumption. Therefore, in this study, we constructed an improved DeepSnap-DL system by combining the processes of generating an image from a 3D-chemical structure, DL using the image as input data, and statistical calculation of prediction-performance. Consequently, we obtained that the three prediction models of agonists or antagonists of MIEs achieved high prediction-performance by optimizing the parameters of DeepSnap, such as the angle used in the depiction of the image of a 3D-chemical structure, data-split, and hyperparameters in DL. The improved DeepSnap-DL system will be a powerful tool for computer-aided molecular design as a novel QSAR system. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 3.0)
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15 pages, 3456 KiB  
Article
Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach
by Valentina A. Afonina, Daniyar A. Mazitov, Albina Nurmukhametova, Maxim D. Shevelev, Dina A. Khasanova, Ramil I. Nugmanov, Vladimir A. Burilov, Timur I. Madzhidov and Alexandre Varnek
Int. J. Mol. Sci. 2022, 23(1), 248; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms23010248 - 27 Dec 2021
Cited by 5 | Viewed by 3245
Abstract
The selection of experimental conditions leading to a reasonable yield is an important and essential element for the automated development of a synthesis plan and the subsequent synthesis of the target compound. The classical QSPR approach, requiring one-to-one correspondence between chemical structure and [...] Read more.
The selection of experimental conditions leading to a reasonable yield is an important and essential element for the automated development of a synthesis plan and the subsequent synthesis of the target compound. The classical QSPR approach, requiring one-to-one correspondence between chemical structure and a target property, can be used for optimal reaction conditions prediction only on a limited scale when only one condition component (e.g., catalyst or solvent) is considered. However, a particular reaction can proceed under several different conditions. In this paper, we describe the Likelihood Ranking Model representing an artificial neural network that outputs a list of different conditions ranked according to their suitability to a given chemical transformation. Benchmarking calculations demonstrated that our model outperformed some popular approaches to the theoretical assessment of reaction conditions, such as k Nearest Neighbors, and a recurrent artificial neural network performance prediction of condition components (reagents, solvents, catalysts, and temperature). The ability of the Likelihood Ranking model trained on a hydrogenation reactions dataset, (~42,000 reactions) from Reaxys® database, to propose conditions that led to the desired product was validated experimentally on a set of three reactions with rich selectivity issues. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 3.0)
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17 pages, 2816 KiB  
Article
A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening
by Jacob Spiegel and Hanoch Senderowitz
Int. J. Mol. Sci. 2022, 23(1), 43; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms23010043 - 21 Dec 2021
Cited by 2 | Viewed by 2626
Abstract
Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as [...] Read more.
Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set (e.g.,QCV2) or the test set (e.g., specificity, selectivity or QF1/F2/F32). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 3.0)
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25 pages, 7016 KiB  
Article
In Silico Screening of Novel α1-GABAA 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 3162
Abstract
The ionotropic GABAA receptor (GABAAR) 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 GABAARs with potent antipsychotic activities, have been [...] Read more.
The ionotropic GABAA receptor (GABAAR) 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 GABAARs 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 GABAAR 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-GABAAR PAMs with antipsychotic activities. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design 2.0)
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21 pages, 7623 KiB  
Article
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 1933
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 [...] Read more.
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, rpred2 = 0.948 in a CoMFA model, and q2 = 0.922, R2 = 0.990, rpred2 = 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)
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16 pages, 11788 KiB  
Article
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 3321
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 = [...] Read more.
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 r2test-set = 0.91; CoMSIA r2test-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)
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15 pages, 1745 KiB  
Article
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 1931
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 [...] Read more.
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)
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23 pages, 2555 KiB  
Article
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 3263
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 [...] Read more.
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)
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24 pages, 793 KiB  
Article
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 2435
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 [...] Read more.
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)
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27 pages, 9495 KiB  
Article
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 2650
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 [...] Read more.
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)
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20 pages, 4074 KiB  
Article
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 4121
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 [...] Read more.
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)
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20 pages, 554 KiB  
Article
Evaluation of QSAR Equations for Virtual Screening
by Jacob Spiegel and Hanoch Senderowitz
Int. J. Mol. Sci. 2020, 21(21), 7828; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21217828 - 22 Oct 2020
Cited by 11 | Viewed by 2293
Abstract
Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large [...] Read more.
Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large number of calculate-able descriptors and consequently, the much larger number of descriptors combinations, the derivation of QSAR models could be treated as an optimization problem. For continuous responses, metrics which are typically being optimized in this process are related to model performances on the training set, for example, R2 and QCV2. Similar metrics, calculated on an external set of data (e.g., QF1/F2/F32), are used to evaluate the performances of the final models. A common theme of these metrics is that they are context -” ignorant”. In this work we propose that QSAR models should be evaluated based on their intended usage. More specifically, we argue that QSAR models developed for Virtual Screening (VS) should be derived and evaluated using a virtual screening-aware metric, e.g., an enrichment-based metric. To demonstrate this point, we have developed 21 Multiple Linear Regression (MLR) models for seven targets (three models per target), evaluated them first on validation sets and subsequently tested their performances on two additional test sets constructed to mimic small-scale virtual screening campaigns. As expected, we found no correlation between model performances evaluated by “classical” metrics, e.g., R2 and QF1/F2/F32 and the number of active compounds picked by the models from within a pool of random compounds. In particular, in some cases models with favorable R2 and/or QF1/F2/F32 values were unable to pick a single active compound from within the pool whereas in other cases, models with poor R2 and/or QF1/F2/F32 values performed well in the context of virtual screening. We also found no significant correlation between the number of active compounds correctly identified by the models in the training, validation and test sets. Next, we have developed a new algorithm for the derivation of MLR models by optimizing an enrichment-based metric and tested its performances on the same datasets. We found that the best models derived in this manner showed, in most cases, much more consistent results across the training, validation and test sets and outperformed the corresponding MLR models in most virtual screening tests. Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)-based models across training/validation/test sets, in most cases. We attribute the better performances of the Enrichment Optimizer Algorithm (EOA) models in VS to better handling of inactive random compounds. Optimizing an enrichment-based metric is therefore a promising strategy for the derivation of QSAR models for classification and virtual screening. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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13 pages, 1361 KiB  
Article
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 3871
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 [...] Read more.
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)
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22 pages, 8025 KiB  
Article
Discovery of Novel Hsp90 C-Terminal Inhibitors Using 3D-Pharmacophores Derived from Molecular Dynamics Simulations
by Tihomir Tomašič, Martina Durcik, Bradley M. Keegan, Darja Gramec Skledar, Živa Zajec, Brian S. J. Blagg and Sharon D. Bryant
Int. J. Mol. Sci. 2020, 21(18), 6898; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21186898 - 20 Sep 2020
Cited by 18 | Viewed by 4052
Abstract
Hsp90 C-terminal domain (CTD) inhibitors are promising novel agents for cancer treatment, as they do not induce the heat shock response associated with Hsp90 N-terminal inhibitors. One challenge associated with CTD inhibitors is the lack of a co-crystallized complex, requiring the use of [...] Read more.
Hsp90 C-terminal domain (CTD) inhibitors are promising novel agents for cancer treatment, as they do not induce the heat shock response associated with Hsp90 N-terminal inhibitors. One challenge associated with CTD inhibitors is the lack of a co-crystallized complex, requiring the use of predicted allosteric apo pocket, limiting structure-based (SB) design approaches. To address this, a unique approach that enables the derivation and analysis of interactions between ligands and proteins from molecular dynamics (MD) trajectories was used to derive pharmacophore models for virtual screening (VS) and identify suitable binding sites for SB design. Furthermore, ligand-based (LB) pharmacophores were developed using a set of CTD inhibitors to compare VS performance with the MD derived models. Virtual hits identified by VS with both SB and LB models were tested for antiproliferative activity. Compounds 9 and 11 displayed antiproliferative activities in MCF-7 and Hep G2 cancer cell lines. Compound 11 inhibited Hsp90-dependent refolding of denatured luciferase and induced the degradation of Hsp90 clients without the concomitant induction of Hsp70 levels. Furthermore, compound 11 offers a unique scaffold that is promising for the further synthetic optimization and development of molecules needed for the evaluation of the Hsp90 CTD as a target for the development of anticancer drugs. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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23 pages, 2314 KiB  
Article
Consensus-Based Pharmacophore Mapping for New Set of N-(disubstituted-phenyl)-3-hydroxyl-naphthalene-2-carboxamides
by Andrzej Bak, Jiri Kos, Hana Michnova, Tomas Gonec, Sarka Pospisilova, Violetta Kozik, Alois Cizek, Adam Smolinski and Josef Jampilek
Int. J. Mol. Sci. 2020, 21(18), 6583; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21186583 - 09 Sep 2020
Cited by 11 | Viewed by 2140
Abstract
A series of twenty-two novel N-(disubstituted-phenyl)-3-hydroxynaphthalene- 2-carboxamide derivatives was synthesized and characterized as potential antimicrobial agents. N-[3,5-bis(trifluoromethyl)phenyl]- and N-[2-chloro-5-(trifluoromethyl)phenyl]-3-hydroxy- naphthalene-2-carboxamide showed submicromolar (MICs 0.16–0.68 µM) activity against methicillin-resistant Staphylococcus aureus isolates. N-[3,5-bis(trifluoromethyl)phenyl]- and N-[4-bromo-3-(trifluoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide revealed activity against M. [...] Read more.
A series of twenty-two novel N-(disubstituted-phenyl)-3-hydroxynaphthalene- 2-carboxamide derivatives was synthesized and characterized as potential antimicrobial agents. N-[3,5-bis(trifluoromethyl)phenyl]- and N-[2-chloro-5-(trifluoromethyl)phenyl]-3-hydroxy- naphthalene-2-carboxamide showed submicromolar (MICs 0.16–0.68 µM) activity against methicillin-resistant Staphylococcus aureus isolates. N-[3,5-bis(trifluoromethyl)phenyl]- and N-[4-bromo-3-(trifluoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide revealed activity against M. tuberculosis (both MICs 10 µM) comparable with that of rifampicin. Synergistic activity was observed for the combinations of ciprofloxacin with N-[4-bromo-3-(trifluoromethyl)phenyl]- and N-(4-bromo-3-fluorophenyl)-3-hydroxynaphthalene-2-carboxamides against MRSA SA 630 isolate. The similarity-related property space assessment for the congeneric series of structurally related carboxamide derivatives was performed using the principal component analysis. Interestingly, different distribution of mono-halogenated carboxamide derivatives with the –CF3 substituent is accompanied by the increased activity profile. A symmetric matrix of Tanimoto coefficients indicated the structural dissimilarities of dichloro- and dimetoxy-substituted isomers from the remaining ones. Moreover, the quantitative sampling of similarity-related activity landscape provided a subtle picture of favorable and disallowed structural modifications that are valid for determining activity cliffs. Finally, the advanced method of neural network quantitative SAR was engaged to illustrate the key 3D steric/electronic/lipophilic features of the ligand-site composition by the systematic probing of the functional group. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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11 pages, 5369 KiB  
Article
Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies
by Oliver Snow, Nada Lallous, Martin Ester and Artem Cherkasov
Int. J. Mol. Sci. 2020, 21(16), 5847; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21165847 - 14 Aug 2020
Cited by 13 | Viewed by 2510
Abstract
Gain-of-function mutations in human androgen receptor (AR) are among the major causes of drug resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols, as well as for designing drugs that do not elicit [...] Read more.
Gain-of-function mutations in human androgen receptor (AR) are among the major causes of drug resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols, as well as for designing drugs that do not elicit adverse responses. However, experimental characterization of these mutations is time consuming and costly; thus, predictive models are needed to anticipate resistant mutations and to guide the drug discovery process. In this work, we leverage experimental data collected on 68 AR mutants, either observed in the clinic or described in the literature, to train a deep neural network (DNN) that predicts the response of these mutants to currently used and experimental anti-androgens and testosterone. We demonstrate that the use of this DNN, with general 2D descriptors, provides a more accurate prediction of the biological outcome (inhibition, activation, no-response, mixed-response) in AR mutant-drug pairs compared to other machine learning approaches. Finally, the developed approach was used to make predictions of AR mutant response to the latest AR inhibitor darolutamide, which were then validated by in-vitro experiments. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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20 pages, 1860 KiB  
Article
Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions
by Assima Rakhimbekova, Timur I. Madzhidov, Ramil I. Nugmanov, Timur R. Gimadiev, Igor I. Baskin and Alexandre Varnek
Int. J. Mol. Sci. 2020, 21(15), 5542; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21155542 - 03 Aug 2020
Cited by 32 | Viewed by 3436
Abstract
Nowadays, the problem of the model’s applicability domain (AD) definition is an active research topic in chemoinformatics. Although many various AD definitions for the models predicting properties of molecules (Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) models) were described in the literature, no one for chemical [...] Read more.
Nowadays, the problem of the model’s applicability domain (AD) definition is an active research topic in chemoinformatics. Although many various AD definitions for the models predicting properties of molecules (Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) models) were described in the literature, no one for chemical reactions (Quantitative Reaction-Property Relationships (QRPR)) has been reported to date. The point is that a chemical reaction is a much more complex object than an individual molecule, and its yield, thermodynamic and kinetic characteristics depend not only on the structures of reactants and products but also on experimental conditions. The QRPR models’ performance largely depends on the way that chemical transformation is encoded. In this study, various AD definition methods extensively used in QSAR/QSPR studies of individual molecules, as well as several novel approaches suggested in this work for reactions, were benchmarked on several reaction datasets. The ability to exclude wrong reaction types, increase coverage, improve the model performance and detect Y-outliers were tested. As a result, several “best” AD definitions for the QRPR models predicting reaction characteristics have been revealed and tested on a previously published external dataset with a clear AD definition problem. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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12 pages, 3597 KiB  
Article
X-ray Structure-Based Chemoinformatic Analysis Identifies Promiscuous Ligands Binding to Proteins from Different Classes with Varying Shapes
by Christian Feldmann and Jürgen Bajorath
Int. J. Mol. Sci. 2020, 21(11), 3782; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21113782 - 27 May 2020
Cited by 6 | Viewed by 1891
Abstract
(1) Background: Compounds with multitarget activity are of interest in basic research to explore molecular foundations of promiscuous binding and in drug discovery as agents eliciting polypharmacological effects. Our study has aimed to systematically identify compounds that form complexes with proteins from distinct [...] Read more.
(1) Background: Compounds with multitarget activity are of interest in basic research to explore molecular foundations of promiscuous binding and in drug discovery as agents eliciting polypharmacological effects. Our study has aimed to systematically identify compounds that form complexes with proteins from distinct classes and compare their bioactive conformations and molecular properties. (2) Methods: A large-scale computational investigation was carried out that combined the analysis of complex X-ray structures, ligand binding modes, compound activity data, and various molecular properties. (3) Results: A total of 515 ligands with multitarget activity were identified that included 70 organic compounds binding to proteins from different classes. These multiclass ligands (MCLs) were often flexible and surprisingly hydrophilic. Moreover, they displayed a wide spectrum of binding modes. In different target structure environments, binding shapes of MCLs were often similar, but also distinct. (4) Conclusions: Combined structural and activity data analysis identified compounds with activity against proteins with distinct structures and functions. MCLs were found to have greatly varying shape similarity when binding to different protein classes. Hence, there were no apparent canonical binding shapes indicating multitarget activity. Rather, conformational versatility characterized MCL binding. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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15 pages, 3374 KiB  
Article
Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope
by Neann Mathai and Johannes Kirchmair
Int. J. Mol. Sci. 2020, 21(10), 3585; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21103585 - 19 May 2020
Cited by 23 | Viewed by 4542
Abstract
Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates [...] Read more.
Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates are commonly reported as averages over all compounds of a test set and do not consider the structural relationship between the individual test compounds and the training instances. In order to obtain a better understanding of the value of ligand-based methods for target prediction, we benchmarked a similarity-based method and a random forest based machine learning approach (both employing 2D molecular fingerprints) under three testing scenarios: a standard testing scenario with external data, a standard time-split scenario, and a scenario that is designed to most closely resemble real-world conditions. In addition, we deconvoluted the results based on the distances of the individual test molecules from the training data. We found that, surprisingly, the similarity-based approach generally outperformed the machine learning approach in all testing scenarios, even in cases where queries were structurally clearly distinct from the instances in the training (or reference) data, and despite a much higher coverage of the known target space. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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24 pages, 3672 KiB  
Article
In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression
by Ming-Han Lee, Giang Huong Ta, Ching-Feng Weng and Max K. Leong
Int. J. Mol. Sci. 2020, 21(10), 3582; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21103582 - 19 May 2020
Cited by 11 | Viewed by 2897
Abstract
The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure–activity relationship (QSAR) model was [...] Read more.
The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure–activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75–0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 0.78–0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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Review

Jump to: Editorial, Research

22 pages, 2252 KiB  
Review
Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement
by Viet-Khoa Tran-Nguyen and Didier Rognan
Int. J. Mol. Sci. 2020, 21(12), 4380; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21124380 - 19 Jun 2020
Cited by 6 | Viewed by 3519
Abstract
Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years. Numerous artificially constructed data collections were developed, such as DUD, DUD-E, or DEKOIS. However, they all suffer from multiple drawbacks, [...] Read more.
Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years. Numerous artificially constructed data collections were developed, such as DUD, DUD-E, or DEKOIS. However, they all suffer from multiple drawbacks, one of which is the absence of experimental results confirming the impotence of presumably inactive molecules, leading to possible false negatives in the ligand sets. In light of this problem, the PubChem BioAssay database, an open-access repository providing the bioactivity information of compounds that were already tested on a biological target, is now a recommended source for data set construction. Nevertheless, there exist several issues with the use of such data that need to be properly addressed. In this article, an overview of benchmarking data collections built upon experimental PubChem BioAssay input is provided, along with a thorough discussion of noteworthy issues that one must consider during the design of new ligand sets from this database. The points raised in this review are expected to guide future developments in this regard, in hopes of offering better evaluation tools for novel in silico screening procedures. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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