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Special Issue "Chemoinformatics and Bioinformatics Tools in Structure-Activity Modelling in Molecular Sciences"

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: 31 December 2021.

Special Issue Editor

Dr. Bono Lučić
E-Mail
Guest Editor
The Ruđer Bošković Institute, Zagreb, Croatia
Interests: chemoinformatics; structural bioinformatics; structure–activity modeling; QSAR; QSPR; molecular modeling; computational chemistry; molecular structural biophysics; development of model validation algorithms; variable selection algorithms; classification modeling; chance accuracy estimation; development of accuracy parameters; computational research in bioprospecting research; protein structure analysis and prediction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

At a time of universal digitization of data in various fields of research, including molecular sciences, there are more and more studies modeling continuous or classification endpoints (activities/properties) of molecules. In doing so, endpoints of molecules are most often classified (digitized) into two classes—active or inactive, and the classification is often carried out by grouping data into three or more classes.

Quantitative structure–activity/property relationships (QSAR/QSPR) are the most common, but not the only, forms of structure–endpoint models in molecular sciences. The accuracy of models is expressed by validation procedures, and many quality parameters are defined in the OECD document related to regulatory structure–activity models for the purpose of health and environmental protection [1]. In this document, the accuracy parameters of classification models are very sparsely presented. However, numerous accuracy parameters are used today, and those used for classification models are calculated from the confusion matrix elements [2]. There is also an increased need for a better definition of procedures for validation of regulatory structure–activity models in the OECD document [1]. Their application in environmental and health protection (toxicity, bioavailability, sorption, biodegradability, etc.) has been defined by EU REACH regulations [3].

The development of structure–activity modeling of different types of endpoints of molecules (usually various types of biological activities) is accelerated using chemoinformatics and bioinformatics tools, servers, algorithms, and databases developed for small molecules and proteins.

The research activities in the development of novel chemoinformatics and bioinformatics tools are particularly important topics for this Special Issue, such as the development of:

- Valuable databases, servers, and data mining tools;

- Drug or lead structure identification or dereplication approaches used in bioprospecting research;

- Structure optimization tools;

- Molecular descriptors;

- Modeling and variable selection algorithms;

- Computational model validation methods;

- Multivariate linear and nonlinear methods;

- Machine learning and deep learning algorithms;

- Predictive or descriptive structure–activity models;

- Different visualization tools;

- Protein–ligand (target/small compound) interactions;

- Molecular docking, etc.

All these topics are of the highest importance for structure–activity modeling in molecular sciences.

This Special Issue aims to collect relevant contributions (papers) belonging to one or more of the topics listed above (and those related to them), which are important for the acceleration of structure–activity research in molecular sciences. Applications aimed at modeling a broad spectrum of chemical, biological, pharmaceutical, biochemical, and environmentally relevant activities and properties of molecules are also appreciated.

All forms of scientific articles covering mentioned or related topics are welcomed, i.e., original papers, reviews, and communications. 

[1] Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models, [https://www.oecd.org/env/guidance-document-on-the-validation-of-quantitative-structure-activity-relationship-q-sar-models-9789264085442-en.htm]

[2] D. M. W. Powers, Evaluation: from precision, recall and f-measure to roc, informedness, markedness & correlation, J. Machine Learning Techn., 2011, 2, 37-63

[3] Regulation (EC) No 1907/2006: REACH - Registration, Evaluation, Authorisation and Restriction of Chemicals. [http://ec.europa.eu/enterprise/sectors/chemicals/reach/index_en.htm].

Dr. Bono Lučić
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 papers will be 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 tools 
  • Bioinformatics tools 
  • Structure–activity modeling 
  • Structure–property modeling 
  • QSAR, QSPR 
  • Drug/structure identification in bioprospecting research 
  • Development of algorithms 
  • Databases and web servers 
  • Data mining 
  • Structure representation and optimization 
  • Molecular descriptors 
  • Modelling of health and environmentally relevant endpoints/activities/properties 
  • Toxicity, carcinogenicity 
  • Computational methods 
  • Model validation approaches 
  • Multivariate modeling 
  • Predictive modeling 
  • Descriptive modeling 
  • Classification modeling 
  • Machine learning 
  • Deep learning 
  • Structure visualization

Published Papers (10 papers)

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Research

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Article
Unsupervised Representation Learning for Proteochemometric Modeling
Int. J. Mol. Sci. 2021, 22(23), 12882; https://doi.org/10.3390/ijms222312882 - 28 Nov 2021
Viewed by 483
Abstract
In silico protein–ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection and prioritization of possible drug candidates. Proteochemometric [...] Read more.
In silico protein–ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection and prioritization of possible drug candidates. Proteochemometric modeling (PCM) attempts to create an accurate model of the protein–ligand interaction space by combining explicit protein and ligand descriptors. This requires the creation of information-rich, uniform and computer interpretable representations of proteins and ligands. Previous studies in PCM modeling rely on pre-defined, handcrafted feature extraction methods, and many methods use protein descriptors that require alignment or are otherwise specific to a particular group of related proteins. However, recent advances in representation learning have shown that unsupervised machine learning can be used to generate embeddings that outperform complex, human-engineered representations. Several different embedding methods for proteins and molecules have been developed based on various language-modeling methods. Here, we demonstrate the utility of these unsupervised representations and compare three protein embeddings and two compound embeddings in a fair manner. We evaluate performance on various splits of a benchmark dataset, as well as on an internal dataset of protein–ligand binding activities and find that unsupervised-learned representations significantly outperform handcrafted representations. Full article
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Article
Generation of Non-Nucleotide CD73 Inhibitors Using a Molecular Docking and 3D-QSAR Approach
Int. J. Mol. Sci. 2021, 22(23), 12745; https://doi.org/10.3390/ijms222312745 - 25 Nov 2021
Viewed by 217
Abstract
Radiotherapy and chemotherapy are conventional cancer treatments. Around 60% of all patients who are diagnosed with cancer receive radio- or chemotherapy in combination with surgery during their disease. Only a few patients respond to the blockage of immune checkpoints alone, or in combination [...] Read more.
Radiotherapy and chemotherapy are conventional cancer treatments. Around 60% of all patients who are diagnosed with cancer receive radio- or chemotherapy in combination with surgery during their disease. Only a few patients respond to the blockage of immune checkpoints alone, or in combination therapy, because their tumours might not be immunogenic. Under these circumstances, an increasing level of extracellular adenosine via the activation of ecto-5’-nucleotidase (CD73) and consequent adenosine receptor signalling is a typical mechanism that tumours use to evade immune surveillance. CD73 is responsible for the conversion of adenosine monophosphate to adenosine. CD73 is overexpressed in various tumour types. Hence, targetting CD73’s signalling is important for the reversal of adenosine-facilitated immune suppression. In this study, we selected a potent series of the non-nucleotide small molecule inhibitors of CD73. Molecular docking studies were performed in order to examine the binding mode of the inhibitors inside the active site of CD73 and 3D-QSAR was used to study the structure–activity relationship. The obtained CoMFA (q2 = 0.844, ONC = 5, r2 = 0.947) and CoMSIA (q2 = 0.804, ONC = 4, r2 = 0.954) models showed reasonable statistical values. The 3D-QSAR contour map analysis revealed useful structural characteristics that were needed to modify non-nucleotide small molecule inhibitors. We used the structural information from the overall docking and 3D-QSAR results to design new, potent CD73 non-nucleotide inhibitors. The newly designed CD73 inhibitors exhibited higher activity (predicted pIC50) than the most active compound of all of the derivatives that were selected for this study. Further experimental studies are needed in order to validate the new CD73 inhibitors. Full article
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Article
Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure–Activity Relationship System
Int. J. Mol. Sci. 2021, 22(19), 10821; https://doi.org/10.3390/ijms221910821 - 06 Oct 2021
Viewed by 401
Abstract
In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure–activity relationship (QSAR) analysis has the advantages that it is able to construct models to [...] Read more.
In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure–activity relationship (QSAR) analysis has the advantages that it is able to construct models to predict the biological properties of chemicals based on structural information. Previously, we reported a deep learning (DL) algorithm-based QSAR approach called DeepSnap-DL for high-performance prediction modeling of the agonist and antagonist activity of key molecules in molecular initiating events in toxicological pathways using optimized hyperparameters. In the present study, to achieve high throughput in the DeepSnap-DL system–which consists of the preparation of three-dimensional molecular structures of chemical compounds, the generation of snapshot images from the three-dimensional chemical structures, DL, and statistical calculations—we propose an improved DeepSnap-DL approach. Using this improved system, we constructed 59 prediction models for the agonist and antagonist activity of key molecules in the Tox21 10K library. The results indicate that modeling of the agonist and antagonist activity with high prediction performance and high throughput can be achieved by optimizing suitable parameters in the improved DeepSnap-DL system. Full article
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Article
Molecular Dynamics-Derived Pharmacophore Model Explaining the Nonselective Aspect of KV10.1 Pore Blockers
Int. J. Mol. Sci. 2021, 22(16), 8999; https://doi.org/10.3390/ijms22168999 - 20 Aug 2021
Viewed by 442
Abstract
The KV10.1 voltage-gated potassium channel is highly expressed in 70% of tumors, and thus represents a promising target for anticancer drug discovery. However, only a few ligands are known to inhibit KV10.1, and almost all also inhibit the very [...] Read more.
The KV10.1 voltage-gated potassium channel is highly expressed in 70% of tumors, and thus represents a promising target for anticancer drug discovery. However, only a few ligands are known to inhibit KV10.1, and almost all also inhibit the very similar cardiac hERG channel, which can lead to undesirable side-effects. In the absence of the structure of the KV10.1–inhibitor complex, there remains the need for new strategies to identify selective KV10.1 inhibitors and to understand the binding modes of the known KV10.1 inhibitors. To investigate these binding modes in the central cavity of KV10.1, a unique approach was used that allows derivation and analysis of ligand–protein interactions from molecular dynamics trajectories through pharmacophore modeling. The final molecular dynamics-derived structure-based pharmacophore model for the simulated KV10.1–ligand complexes describes the necessary pharmacophore features for KV10.1 inhibition and is highly similar to the previously reported ligand-based hERG pharmacophore model used to explain the nonselectivity of KV10.1 pore blockers. Moreover, analysis of the molecular dynamics trajectories revealed disruption of the π–π network of aromatic residues F359, Y464, and F468 of KV10.1, which has been reported to be important for binding of various ligands for both KV10.1 and hERG channels. These data indicate that targeting the KV10.1 channel pore is also likely to result in undesired hERG inhibition, and other potential binding sites should be explored to develop true KV10.1-selective inhibitors as new anticancer agents. Full article
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Article
Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets
Int. J. Mol. Sci. 2021, 22(15), 8073; https://doi.org/10.3390/ijms22158073 - 28 Jul 2021
Viewed by 623
Abstract
Drug-induced liver toxicity is one of the significant safety challenges for the patient’s health and the pharmaceutical industry. It causes termination of drug candidates in clinical trials and also the retractions of approved drugs from the market. Thus, it is essential to identify [...] Read more.
Drug-induced liver toxicity is one of the significant safety challenges for the patient’s health and the pharmaceutical industry. It causes termination of drug candidates in clinical trials and also the retractions of approved drugs from the market. Thus, it is essential to identify hepatotoxic compounds in the initial stages of drug development process. The purpose of this study is to construct quantitative structure activity relationship models using machine learning algorithms and systematical feature selection methods for molecular descriptor sets. The models were built from a large and diverse set of 1253 drug compounds and were validated internally with 10-fold cross-validation. In this study, we applied a variety of feature selection techniques to extract the optimal subset of descriptors as modeling features to improve the prediction performance. Experimental results suggested that the support vector machine-based classifier had achieved a better classification accuracy with reduced molecular descriptors. The final optimal model provides an accuracy of 0.811, a sensitivity of 0.840, a specificity of 0.783 and Mathew’s correlation coefficient of 0.623 with an internal validation set. Furthermore, this model outperformed the prior studies while evaluated in both the internal and external test sets. The utilization of distinct optimal molecular descriptors as modeling features produce an in silico model with a superior performance. Full article
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Article
Biological Activities Related to Plant Protection and Environmental Effects of Coumarin Derivatives: QSAR and Molecular Docking Studies
Int. J. Mol. Sci. 2021, 22(14), 7283; https://doi.org/10.3390/ijms22147283 - 06 Jul 2021
Cited by 1 | Viewed by 1115
Abstract
The aim was to study the inhibitory effects of coumarin derivatives on the plant pathogenic fungi, as well as beneficial bacteria and nematodes. The antifungal assay was performed on four cultures of phytopathogenic fungi by measuring the radial growth of the fungal colonies. [...] Read more.
The aim was to study the inhibitory effects of coumarin derivatives on the plant pathogenic fungi, as well as beneficial bacteria and nematodes. The antifungal assay was performed on four cultures of phytopathogenic fungi by measuring the radial growth of the fungal colonies. Antibacterial activity was determined by the broth microdilution method performed on two beneficial soil organisms. Nematicidal activity was tested on two entomopathogenic nematodes. The quantitative structure-activity relationship (QSAR) model was generated by genetic algorithm, and toxicity was estimated by T.E.S.T. software. The mode of inhibition of enzymes related to the antifungal activity is elucidated by molecular docking. Coumarin derivatives were most effective against Macrophomina phaseolina and Sclerotinia sclerotiorum, but were not harmful against beneficial nematodes and bacteria. A predictive QSAR model was obtained for the activity against M. phaseolina (R2tr = 0.78; R2ext = 0.67; Q2loo = 0.67). A QSAR study showed that multiple electron-withdrawal groups, especially at position C-3, enhanced activities against M. phaseolina, while the hydrophobic benzoyl group at the pyrone ring, and –Br, –OH, –OCH3, at the benzene ring, may increase inhibition of S. sclerotiourum. Tested compounds possibly act inhibitory against plant wall-degrading enzymes, proteinase K. Coumarin derivatives are the potentially active ingredient of environmentally friendly plant-protection products. Full article
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Article
Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances
Int. J. Mol. Sci. 2021, 22(13), 6695; https://doi.org/10.3390/ijms22136695 - 22 Jun 2021
Cited by 1 | Viewed by 571
Abstract
Many chemicals that enter the environment, food chain, and the human body can disrupt androgen-dependent pathways and mimic hormones and therefore, may be responsible for multiple diseases from reproductive to tumor. Thus, modeling and predicting androgen receptor activity is an important area of [...] Read more.
Many chemicals that enter the environment, food chain, and the human body can disrupt androgen-dependent pathways and mimic hormones and therefore, may be responsible for multiple diseases from reproductive to tumor. Thus, modeling and predicting androgen receptor activity is an important area of research. The aim of the current study was to find a method or combination of methods to predict compounds that can bind to and/or disrupt the androgen receptor, and thereby guide decision making and further analysis. A stepwise procedure proceeded from analysis of protein structures from human, chimp, and rat, followed by docking and subsequent ligand, and statistics based techniques that improved classification gradually. The best methods used multivariate logistic regression of combinations of chimpanzee protein structural docking scores, extended connectivity fingerprints, and naïve Bayesians of known binders and non-binders. Combination or consensus methods included data from a variety of procedures to improve the final model accuracy. Full article
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Article
Evaluation of Selective COX-2 Inhibition and In Silico Study of Kuwanon Derivatives Isolated from Morus alba
Int. J. Mol. Sci. 2021, 22(7), 3659; https://doi.org/10.3390/ijms22073659 - 01 Apr 2021
Viewed by 641
Abstract
Six kuwanon derivatives (A/B/C/E/H/J) extracted from the roots of Morus alba L. were evaluated to determine their cyclooxygenase (COX)-1 and 2 inhibitory effects. Cyclooxygenase (COX) is known as the target enzyme of nonsteroidal anti-inflammatory drugs (NSAIDs), which are the most widely used therapeutic [...] Read more.
Six kuwanon derivatives (A/B/C/E/H/J) extracted from the roots of Morus alba L. were evaluated to determine their cyclooxygenase (COX)-1 and 2 inhibitory effects. Cyclooxygenase (COX) is known as the target enzyme of nonsteroidal anti-inflammatory drugs (NSAIDs), which are the most widely used therapeutic agents for pain and inflammation. Among six kuwanon derivatives, kuwanon A showed selective COX-2 inhibitory activity, almost equivalent to that of celecoxib, a known COX inhibitor. Kuwanon A showed high COX-2 inhibitory activity (IC50 = 14 μM) and a selectivity index (SI) range of >7.1, comparable to celecoxib (SI > 6.3). To understand the mechanisms underlying this effect, we performed docking simulations, fragment molecular orbital (FMO) calculations, and pair interaction energy decomposition analysis (PIEDA) at the quantum-mechanical level. As a result, kuwanon A had the strongest interaction with Arg120 and Tyr355 at the gate of the COX active site (−7.044 kcal/mol) and with Val89 in the membrane-binding domain (−6.599 kcal/mol). In addition, kuwanon A closely bound to Val89, His90, and Ser119, which are residues at the entrance and exit routes of the COX active site (4.329 Å). FMO calculations and PIEDA well supported the COX-2 selective inhibitory action of kuwanon A. It showed that the simulation and modeling results and experimental evidence were consistent. Full article
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Article
Structure-Activity Relationship Modeling and Experimental Validation of the Imidazolium and Pyridinium Based Ionic Liquids as Potential Antibacterials of MDR Acinetobacter baumannii and Staphylococcus aureus
Int. J. Mol. Sci. 2021, 22(2), 563; https://doi.org/10.3390/ijms22020563 - 08 Jan 2021
Cited by 1 | Viewed by 913
Abstract
Online Chemical Modeling Environment (OCHEM) was used for QSAR analysis of a set of ionic liquids (ILs) tested against multi-drug resistant (MDR) clinical isolate Acinetobacter baumannii and Staphylococcus aureus strains. The predictive accuracy of regression models has coefficient of determination q2 = [...] Read more.
Online Chemical Modeling Environment (OCHEM) was used for QSAR analysis of a set of ionic liquids (ILs) tested against multi-drug resistant (MDR) clinical isolate Acinetobacter baumannii and Staphylococcus aureus strains. The predictive accuracy of regression models has coefficient of determination q2 = 0.66 − 0.79 with cross-validation and independent test sets. The models were used to screen a virtual chemical library of ILs, which was designed with targeted activity against MDR Acinetobacter baumannii and Staphylococcus aureus strains. Seven most promising ILs were selected, synthesized, and tested. Three ILs showed high activity against both these MDR clinical isolates. Full article
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Review

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Review
Towards Quantum-Chemical Modeling of the Activity of Anesthetic Compounds
Int. J. Mol. Sci. 2021, 22(17), 9272; https://doi.org/10.3390/ijms22179272 - 27 Aug 2021
Viewed by 686
Abstract
The modeling of the activity of anesthetics is a real challenge because of their unique electronic and structural characteristics. Microscopic approaches relevant to the typical features of these systems have been developed based on the advancements in the theory of intermolecular interactions. By [...] Read more.
The modeling of the activity of anesthetics is a real challenge because of their unique electronic and structural characteristics. Microscopic approaches relevant to the typical features of these systems have been developed based on the advancements in the theory of intermolecular interactions. By stressing the quantum chemical point of view, here, we review the advances in the field highlighting differences and similarities among the chemicals within this group. The binding of the anesthetics to their partners has been analyzed by Symmetry-Adapted Perturbation Theory to provide insight into the nature of the interaction and the modeling of the adducts/complexes allows us to rationalize their anesthetic properties. A new approach in the frame of microtubule concept and the importance of lipid rafts and channels in membranes is also discussed. Full article
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