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QSAR and QSPR: Recent Developments and Applications, 3rd Edition

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

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 35962

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Dr. Bono Lučić

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Rudjer Bošković Institute, NMR Centre, Zagreb, Croatia
Interests: QSAR; QSPR; modelling in chemistry and molecular biophysics; development of molecular descriptors; model selection methods; model validation algorithms; classification modelling; antioxidant activity modelling; cheminformatics; bioinformatics
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Dr. Bakhtiyor Rasulev

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Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58108, USA
Interests: cheminformatics; computational nanosciences; materials informatics
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Dear Colleagues,

Since the introduction of Quantitative Structure-Activity Relationships (QSAR) almost 60 years ago when the biological activities of molecules (potential drugs) have been modelled, the application of this methodology has extended to modelling a number of physicochemical properties of molecules (QSPR). From the initial small sets of molecules having a limited number of molecular descriptors with clear chemical interpretation, and the simplest and mostly linear models that linked the structural characteristics of molecules with their biological activity - today in the field of QSAR/QSPR modelling sets of molecules and molecular descriptors are large, functional relationships between molecular descriptors and modelled activity/property are (generally) nonlinear and complex. The clear interpretation of today's QSAR/QSPR models is not an easy task due to the complex inter-relation between input descriptors and between the input descriptors and modelled activity/property. Also, numerous biological activities and physicochemical properties of molecules are modelled meaning that the QSAR/QSPR methodology is applied in various fields of chemistry, chemical engineering, drug design, biotechnological, environmental chemistry and toxicology, polymer chemistry as well as in bioscience and nanoscience. Continuous issues of improvement of QSAR/QSPR methodology is related to:

(1) development of mathematical and informational tools in encoding structural information at the constitutional, 2D, topological, as well as 3D semi-empirical or DFT level of theory, development of molecular descriptor theory, information content in molecular data sets, inter-relation between descriptors;

(2) data mining, descriptor (pre) selection, model development tools (including different modelling and knowledge extraction algorithms from multivariate regressions to machine learning and deep learning), model optimization and validation algorithms, model quality parameters (metrics), random accuracy;

(3) model applicability domain definition, improvement of model interpretability, model predictivity on real (external) sets of molecules, ensemble (consensus) QSAR/QSPR modelling.

The contributions/studies covering each of the mentioned aspects connected to the improvement of QSAR/QSPR methodology are welcome, as well as comparative studies performed on benchmark data sets. Applications of QSAR/QSPR methodology on different problems related to continuous or classification endpoints (activities or properties) in drug design, chemical, pharmaceutical, biotechnological or environmental sciences, chemical engineering, bioscience or nanoscience are welcome.

Dr. Bono Lučić
Prof. Dr. Bakhtiyor Rasulev
Guest Editors

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Keywords

QSAR/QSPR modelling
development of molecular descriptors
molecular structure representation
data mining
machine learning
regression QSAR/QSPR modelling
classification QSAR/QSPR modelling
model optimization protocols
model validation algorithms
model quality metrics
random accuracy
ensemble QSAR/QSPR modelling
applicability domain
QSAR/QSPR model interpretation
comparative modelling
drug design
drug discovery
environmentally-relevant activity
biological activity
physicochemical property

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Related Special Issues

QSAR and QSPR: Recent Developments and Applications in Molecules (16 articles)
QSAR and QSPR: Recent Developments and Applications, 2nd Edition in Molecules (16 articles)
QSAR and QSPR: Recent Developments and Applications, 4th Edition in Molecules (3 articles)

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Research

14 pages, 2454 KiB

Open AccessArticle

Three-Dimensional-QSAR and Relative Binding Affinity Estimation of Focal Adhesion Kinase Inhibitors

by Suparna Ghosh and Seung Joo Cho

Molecules 2023, 28(3), 1464; https://doi.org/10.3390/molecules28031464 - 2 Feb 2023

Cited by 3 | Viewed by 2219

Abstract

Precise binding affinity predictions are essential for structure-based drug discovery (SBDD). Focal adhesion kinase (FAK) is a member of the tyrosine kinase protein family and is overexpressed in a variety of human malignancies. Inhibition of FAK using small molecules is a promising therapeutic option for several types of cancer. Here, we conducted computational modeling of FAK-targeting inhibitors using three-dimensional structure–activity relationship (3D-QSAR), molecular dynamics (MD), and hybrid topology-based free energy perturbation (FEP) methods. The structure–activity relationship (SAR) studies between the physicochemical descriptors and inhibitory activities of the chemical compounds were performed with reasonable statistical accuracy using CoMFA and CoMSIA. These are two well-known 3D-QSAR methods based on the principle of supervised machine learning (ML). Essential information regarding residue-specific binding interactions was determined using MD and MM-PB/GBSA methods. Finally, physics-based relative binding free energy (

Δ Δ G_{RBFE}^{A \to B}

) terms of analogous ligands were estimated using alchemical FEP simulation. An acceptable agreement was observed between the experimental and computed relative binding free energies. Overall, the results suggested that using ML and physics-based hybrid approaches could be useful in synergy for the rational optimization of accessible lead compounds with similar scaffolds targeting the FAK receptor. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 3rd Edition)

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24 pages, 6008 KiB

Open AccessArticle

How the Structure of Per- and Polyfluoroalkyl Substances (PFAS) Influences Their Binding Potency to the Peroxisome Proliferator-Activated and Thyroid Hormone Receptors—An In Silico Screening Study

by Dominika Kowalska, Anita Sosnowska, Natalia Bulawska, Maciej Stępnik, Harrie Besselink, Peter Behnisch and Tomasz Puzyn

Molecules 2023, 28(2), 479; https://doi.org/10.3390/molecules28020479 - 4 Jan 2023

Cited by 13 | Viewed by 5160

Abstract

In this study, we investigated PFAS (per- and polyfluoroalkyl substances) binding potencies to nuclear hormone receptors (NHRs): peroxisome proliferator-activated receptors (PPARs) α, β, and γ and thyroid hormone receptors (TRs) α and β. We have simulated the docking scores of 43 perfluoroalkyl compounds and based on these data developed QSAR (Quantitative Structure-Activity Relationship) models for predicting the binding probability to five receptors. In the next step, we implemented the developed QSAR models for the screening approach of a large group of compounds (4464) from the NORMAN Database. The in silico analyses indicated that the probability of PFAS binding to the receptors depends on the chain length, the number of fluorine atoms, and the number of branches in the molecule. According to the findings, the considered PFAS group bind to the PPARα, β, and γ only with low or moderate probability, while in the case of TR α and β it is similar except that those chemicals with longer chains show a moderately high probability of binding. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 3rd Edition)

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18 pages, 10109 KiB

Open AccessArticle

The Hydrolysis Rate of Paraoxonase-1 Q and R Isoenzymes: An In Silico Study Based on In Vitro Data

by Sedat Karabulut, Basel Mansour, Gerardo M. Casanola-Martin, Bakhtiyor Rasulev and James W. Gauld

Molecules 2022, 27(20), 6780; https://doi.org/10.3390/molecules27206780 - 11 Oct 2022

Cited by 3 | Viewed by 1686

Abstract

Human serum paraoxonase-1 (PON1) is an important hydrolase-type enzyme found in numerous tissues. Notably, it can exist in two isozyme-forms, Q and R, that exhibit different activities. This study presents an in silico (QSAR, Docking, MD and QM/MM) study of a set of compounds on the activity towards the PON1 isoenzymes (QPON1 and RPON1). Different rates of reaction for the Q and R isoenzymes were analyzed by modelling the effect of Q192R mutation on active sites. It was concluded that the Q192R mutation is not even close to the active site, while it is still changing the geometry of it. Using the combined genetic algorithm with multiple linear regression (GA-MLR) technique, several QSAR models were developed and relative activity rates of the isozymes of PON1 explained. From these, two QSAR models were selected, one each for the QPON1 and RPON1. Best selected models are four-variable MLR models for both Q and R isozymes with squared correlation coefficient R² values of 0.87 and 0.83, respectively. In addition, the applicability domain of the models was analyzed based on the Williams plot. The results were discussed in the light of the main factors that influence the hydrolysis activity of the PON1 isozymes. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 3rd Edition)

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19 pages, 1803 KiB

Open AccessArticle

Cheminformatics Modeling of Gene Silencing for Both Natural and Chemically Modified siRNAs

by Xialan Dong and Weifan Zheng

Molecules 2022, 27(19), 6412; https://doi.org/10.3390/molecules27196412 - 28 Sep 2022

Cited by 1 | Viewed by 1940

Abstract

In designing effective siRNAs for a specific mRNA target, it is critically important to have predictive models for the potency of siRNAs. None of the published methods characterized the chemical structures of individual nucleotides constituting a siRNA molecule; therefore, they cannot predict the potency of gene silencing by chemically modified siRNAs (cm-siRNA). We propose a new approach that can predict the potency of gene silencing by cm-siRNAs, which characterizes each nucleotide (NT) using 12 BCUT cheminformatics descriptors describing its charge distribution, hydrophobic and polar properties. Thus, a 21-NT siRNA molecule is described by 252 descriptors resulting from concatenating all the BCUT values of its composing nucleotides. Partial Least Square is employed to develop statistical models. The Huesken data (2431 natural siRNA molecules) were used to perform model building and evaluation for natural siRNAs. Our results were comparable with or superior to those from Huesken’s algorithm. The Bramsen dataset (48 cm-siRNAs) was used to build and test the models for cm-siRNAs. The predictive r² of the resulting models reached 0.65 (or Pearson r values of 0.82). Thus, this new method can be used to successfully model gene silencing potency by both natural and chemically modified siRNA molecules. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 3rd Edition)

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14 pages, 1753 KiB

Open AccessArticle

Machine Learning for Evaluating the Cytotoxicity of Mixtures of Nano-TiO₂ and Heavy Metals: QSAR Model Apply Random Forest Algorithm after Clustering Analysis

by Leqi Sang, Yunlin Wang, Cheng Zong, Pengfei Wang, Huazhong Zhang, Dan Guo, Beilei Yuan and Yong Pan

Molecules 2022, 27(18), 6125; https://doi.org/10.3390/molecules27186125 - 19 Sep 2022

Cited by 10 | Viewed by 2545

Abstract

With the development and application of nanomaterials, their impact on the environment and organisms has attracted attention. As a common nanomaterial, nano-titanium dioxide (nano-TiO₂) has adsorption properties to heavy metals in the environment. Quantitative structure-activity relationship (QSAR) is often used to predict the cytotoxicity of a single substance. However, there is little research on the toxicity of interaction between nanomaterials and other substances. In this study, we exposed human renal cortex proximal tubule epithelial (HK-2) cells to mixtures of eight heavy metals with nano-TiO₂, measured absorbance values by CCK-8, and calculated cell viability. PLS and two ensemble learning algorithms are used to build multiple QSAR models for data sets, and the test set R² is increased from 0.38 to 0.78 and 0.85, and RMSE is decreased from 0.18 to 0.12 and 0.10. After selecting the better random forest algorithm, the K-means clustering algorithm is used to continue to optimize the model, increasing the test set R² to 0.95 and decreasing the RMSE to 0.08 and 0.06. As a reliable machine algorithm, random forest can be used to predict the toxicity of the mixture of nano-metal oxides and heavy metals. The cluster analysis can effectively improve the stability and predictability of the model, and provide a new idea for the prediction of cytotoxicity model in the future. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 3rd Edition)

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18 pages, 3467 KiB

Open AccessArticle

Hybrid Classification/Regression Approach to QSAR Modeling of Stoichiometric Antiradical Capacity Assays’ Endpoints

by Petko Alov, Ivanka Tsakovska and Ilza Pajeva

Molecules 2022, 27(7), 2084; https://doi.org/10.3390/molecules27072084 - 24 Mar 2022

Cited by 1 | Viewed by 2235

Abstract

Quantitative structure–activity relationships (QSAR) are a widely used methodology allowing not only a better understanding of the mechanisms of chemical reactions, including radical scavenging, but also to predict the relevant properties of chemical compounds without their synthesis, isolation and experimental testing. Unlike the QSAR modeling of the kinetic antioxidant assays, modeling of the assays with stoichiometric endpoints depends strongly on the number of hydroxyl groups in the antioxidant molecule, as well as on some integral molecular descriptors characterizing the proportion of OH-groups able to enter and complete the radical scavenging reaction. In this work, we tested the feasibility of a “hybrid” classification/regression approach, consisting of explicit classification of individual OH-groups as involved in radical scavenging reactions, and using further the number of these OH-groups as a descriptor in simple-regression QSAR models of antiradical capacity assays with stoichiometric endpoints. A simple threshold classification based on the sum of trolox-equivalent antiradical capacity values was used, selecting OH-groups with specific radical stability- and reactivity-related electronic parameters or their combination as “active” or “inactive”. We showed that this classification/regression modeling approach provides a substantial improvement of the simple-regression QSAR models over those built on the number of total phenolic OH-groups only, and yields a statistical performance similar to that of the best reported multiple-regression QSARs for antiradical capacity assays with stoichiometric endpoints. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 3rd Edition)

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18 pages, 1694 KiB

Open AccessArticle

Assisting Multitargeted Ligand Affinity Prediction of Receptor Tyrosine Kinases Associated Nonsmall Cell Lung Cancer Treatment with Multitasking Principal Neighborhood Aggregation

by Fahsai Nakarin, Kajjana Boonpalit, Jiramet Kinchagawat, Patcharapol Wachiraphan, Thanyada Rungrotmongkol and Sarana Nutanong

Molecules 2022, 27(4), 1226; https://doi.org/10.3390/molecules27041226 - 11 Feb 2022

Cited by 6 | Viewed by 3099

Abstract

A multitargeted therapeutic approach with hybrid drugs is a promising strategy to enhance anticancer efficiency and overcome drug resistance in nonsmall cell lung cancer (NSCLC) treatment. Estimating affinities of small molecules against targets of interest typically proceeds as a preliminary action for recent drug discovery in the pharmaceutical industry. In this investigation, we employed machine learning models to provide a computationally affordable means for computer-aided screening to accelerate the discovery of potential drug compounds. In particular, we introduced a quantitative structure–activity-relationship (QSAR)-based multitask learning model to facilitate an in silico screening system of multitargeted drug development. Our method combines a recently developed graph-based neural network architecture, principal neighborhood aggregation (PNA), with a descriptor-based deep neural network supporting synergistic utilization of molecular graph and fingerprint features. The model was generated by more than ten-thousands affinity-reported ligands of seven crucial receptor tyrosine kinases in NSCLC from two public data sources. As a result, our multitask model demonstrated better performance than all other benchmark models, as well as achieving satisfying predictive ability regarding applicable QSAR criteria for most tasks within the model’s applicability. Since our model could potentially be a screening tool for practical use, we have provided a model implementation platform with a tutorial that is freely accessible hence, advising the first move in a long journey of cancer drug development. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 3rd Edition)

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30 pages, 131019 KiB

Open AccessArticle

Selection of Promising Novel Fragment Sized S. aureus SrtA Noncovalent Inhibitors Based on QSAR and Docking Modeling Studies

by Dmitry A. Shulga and Konstantin V. Kudryavtsev

Molecules 2021, 26(24), 7677; https://doi.org/10.3390/molecules26247677 - 19 Dec 2021

Cited by 7 | Viewed by 3020

Abstract

Sortase A (SrtA) of Staphylococcus aureus has been identified as a promising target to a new type of antivirulent drugs, and therefore, the design of lead molecules with a low nanomolar range of activity and suitable drug-like properties is important. In this work, we aimed at identifying new fragment-sized starting points to design new noncovalent S. aureus SrtA inhibitors by making use of the dedicated molecular motif, 5-arylpyrrolidine-2-carboxylate, which has been previously shown to be significant for covalent binding SrtA inhibitors. To this end, an in silico approach combining QSAR and molecular docking studies was used. The known SrtA inhibitors from the ChEMBL database with diverse scaffolds were first employed to derive descriptors and interpret their significance and correlation to activity. Then, the classification and regression QSAR models were built, which were used for rough ranking of the virtual library of the synthetically feasible compounds containing the dedicated motif. Additionally, the virtual library compounds were docked into the “activated” model of SrtA (PDB:2KID). The consensus ranking of the virtual library resulted in the most promising structures, which will be subject to further synthesis and experimental testing in order to establish new fragment-like molecules for further development into antivirulent drugs. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 3rd Edition)

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18 pages, 3004 KiB

Open AccessArticle

Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

by Amit Kumar Halder, Reza Haghbakhsh, Iuliia V. Voroshylova, Ana Rita C. Duarte and M. Natalia D. S. Cordeiro

Molecules 2021, 26(19), 5779; https://doi.org/10.3390/molecules26195779 - 24 Sep 2021

Cited by 25 | Viewed by 3002

Abstract

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 3rd Edition)

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22 pages, 6427 KiB

Open AccessArticle

QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs

by Yangxi Yu, Hiep Dong, Youyi Peng and William J. Welsh

Molecules 2021, 26(17), 5270; https://doi.org/10.3390/molecules26175270 - 30 Aug 2021

Cited by 3 | Viewed by 3244

Abstract

S2R overexpression is associated with various forms of cancer as well as both neuropsychiatric disorders (e.g., schizophrenia) and neurodegenerative diseases (Alzheimer’s disease: AD). In the present study, three ligand-based methods (QSAR modeling, pharmacophore mapping, and shape-based screening) were implemented to select putative S2R ligands from the DrugBank library comprising 2000+ entries. Four separate optimization algorithms (i.e., stepwise regression, Lasso, genetic algorithm (GA), and a customized extension of GA called GreedGene) were adapted to select descriptors for the QSAR models. The subsequent biological evaluation of selected compounds revealed that three FDA-approved drugs for unrelated therapeutic indications exhibited sub-1 uM binding affinity for S2R. In particular, the antidepressant drug nefazodone elicited a S2R binding affinity Ki = 140 nM. A total of 159 unique S2R ligands were retrieved from 16 publications for model building, validation, and testing. To our best knowledge, the present report represents the first case to develop comprehensive QSAR models sourced by pooling and curating a large assemblage of structurally diverse S2R ligands, which should prove useful for identifying new drug leads and predicting their S2R binding affinity prior to the resource-demanding tasks of chemical synthesis and biological evaluation. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 3rd Edition)

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15 pages, 2124 KiB

Open AccessArticle

Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads

by Ajaykumar Gandhi, Vijay Masand, Magdi E. A. Zaki, Sami A. Al-Hussain, Anis Ben Ghorbal and Archana Chapolikar

Molecules 2021, 26(16), 4795; https://doi.org/10.3390/molecules26164795 - 7 Aug 2021

Cited by 2 | Viewed by 2717

Abstract

In the present endeavor, for the dataset of 219 in vitro MDA-MB-231 TNBC cell antagonists, a (QSAR) quantitative structure–activity relationships model has been carried out. The quantitative and explicative assessments were performed to identify inconspicuous yet pre-eminent structural features that govern the anti-tumor activity of these compounds. GA-MLR (genetic algorithm multi-linear regression) methodology was employed to build statistically robust and highly predictive multiple QSAR models, abiding by the OECD guidelines. Thoroughly validated QSAR models attained values for various statistical parameters well above the threshold values (i.e., R² = 0.79, Q²_LOO = 0.77, Q²_LMO = 0.76–0.77, Q²-Fⁿ = 0.72–0.76). Both de novo QSAR models have a sound balance of descriptive and statistical approaches. Decidedly, these QSAR models are serviceable in the development of MDA-MB-231 TNBC cell antagonists. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications, 3rd Edition)

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