Comparative QSAR: Toward a Deeper Understanding of Chemicobiological Interactions

Automated machine learning approach for developing a quantitative structure-activity relationship model for cardiac steroid inhibition of Na+/K+-ATPase

BACKGROUND

Quantitative structure-activity relationship (QSAR) modeling is a method of characterizing the relationship between chemical structures and biological activity. Automated machine learning enables computers to learn from large datasets and can be used for chemoinformatics. Cardiac steroids (CSs) inhibit the activity of Na⁺/K⁺-ATPase (NKA) in several species, including humans, since the binding pocket in which NKA binds to CSs is highly conserved. CSs are used to treat heart disease and have been developed into anticancer drugs for use in clinical trials. Novel CSs are, therefore, frequently synthesized and their activities evaluated. The purpose of this study is to develop a QSAR model via automated machine learning to predict the potential inhibitory activity of compounds without performing experiments.

METHODS

The chemical structures and inhibitory activities of 215 CS derivatives were obtained from the scientific literature. Predictive QSAR models were constructed using molecular descriptors, fingerprints, and biological activities.

RESULTS

The best predictive QSAR models were selected based on the LogLoss value. Using these models, the Matthews correlation coefficient, F1 score, and area under the curve of the test dataset were 0.6729, 0.8813, and 0.8812, respectively. Next, we showed automated construction of the predictive models for CS derivatives, which may be useful for identifying novel CSs suitable for candidate drug development.

CONCLUSION

The automated machine learning-based QSAR method developed here should be applicable for the time-efficient construction of predictive models using only a small number of compounds.

Image-guided thermosensitive liposomes for focused ultrasound enhanced co-delivery of carboplatin and SN-38 against triple negative breast cancer in mice

Triggerable nanocarriers have the potential to significantly improve the therapeutic index of existing anticancer agents. They allow for highly localised delivery and release of therapeutic cargos, reducing off-target toxicity and increasing anti-tumour activity. Liposomes may be engineered to respond to an externally applied stimulus such as focused ultrasound (FUS). Here, we report the first co-delivery of SN-38 (irinotecan's super-active metabolite) and carboplatin, using an MRI-visible thermosensitive liposome (iTSL). MR contrast enhancement was achieved by the incorporation of a gadolinium lipid conjugate in the liposome bilayer along with a dye-labelled lipid for near infrared fluorescence bioimaging. The resulting iTSL were successfully loaded with SN-38 in the lipid bilayer and carboplatin in the aqueous core - allowing co-delivery of both. The iTSL demonstrated both thermosensitivity and MR-imageability. In addition, they showed effective local targeted co-delivery of carboplatin and SN-38 after triggered release with brief FUS treatments. A single dosage induced significant improvement of anti-tumour activity (over either the free drugs or the iTSL without FUS-activation) in triple negative breast cancer xenografts tumours in mice.

Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets

Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure-activity relationships (QSARs), there is no agreed single best algorithm for QSAR learning. Therefore, a comprehensive understanding of the performance characteristics of popular ML algorithms used in QSAR learning is highly desirable. In this study, five linear algorithms [linear function Gaussian process regression (linear-GPR), linear function support vector machine (linear-SVM), partial least squares regression (PLSR), multiple linear regression (MLR) and principal component regression (PCR)], three analogizers [radial basis function support vector machine (rbf-SVM), K-nearest neighbor (KNN) and radial basis function Gaussian process regression (rbf-GPR)], six symbolists [extreme gradient boosting (XGBoost), Cubist, random forest (RF), multiple adaptive regression splines (MARS), gradient boosting machine (GBM), and classification and regression tree (CART)] and two connectionists [principal component analysis artificial neural network (pca-ANN) and deep neural network (DNN)] were employed to learn the regression-based QSAR models for 14 public data sets comprising nine physicochemical properties and five toxicity endpoints. The results show that rbf-SVM, rbf-GPR, XGBoost and DNN generally illustrate better performances than the other algorithms. The overall performances of different algorithms can be ranked from the best to the worst as follows: rbf-SVM > XGBoost > rbf-GPR > Cubist > GBM > DNN > RF > pca-ANN > MARS > linear-GPR ≈ KNN > linear-SVM ≈ PLSR > CART ≈ PCR ≈ MLR. In terms of prediction accuracy and computational efficiency, SVM and XGBoost are recommended to the regression learning for small data sets, and XGBoost is an excellent choice for large data sets. We then investigated the performances of the ensemble models by integrating the predictions of multiple ML algorithms. The results illustrate that the ensembles of two or three algorithms in different categories can indeed improve the predictions of the best individual ML algorithms.

In silico prediction of toxicity and its applications for chemicals at work

OBJECTIVE AND METHODS

This study reviewed the concept of in silico prediction of chemical toxicity for prevention of occupational cancer and future prospects in workers' health. In this review, a new approach to determine the credibility of in silico predictions with raw data is explored, and the method of determining the confidence level of evaluation based on the credibility of data is discussed. I searched various papers and books related to the in silico prediction of chemical toxicity and carcinogenicity. The intention was to utilize the most recent reports after 2015 regarding in silico prediction.

RESULTS AND CONCLUSION

The application of in silico methods is increasing with the prediction of toxic risks to human and the environment. The various toxic effects of industrial chemicals have triggered the recognition of the importance of using a combination of in silico models in the risk assessments. In silico occupational exposure models, industrial accidents, and occupational cancers are effectively managed and chemicals evaluated. It is important to identify and manage hazardous substances proactively through the rigorous evaluation of chemicals.

Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSAR

Quantitative structure-activity relationship (QSAR) modelling of 55 focal adhesion kinase (FAK) (EC 2.7.10.2) inhibitors of triazinic nature was performed using the Monte Carlo method. The QSAR models were designed by CORAL software, and optimal descriptors were calculated with the simplified molecular input line entry system (SMILES). Four splits were made from the triazinic derivative data by random division into training, invisible training, calibration and validation sets. The QSAR results from these four random splits were robust, very simple, predictive and reliable. The best statistical parameters of the validation set (r² = 0.8398 and Q² = 0.7722) for the QSAR equation for split 3 with IIC = 0.9127 were obtained. The predictive potential of QSAR models of FAK inhibitors was explored by applying the index of ideality of correlation (IIC), which is a new criterion for the prediction of the potential for quantitative structure-property activity relationships (QSPRs/QSARs). The present method follows OECD principles.

Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints

BACKGROUND

Quantitative and qualitative structure–activity relationships (QSARs) have been used to understand chemical behavior for almost a century. The main source of QSAR models is the scientific literature, but the open question is how well these models are documented.

OBJECTIVES

The main aim of this study was to critically analyze the publication practices of QSARs with regard to transparency, potential reproducibility, and independent verification. The focus was on the level of technical completeness of the published QSARs.

METHODS

A total of 1,533 QSAR articles reporting 79 individual endpoints, mostly in environmental and health science, were reviewed. The QSAR parameters required for technical completeness were grouped into five categories: chemical structures, experimental endpoint values, descriptor values, mathematical representation of the model, and predicted endpoint values. The data were summarized and discussed using Circos plots.

RESULTS

Altogether, 42.5% of the reviewed articles were found to be potentially reproducible. The potential reproducibility for different endpoint groups varied; the respective rates were 39% for physical and chemical properties, 52% for ecotoxicity, 56% for environmental fate, 30% for human health, and 32% for toxicokinetics. The reproducibility of QSARs is discussed and placed in the context of the reproducibility of the experimental methods. Included are 65 references to open QSAR datasets as examples of models restored from scientific articles.

DISCUSSION

Strikingly poor documentation of QSARs was observed, which reduces the transparency, availability, and consequently, the application of research results in scientific, industrial, and regulatory areas. A list of the components needed to ensure the best practices for QSAR reporting is provided, allowing long-term use and preservation of the models. This list also allows an assessment of the reproducibility of models by interested parties such as journal editors, reviewers, regulators, evaluators, and potential users. https://doi.org/10.1289/EHP3264.

Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR)

INTRODUCTION

Quantitative structure-activity/property relationships (QSAR/QSPR) are statistical models which quantitatively correlate quantitative chemical structure information (described as molecular descriptors) to the response end points (biological activity, property, toxicity, etc.). Important strategies for QSAR model development and validation include dataset curation, variable selection, and dataset division, selection of modeling algorithms and appropriate measures of model validation. Areas covered: Different feature selection methods and various linear and nonlinear learning algorithms are employed to address the complexity of data sets for selection of appropriate features important for the responses being modeled, to reduce overfitting of the models, and to derive interpretable models. This review provides an overview of various feature selection methods as well as different statistical learning algorithms for QSAR modeling at an elementary level for nonexpert readers. Expert opinion: Novel sets of descriptors are being continuously introduced to this field; therefore, to handle this issue, there is a need to improve new tools for feature selection, which can lead to development of statistically meaningful models, usable by nonexperts in the fields. While handling data sets of limited size, special techniques like double cross-validation and consensus modeling might be more meaningful in order to remove the possibility of bias in descriptor selection.

QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer's Disease

Alzheimer's Disease (AD) is a multifactorial neurological syndrome with the combination of aging, genetic, and environmental factors triggering the pathological decline. Interestingly, the importance of the Acetylcholinesterase (AChE) enzyme has increased due to its involvement in the ß-amyloid peptide fibril formation during AD pathogenesis. In silico technique, QSAR has proven its usefulness in pharmaceutical research for the design/optimization of new chemical entities. Further, QSAR method advanced the scope of rational drug design and the search for the mechanism of drug action. It is a well-established fact that the chemical and pharmaceutical effects of a compound are closely related to its physico-chemical properties, which can be calculated by various methods from the compound structure. This chapter focuses on different Quantitative Structure-Activity Relationship (QSAR) studies carried out for a variety of cholinesterase inhibitors for the treatment of AD. These predictive models will be potentially used for further designing better and safer drugs against AD.

Quantitative Structure-Activity/Property/Toxicity Relationships through Conceptual Density Functional Theory-Based Reactivity Descriptors

Developing effective structure-activity/property/toxicity relationships (QSAR/QSPR/QSTR) is very helpful in predicting biological activity, property, and toxicity of a given set of molecules. Regular change in these properties with the structural alteration is the main reason to obtain QSAR/QSPR/QSTR models. The advancement in making different QSAR/QSPR/QSTR models to describe activity, property, and toxicity of various groups of molecules is reviewed in this chapter. The successful implementation of Conceptual Density Functional Theory (CDFT)-based global as well as local reactivity descriptors in modeling effective QSAR/QSPR/QSTR is highlighted.

A Theoretical Study of the Relationship between the Electrophilicity ω Index and Hammett Constant σp in [3+2] Cycloaddition Reactions of Aryl Azide/Alkyne Derivatives

The relationship between the electrophilicity ω index and the Hammett constant σ_p has been studied for the [2+3] cycloaddition reactions of a series of para-substituted phenyl azides towards para-substituted phenyl alkynes. The electrophilicity ω index—a reactivity density functional theory (DFT) descriptor evaluated at the ground state of the molecules—shows a good linear relationship with the Hammett substituent constants σ_p. The theoretical scale of reactivity correctly explains the electrophilic activation/deactivation effects promoted by electron-withdrawing and electron-releasing substituents in both azide and alkyne components.

A quantitative structure-activity relationship (QSAR) study of some diaryl urea derivatives of B-RAF inhibitors

In the current study, both ligand-based molecular docking and receptor-based quantitative structure activity relationships (QSAR) modeling were performed on 35 diaryl urea derivative inhibitors of ^V600EB-RAF. In this QSAR study, a linear (multiple linear regressions) and a nonlinear (partial least squares least squares support vector machine (PLS-LS-SVM)) were used and compared. The predictive quality of the QSAR models was tested for an external set of 31 compounds, randomly chosen out of 35 compounds. The results revealed the more predictive ability of PLS-LS-SVM in analysis of compounds with urea structure. The selected descriptors indicated that size, degree of branching, aromaticity, and polarizability affected the inhibition activity of these inhibitors. Furthermore, molecular docking was carried out to study the binding mode of the compounds. Docking analysis indicated some essential H-bonding and orientations of the molecules in the active site.

A study on quantitative structure-activity relationship and molecular docking of metalloproteinase inhibitors based on L-tyrosine scaffold

Background

MMP-2 enzyme is a kind of matrix metalloproteinases that digests the denatured collagens and gelatins. It is highly involved in the process of tumor invasion and has been considered as a promising target for cancer therapy. The structural requirements of an MMP-2 inhibitor are: (1) a functional group that binds the zinc ion, and (2) a functional group which interacts with the enzyme backbone and the side chains which undergo effective interactions with the enzyme subsites.

Methods

In the present study, a QSAR model was generated to screen new inhibitors of MMP-2 based on L–hydroxy tyrosine scaffold. Descriptors generation were done by Hyperchem 8, DRAGON and Gaussian98W programs. SPSS and MATLAB programs have been used for multiple linear regression (MLR) and genetic algorithm partial least squares (GA-PLS) analyses and for theoretical validation. Applicability domain of the model was performed to screen new compounds. The binding site potential of all inhibitors was verified by structure-based docking according to their binding energy and then the best inhibitors were selected.

Results

The best QSAR models in MLR and GA-PLS were reported, with the square correlation coefficient for leave-one-out cross-validation (Q²_LOO) larger than 0.921 and 0.900 respectively. The created MLR and GA-PLS models indicated the importance of molecular size, degree of branching, flexibility, shape, three-dimensional coordination of different atoms in a molecule in inhibitory activities against MMP-2.

The docking study indicated that lipophilic and hydrogen bonding interactions among the inhibitors and the receptor are involved in a ligand-receptor interaction. The oxygen of carbonyl and sulfonyl groups is important for hydrogen bonds of ligand with Leu82 and Ala83. R₂ and R₃ substituents play a main role in hydrogen bonding interactions. R₁ is sited in the hydrophobic pocket. Methylene group can help a ligand to be fitted in the lipophilic pocket, so two methylene groups are better than one. The Phenyl group can create a π-π interaction with Phe86.

Conclusions

The QSAR and docking analyses demonstrated to be helpful tools in the prediction of anti-cancer activities and a guide to the synthesis of new metalloproteinase inhibitors based on L-tyrosine scaffold.

A study on quantitative structure–activity relationship and molecular docking of metalloproteinase inhibitors based on L-tyrosine scaffold

Background

MMP-2 enzyme is a kind of matrix metalloproteinases that digests the denatured collagens and gelatins. It is highly involved in the process of tumor invasion and has been considered as a promising target for cancer therapy. The structural requirements of an MMP-2 inhibitor are: (1) a functional group that binds the zinc ion, and (2) a functional group which interacts with the enzyme backbone and the side chains which undergo effective interactions with the enzyme subsites.

Methods

In the present study, a QSAR model was generated to screen new inhibitors of MMP-2 based on L–hydroxy tyrosine scaffold. Descriptors generation were done by Hyperchem 8, DRAGON and Gaussian98W programs. SPSS and MATLAB programs have been used for multiple linear regression (MLR) and genetic algorithm partial least squares (GA-PLS) analyses and for theoretical validation. Applicability domain of the model was performed to screen new compounds. The binding site potential of all inhibitors was verified by structure-based docking according to their binding energy and then the best inhibitors were selected.

Results

The best QSAR models in MLR and GA-PLS were reported, with the square correlation coefficient for leave-one-out cross-validation (Q2 LOO) larger than 0.921 and 0.900 respectively. The created MLR and GA-PLS models indicated the importance of molecular size, degree of branching, flexibility, shape, three-dimensional coordination of different atoms in a molecule in inhibitory activities against MMP-2.

The docking study indicated that lipophilic and hydrogen bonding interactions among the inhibitors and the receptor are involved in a ligand-receptor interaction. The oxygen of carbonyl and sulfonyl groups is important for hydrogen bonds of ligand with Leu82 and Ala83. R2 and R3 substituents play a main role in hydrogen bonding interactions. R1 is sited in the hydrophobic pocket. Methylene group can help a ligand to be fitted in the lipophilic pocket, so two methylene groups are better than one. The Phenyl group can create a π-π interaction with Phe86.

Conclusions

The QSAR and docking analyses demonstrated to be helpful tools in the prediction of anti-cancer activities and a guide to the synthesis of new metalloproteinase inhibitors based on L-tyrosine scaffold.

A structure-activity relationship study of imidazole-5-carboxylic acids derivatives as angiotensin II receptor antagonists combining 2D and 3D QSAR methods

Two Dimensional (2D) and Three Dimensional (3D) Quantitative Structure-Activity Relationship (QSAR) studies were performed for correlating the chemical composition of Imidazole-5-carboxylic Acids analogues and their Angiotensin II AT1 Receptor Antagonists activity using partial least squares and k Nearest Neighbor respectively. For Comparing the three different feature selection methods of 2D-QSAR, k Nearest Neighbor models was used in conjunction with simulated annealing (SA), genetic algorithm (GA) and stepwise (SW) coupled with Partial least square (PLS) showed variation in biological activity. The statistically significant best 2D-QSAR model having good predictive ability with statistical values of r2 = 0.8040, and pred_r2 = 0.7764, was developed by SA-Partial least square with the descriptors like SsCH3Count, 5Chain Count, SdsCHE-index and H-acceptor count showed that increase in the values of these descriptors are beneficial for the activity. The 3D-QSAR studies were performed using the SA-PLS a leave-one-out cross-validated correlation coefficient q2=0.7188 and predicate activity pred_r2 =0.7226 were obtained. The information rendered by QSAR models may lead to a better understanding of structural requirements of substituted Imidazole-5-carboxylic Acids derivatives and also aid in designing novel potent antihypertensive molecules.

SAR and Computer-Aided Drug Design Approaches in the Discovery of Peroxisome Proliferator-Activated Receptor γ Activators: A Perspective

Activators of PPARγ, Troglitazone (TGZ), Rosiglitazone (RGZ), and Pioglitazone (PGZ) were introduced for treatment of Type 2 diabetes, but TGZ and RGZ have been withdrawn from the market along with other promising leads due cardiovascular side effects and hepatotoxicity. However, the continuously improving understanding of the structure/function of PPARγ and its interactions with potential ligands maintain the importance of PPARγ as an antidiabetic target. Extensive structure activity relationship (SAR) studies have thus been performed on a variety of structural scaffolds by various research groups. Computer-aided drug discovery (CADD) approaches have also played a vital role in the search and optimization of potential lead compounds. This paper focuses on these approaches adopted for the discovery of PPARγ ligands for the treatment of Type 2 diabetes. Key concepts employed during the discovery phase, classification based on agonistic character, applications of various QSAR, pharmacophore mapping, virtual screening, molecular docking, and molecular dynamics studies are highlighted. Molecular level analysis of the dynamic nature of ligand-receptor interaction is presented for the future design of ligands with better potency and safety profiles. Recently identified mechanism of inhibition of phosphorylation of PPARγ at SER273 by ligands is reviewed as a new strategy to identify novel drug candidates.

Learning Binding Affinity from Augmented High Throughput Screening Data

In high throughput screening a large number of molecules are tested against a single target protein to determine binding affinity of each molecule to the target. The objective of such tests within the pharmaceutical industry is to identify potential drug-like lead molecules. Current technology allows for thousands of molecules to be tested inexpensively. The analysis of linking such biological data with molecular properties is thus becoming a major goal in both academic and pharmaceutical research. This chapter details how screening data can be augmented with high-dimensional descriptor data and how machine learning techniques can be utilised to build predictive models. The pyruvate kinase protein is used as a model target throughout the chapter. Binding affinity data from a public repository provide binding information on a large set of screened molecules. The authors consider three machine learning paradigms: Bayesian model averaging, Neural Networks, and Support Vector Machines. The authors apply algorithms from the three paradigms to three subsets of the data and comment on the relative merits of each. They also used the learnt models to classify the molecules in a large in-house molecular database that holds commercially available chemical structures from a large number of suppliers. They discuss the degree of agreement in compounds selected and ranked for three algorithms. Details of the technical challenges in such large scale classification and the ability of each paradigm to cope with these are put forward. The application of machine learning techniques to binding data augmented by high-dimensional can provide a powerful tool in compound testing. The emphasis of this work is on making very few assumptions or technical choices with regard to the machine learning techniques. This is to facilitate application of such techniques by non-experts.