Quantitative Structure-activity Relations Models Research Articles

Development of Imidazo[1,2-a]pyridines Containing Sulfonyl Piperazines as Potential Antivirals against Tomato Spotted Wilt Virus.

Mesoionic structures have become important advancements in recent agrochemical design. However, their potential beyond serving as excellent insecticides remains unexplored with limited reports available. Herein, a series of imidazo[1,2-a]pyridine mesoionics were developed by structurally incorporating sulfonyl piperazine moieties into imidazo[1,2-a]pyridines. Many of the obtained derivatives demonstrated bioactivity against tomato spotted wilt virus (TSWV) in bioassays. In particular, compound Z40, identified via three-dimensional quantitative structure activity relation models, displayed encouraging protective performance (half-maximal effect concentration = 252 μg/mL) compared to the positive controls ningnanmycin (332 μg/mL) and vanisulfane (523 μg/mL). Through defense enzyme assays, real-time quantitative polymerase chain reaction, and proteomics analysis, Z40 was identified as a plant immunomodulator that promotes defense enzyme activity and the mediates oxidative phosphorylation pathway, enabling plants to resist TSWV. We expect this study to help expand the possibilities of mesoionic compounds.

Boosting Sinh Cosh Optimizer and arithmetic optimization algorithm for improved prediction of biological activities for indoloquinoline derivatives

Quantitative Structure Activity Relation (QSAR) models are mathematical techniques used to link structural characteristics with biological activities, thus considered a useful tool in drug discovery, hazard evaluation, and identifying potentially lethal molecules. The QSAR regulations are determined by the Organization for Economic Cooperation and Development (OECD). QSAR models are helpful in discovering new drugs and chemicals to treat severe diseases. In order to improve the QSAR model's predictive power for biological activities of naturally occurring indoloquinoline derivatives against different cancer cell lines, a modified machine learning (ML) technique is presented in this paper. The Arithmetic Optimization Algorithm (AOA) operators are used in the suggested model to enhance the performance of the Sinh Cosh Optimizer (SCHO). Moreover, this improvement functions as a feature selection method that eliminates superfluous descriptors. An actual dataset gathered from previously published research is utilized to evaluate the performance of the suggested model. Moreover, a comparison is made between the outcomes of the suggested model and other established methodologies. In terms of pIC50 values for different indoloquinoline derivatives against human MV4-11 (leukemia), human HCT116 (colon cancer), and human A549 (lung cancer) cell lines, the suggested model achieves root mean square error (RMSE) of 0.6822, 0.6787, 0.4411, and 0.4477, respectively. The biological application of indoloquinoline derivatives as possible anticancer medicines is predicted with a high degree of accuracy by the suggested model, as evidenced by these findings.

Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery

In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemical information such as molecular chirality is ignored. To fill this crucial gap, we propose Molecular-Kernel Graph NeuralNetwork (MolKGNN) for molecular representation learning, which features SE(3)-/conformation invariance, chirality-awareness, and interpretability. For our MolKGNN, we first design a molecular graph convolution to capture the chemical pattern by comparing the atom's similarity with the learnable molecular kernels. Furthermore, we propagate the similarity score to capture the higher-order chemical pattern. To assess the method, we conduct a comprehensive evaluation with nine well-curated datasets spanning numerous important drug targets that feature realistic high class imbalance and it demonstrates the superiority of MolKGNN over other graph neural networks in computer-aided drug discovery. Meanwhile, the learned kernels identify patterns that agree with domain knowledge, confirming the pragmatic interpretability of this approach. Our code and supplementary material are publicly available at https://github.com/meilerlab/MolKGNN.

A structural-chemical explanation of fungal laccase activity

Fungal laccases (EC 1.10.3.2) are multi-copper oxidases that oxidize a wide variety of substrates. Despite extensive studies, the molecular basis for their diverse activity is unclear. Notably, there is no current way to rationally predict the activity of a laccase toward a given substrate. Such knowledge would greatly facilitate the rational design of new laccases for technological purposes. We report a study of three datasets of experimental Km values and activities for Trametes versicolor and Cerrena unicolor laccase, using a range of protein modeling techniques. We identify diverse binding modes of the various substrates and confirm an important role of Asp-206 and His-458 (T. versicolor laccase numbering) in guiding substrate recognition. Importantly, we demonstrate that experimental Km values correlate with binding affinities computed by MMGBSA. This confirms the common assumption that the protein-substrate affinity is a major contributor to observed Km. From quantitative structure-activity relations (QSAR) we identify physicochemical properties that correlate with observed Km and activities. In particular, the ionization potential, shape, and binding affinity of the substrate largely determine the enzyme’s Km for the particular substrate. Our results suggest that Km is not just a binding constant but also contains features of the enzymatic activity. In addition, we identify QSAR models with only a few descriptors showing that phenolic substrates employ optimal hydrophobic packing to reach the T1 site, but then require additional electronic properties to engage in the subsequent electron transfer. Our results advance our ability to model laccase activity and lend promise to future rational optimization of laccases toward phenolic substrates.

Classification of biodegradable materials using QSAR modelling with uncertainty estimation

The ability to determine the biodegradability of chemicals without resorting to expensive tests is ecologically and economically desirable. Models based on quantitative structure–activity relations (QSAR) provide some promise in this direction. However, QSAR models in the literature rarely provide uncertainty estimates in more detail than aggregated statistics such as the sensitivity and specificity of the model’s predictions. Almost never is there a means of assessing the uncertainty in an individual prediction. Without an uncertainty estimate, it is impossible to assess the trustworthiness of any particular prediction, which leaves the model with a low utility for regulatory purposes. In the present work, a QSAR model with uncertainty estimates is used to predict biodegradability for a set of substances from a publicly available data set. Separation was performed using a partial least squares discriminant analysis model, and the uncertainty was estimated using bootstrapping. The uncertainty prediction allows for confidence intervals to be assigned to any of the model’s predictions, allowing for a more complete assessment of the model than would be possible through a traditional statistical analysis. The results presented here are broadly applicable to other areas of modelling as well, because the calculation of the uncertainty will clearly demonstrate where additional tests are needed.

Prediction of Intracellular Localization of Fluorescent Dyes Using QSAR Models.

Control of fluorescent dye localization in live cells is crucial for fluorescence imaging. Here, we describe quantitative structure activity relation (QSAR) models for predicting intracellular localization of fluorescent dyes. For generating the QSAR models, electric charge (Z) calculated by pKa, conjugated bond number (CBN), the largest conjugated fragment (LCF), molecular weight (MW) and log P were used as parameters. We identified the intracellular localization of 119 BODIPY dyes in live NIH3T3 cells, and assessed the accuracy of our models by comparing their predictions with the observed dye localizations. As predicted by the models, no BODIPY dyes localized in nuclei or plasma membranes. The accuracy of the model for localization in fat droplets was 92%, with the models for cytosol and lysosomes showing poorer agreement with observed dye localization, albeit well above chance levels. Overall therefore the utility of QSAR models for predicting dye localization in live cells was clearly demonstrated.

Mechanistic insight into the radical scavenging activity of polyphenols and its application in virtual screening of phytochemical library: an in silico approach

Polyphenols are amply present in fruits, vegetables, beverages and thus very common items in our diet. They are better characterized as antioxidants and are able to scavenge various radicals and singlet oxygen. The present study aims to provide structural and electronic insight into the antioxidant activity of polyphenols and identify novel natural antioxidant. With systematic searches of the large descriptor spaces available in CODESSA, we found that number of benzene ring (constitutional descriptors), electrostatic descriptors related to the hydrogen bonding ability and the lowest unoccupied molecular orbital (LUMO) energy of the polyphenols are very important descriptors related to the electron donation ability of polyphenols, evident from t test values and principal component analysis. Decreasing LUMO energy facilitates electron transfer and stability of the resulting polyphenolic radical increases with the number of attached aromatic ring due to enhanced resonance. Hydrogen bonding present in the parent polyphenol also facilitates electron donation and stabilizes the phenolic radical. Using multiple linear regressions, we have derived two quantitative structure activity relation (QSAR) models with four and five descriptors, respectively, and validated using cross-validation techniques. The calculated square correlation coefficient for four and five descriptor models is R2 = 0.89 and R2 = 0.9, respectively, with cross-validated squared correlation coefficient \({R}_{\text{CV}}^{2}\) = 0.83 and \({R}_{\text{CV}}^{2}\) = 0.87, respectively. Virtual screening using the derived QSAR models on in-house developed phytochemical database identified several highly potent natural antioxidants exhibiting their activity by electron donation coupled proton transfer pathway.

Prediction of potent therapeutic anticonvulsant ligands to N-methyl-d-aspartate receptors among substituted 4,6-dichloroindole-2-carboxylic acids: Molecular modeling approach

We performed two- and three-dimensional quantitative structure–activity relation (QSAR) pharmacophore studies with a series of substituted 4,6-dichloroindole-2-carboxylic acid analogs in order to elucidate the structural properties required for high affinity for the glycine binding site of the N-methyl-d-aspartate receptor. The partial least-squares method coupled with various feature selection methods (stepwise, genetic algorithm and simulated annealing) were used to derive QSAR models, which were validated for statistical significance and predictive ability by internal and external validation. The best two-dimensional QSAR model was selected, which had a correlation coefficient, r2=0.8577, a cross-validated squared correlation coefficient, q2=0.7118 and an external predictive ability pred_r2=0.7642. Molecular field analysis was used to construct the best three-dimensional QSAR model by the genetic algorithm–partial least-squares method, which showed good correlational and predictive capability: q2=0.7538, q2_se=0.4833 and pred_r2=0.7019. In a series of 4,6-dichloroindole-2-carboxylic acid derivatives, the pharmacophore model based on the chemical feature with the lowest root mean square deviation (2.187nm) consisted of one aromatic feature, one hydrogen bond donor, one hydrogen bond acceptor, one aliphatic and one positive coefficient feature. A representative set of 4,6-dichloroindole-2-carboxylic acid compounds with effective biological activity and a good glycine binding site on the N-methyl-d-aspartate receptor were identified, which may be potential leads for drugs with anticonvulsant activity.

Structural requirements of N-aryl-oxazolidinone-5-carboxamide derivatives for anti-HIV protease activity using molecular modelling techniques

Two- and three-dimensional quantitative structure–activity relation (QSAR) models were generated for a series of N-aryl-oxazolidinone-5-carboxamide derivatives to identify potent anti-HIV-1 integrase ligands. The studies were performed by the partial least squares method coupled with various feature selection methods, such as genetic algorithm (GA) and stepwise methods, to derive QSAR models, which were further validated for statistical significance and predictive ability by internal and external validation. The best two-dimensional QSAR model was selected, which had a correlation coefficient, r2, of 0.9246, a cross-validated squared correlation coefficient, q2, of 0.7726 and an external predictive ability, pred_r2, of 0.8331. This model was developed by GA partial least squares with descriptors such as SsNH2 count, SssOE index, T_N_F_4, SsOH count and T_O_O_2. Three-dimensional QSAR studies gave reasonably good predictive models with a high cross-validated q2 value of 0.6953 and pred_r2 of 0.7499 in the GA k nearest neighbour molecular field analysis method. For a series of N-aryl-oxazolidinone-5-carboxamide derivatives, chemical feature-based pharmacophore models with the lowest root mean square deviation value (0.00765nm) showed one aromatic feature, two hydrogen bond acceptors, one hydrogen bond donor and one aliphatic feature. The information provided by two- and three-dimensional QSAR models may lead to better understanding of the structural requirements of oxazolidinone-5-carboxamides and help in designing novel, potent anti-HIV-1 integrase molecules.

Predicting small molecule fluorescent probe localization in living cells using QSAR modeling. 1. Overview and models for probes of structure, properties and function in single cells

Small molecule fluorochromes (synonyms: biosensors, chemosensors, fluorescent probes, vital stains) are widely used to investigate the structure, composition, physicochemical properties and biological functions of living cells, tissues and organisms. Selective entry and accumulation within particular cells and cellular structures are key processes for achieving these diverse objectives. Despite the complexities, probes routinely are applied using standard protocols, often without experimenter awareness of what factors that control accumulation and localization. The mechanisms of many such selective accumulations, however, now are known. Moreover, the influence of physicochemical properties of probes on their uptake and localization often can be defined numerically, hence predicted, using quantitative structure activity relations (QSAR) models with its required numerical structure parameters (or “descriptors”). The state of the art of this approach is described. Available QSAR models are summarized for uptake into cells and localization in the cytosol, endoplasmic reticulum, generic biomembranes, Golgi apparatus, lipid droplets, lysosomes/endosomes, mitochondria, eukaryotic nuclei (histones and DNA), plasma membrane, and ribosomal RNA (cytoplasmic and nucleolar). Integration of such core models to both aid understanding and troubleshooting of current fluorescent probes and to assist the design of novel probes is outlined and illustrated using case examples. Limitations and generic problems arising with this approach and comments on application of such approaches to xenobiotics other than probes, e.g., drugs and herbicides, together with a brief note about an alternative approach to prediction, are given.

Uptake and localisation of small-molecule fluorescent probes in living cells: a critical appraisal of QSAR models and a case study concerning probes for DNA and RNA

Small-molecule fluorochromes are used in biology and medicine to generate informative microscopic and macroscopic images, permitting identification of cell structures, measurement of physiological/physicochemical properties, assessment of biological functions and assay of chemical components. Modes of uptake and precise intracellular localisation of a probe are typically significant factors in its successful application. These processes and localisations can be predicted using quantitative structure activity relations (QSAR) models, which correlate aspects of the physicochemical properties of the probes (expressed numerically) with the uptake/localisation. Pay-offs of such modelling include better understanding and trouble-shooting of current and novel probes, and easier design of future probes ("guided synthesis"). Uptake models discussed consider adsorptive (to lipid or protein domains), phagocytic and pinocytotic endocytosis, as well as passive diffusion. Localisation models discussed include those for cytosol, endoplasmic reticulum, Golgi apparatus, lipid droplets, lysosomes, mitochondria, nucleus and plasma membrane. A case example illustrates how such QSAR modelling of probe interactions can clarify localisation and mode of binding of probes to intracellular nucleic acids of living cells, including not only eukaryotic chromatin DNA and ribosomal RNA, but also prokaryote chromosomes.