SAR and QSAR in Environmental Research最新文献

Fraction unbound in plasma (f_u,p) of drugs is an significant factor for drug delivery and other biological incidences related to the pharmacokinetic behaviours of drugs. Exploration of different molecular fragments for f_u,p of different small molecules/agents can facilitate in identification of suitable candidates in the preliminary stage of drug discovery. Different researchers have implemented strategies to build several prediction models for f_u,p of different drugs. However, these studies did not focus on the identification of responsible molecular fragments to determine the fraction unbound in plasma. In the current work, we tried to focus on the development of robust classification-based QSAR models and evaluated these models with multiple statistical metrics to identify essential molecular fragments/structural attributes for fractions unbound in plasma. The study unequivocally suggests various N-containing aromatic rings and aliphatic groups have positive influences and sulphur-containing thiadiazole rings have negative influences for the f_u,p values. The molecular fragments may help for the assessment of the f_u,p values of different small molecules/drugs in a speedy way in comparison to experiment-based in vivo and in vitro studies.

Ionic liquids (ILs) have attracted considerable interest due to their unique properties and prospective uses in various industries. However, their potential toxicity, particularly regarding enzyme inhibition, has become a growing concern. In this study, a QSAR model was proposed to predict the enzyme inhibition toxicity of ILs. A dataset of diverse ILs with corresponding toxicity data against three enzymes was compiled. Molecular descriptors that capture the physicochemical, structural, and topological properties of the ILs were calculated. To optimize the selection of descriptors and develop a robust QSAR model, the chaotic spotted hyena optimization algorithm, a novel nature-inspired metaheuristic, was employed. The proposed algorithm efficiently searches for an optimal subset of descriptors and model parameters, enhancing the predictive performance and interpretability of the QSAR model. The developed model exhibits excellent predictive capability, with high classification accuracy and low computation time. Sensitivity analysis and molecular interpretation of the selected descriptors provide insights into the critical structural features influencing the toxicity of ILs. This study showcases the successful application of the chaotic spotted hyena optimization algorithm in QSAR modelling and contributes to a better understanding of the toxicity mechanisms of ILs, aiding in the design of safer alternatives for industrial applications.

Cathepsin K (CatK), a lysosomal cysteine protease, contributes to skeletal abnormalities, heart diseases, lung inflammation, and central nervous system and immune disorders. Currently, CatK inhibitors are associated with severe adverse effects, therefore limiting their clinical utility. This study focuses on exploring quantitative structure-activity relationships (QSAR) on a dataset of CatK inhibitors (1804) compiled from the ChEMBL database to predict the inhibitory activities. After data cleaning and pre-processing, a total of 1568 structures were selected for exploratory data analysis which revealed physicochemical properties, distributions and statistical significance between the two groups of inhibitors. PubChem fingerprinting with 11 different machine-learning classification models was computed. The comparative analysis showed the ET model performed well with accuracy values for the training set (0.999), cross-validation (0.970) and test set (0.977) in line with OECD guidelines. Moreover, to gain structural insights on the origin of CatK inhibition, 15 diverse molecules were selected for molecular docking. The CatK inhibitors (1 and 2) exhibited strong binding energies of -8.3 and -7.2 kcal/mol, respectively. MD simulation (300 ns) showed strong structural stability, flexibility and interactions in selected complexes. This synergy between QSAR, docking, MD simulation and machine learning models strengthen our evidence for developing novel and resilient CatK inhibitors.

Anaplastic Lymphoma Kinase (ALK) is a receptor tyrosine kinase within the insulin receptor superfamily. Alterations in ALK, such as rearrangements, mutations, or amplifications, have been detected in various tumours, including lymphoma, neuroblastoma, and non-small cell lung cancer. In this study, we outline a computational workflow designed to uncover new inhibitors of ALK. This process starts with a ligand-based exploration of the pharmacophoric space using 13 diverse sets of ALK inhibitors. Subsequently, quantitative structure-activity relationship (QSAR) modelling is employed in combination with a genetic function algorithm to identify the optimal combination of pharmacophores and molecular descriptors capable of elucidating variations in anti-ALK bioactivities within a compiled list of inhibitors. The successful QSAR model revealed three pharmacophores, two of which share three similar features, prompting their merger into a single pharmacophore model. The merged pharmacophore was used as a 3D search query to mine the National Cancer Institute (NCI) database for novel anti-ALK leads. Subsequent in vitro bioassay of the top 40 hits identified two compounds with low micromolar IC₅₀ values. Remarkably, one of the identified leads possesses a novel chemotype compared to known ALK inhibitors.

MMP-2 overexpression is strongly related to several diseases including cancer. However, none of the MMP-2 inhibitors have been marketed as drug candidates due to various adverse effects. Here, a set of sulphonyl pyrrolidines was subjected to validation of molecular modelling followed by binding mode analysis to explore the crucial structural features required for the discovery of promising MMP-2 inhibitors. This study revealed the importance of hydroxamate as a potential zinc-binding group compared to the esters. Importantly, hydrophobic and sterical substituents were found favourable at the terminal aryl moiety attached to the sulphonyl group. The binding interaction study revealed that the S1' pocket of MMP-2 similar to 'a basketball passing through a hoop' allows the aryl moiety for proper fitting and interaction at the active site to execute potential MMP-2 inhibition. Again, the sulphonyl pyrrolidine moiety can be a good fragment necessary for MMP-2 inhibition. Moreover, some novel MMP-2 inhibitors were also reported. They showed the significance of the 3^rd position substitution of the pyrrolidine ring to produce interaction inside S2' pocket. The current study can assist in the design and development of potential MMP-2 inhibitors as effective drug candidates for the management of several diseases including cancers in the future.

Dengue fever, prevalent in Southeast Asian countries, currently lacks effective pharmaceutical interventions for virus replication control. This study employs a strategy that combines machine learning (ML)-based quantitative-structure-activity relationship (QSAR), molecular docking, and molecular dynamics simulations to discover potential inhibitors of the NS3 protease of the dengue virus. We used nine molecular fingerprints from PaDEL to extract features from the NS3 protease dataset of dengue virus type 2 in the ChEMBL database. Feature selection was achieved through the low variance threshold, F-Score, and recursive feature elimination (RFE) methods. Our investigation employed three ML models - support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) - for classifier development. Our SVM model, combined with SVM-RFE, had the best accuracy (0.866) and ROC_AUC (0.964) in the testing set. We identified potent inhibitors on the basis of the optimal classifier probabilities and docking binding affinities. SHAP and LIME analyses highlighted the significant molecular fingerprints (e.g. ExtFP69, ExtFP362, ExtFP576) involved in NS3 protease inhibitory activity. Molecular dynamics simulations indicated that amphotericin B exhibited the highest binding energy of -212 kJ/mol and formed a hydrogen bond with the critical residue Ser196. This approach enhances NS3 protease inhibitor identification and expedites the discovery of dengue therapeutics.

In the search for natural and non-toxic products alternatives to synthetic pesticides, the fumigant and repellent activities of 35 essential oils are predicted in the human head louse (Pediculus humanus capitis) through the Quantitative Structure-Activity Relationships (QSAR) theory. The number of constituents of essential oils with weight percentage composition greater than 1% varies from 1 to 15, encompassing up to 213 structurally diverse compounds in the entire dataset. The 27,976 structural descriptors used to characterizing these complex mixtures are calculated as linear combinations of non-conformational descriptors for the components. This approach is considered simple enough to evaluate the effects that changes in the composition of each component could have on the studied bioactivities. The best linear regression models found, obtained through the Replacement Method variable subset selection method, are applied to predict 13 essential oils from a previous study with unknown property data. The results show that the simple methodology applied here could be useful for predicting properties of interest in complex mixtures such as essential oils.

Human neutrophil elastase (HNE) plays a key role in initiating inflammation in the cardiopulmonary and systemic contexts. Pathological auto-proteolysed two-chain (tc) HNE exhibits reduced binding affinity with inhibitors. Using AutoDock Vina v1.2.0, 66 flavonoid inhibitors, sivelestat and alvelestat were docked with single-chain (sc) HNE and tcHNE. Schrodinger PHASE v13.4.132 was used to generate a 3D-QSAR model. Molecular dynamics (MD) simulations were conducted with AMBER v18. The 3D-QSAR model for flavonoids with scHNE showed r² = 0.95 and q² = 0.91. High-activity compounds had hydrophobic A/A2 and C/C2 rings in the S1 subsite, with hydrogen bond donors at C5 and C7 positions of the A/A2 ring, and the C4' position of the B/B1 ring. All flavonoids except robustaflavone occupied the S1'-S2' subsites of tcHNE with decreased AutoDock binding affinities. During MD simulations, robustaflavone remained highly stable with both HNE forms. Principal Component Analysis suggested that robustaflavone binding induced structural stability in both HNE forms. Cluster analysis and free energy landscape plots showed that robustaflavone remained within the sc and tcHNE binding site throughout the 100 ns MD simulation. The robustaflavone scaffold likely inhibits both tcHNE and scHNE. It is potentially superior to sivelestat and alvelestat and can aid in developing therapeutics targeting both forms of HNE.

Essential oils (EOs) are natural products currently used to control arthropods, and their interaction with insect odorant-binding proteins (OBPs) is fundamental for the discovery of new repellents. This in silico study aimed to predict the potential of EO components to interact with odorant proteins. A total of 684 EO components from PubChem were docked against 23 odorant binding proteins from Protein Data Bank using AutoDock Vina. The ligands and proteins were optimized using Gaussian 09 and Sybyl-X 2.0, respectively. The nature of the protein-ligand interactions was characterized using LigandScout 4.0, and visualization of the binding mode in selected complexes was carried out by Pymol. Additionally, complexes with the best binding energy in molecular docking were subjected to 500 ns molecular dynamics simulations using Gromacs. The best binding affinity values were obtained for the 1DQE-ferutidine (-11 kcal/mol) and 2WCH-kaurene (-11.2 kcal/mol) complexes. Both are natural ligands that dock onto those proteins at the same binding site as DEET, a well-known insect repellent. This study identifies kaurene and ferutidine as possible candidates for natural insect repellents, offering a potential alternative to synthetic chemicals like DEET.

The 3C-like Proteinase (3CLpro) of novel coronaviruses is intricately linked to viral replication, making it a crucial target for antiviral agents. In this study, we employed two fingerprint descriptors (ECFP_4 and MACCS) to comprehensively characterize 889 compounds in our dataset. We constructed 24 classification models using machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), extreme Gradient Boosting (XGBoost), and Deep Neural Networks (DNN). Among these models, the DNN- and ECFP_4-based Model 1D_2 achieved the most promising results, with a remarkable Matthews correlation coefficient (MCC) value of 0.796 in the 5-fold cross-validation and 0.722 on the test set. The application domains of the models were analysed using d_STD-PRO calculations. The collected 889 compounds were clustered by K-means algorithm, and the relationships between structural fragments and inhibitory activities of the highly active compounds were analysed for the 10 obtained subsets. In addition, based on 464 3CLpro inhibitors, 27 QSAR models were constructed using three machine learning algorithms with a minimum root mean square error (RMSE) of 0.509 on the test set. The applicability domains of the models and the structure-activity relationships responded from the descriptors were also analysed.