SAR and QSAR in Environmental Research最新文献

Alpinia officinarum is a commonly used spice with proven folk uses in various traditional medicines. In the current study, six compounds were isolated from its rhizomes, compounds 1-3 were identified as diarylheptanoids, while 4-6 were identified as flavonoids and phenolic acids. The isolated compounds were subjected to virtual screening against α-glucosidase, butyrylcholinesterase (BChE), and acetylcholinesterase (AChE) enzymes to evaluate their potential antidiabetic and anti-Alzheimer's activities. Molecular docking and dynamics studies revealed that 3 exhibited a strong binding affinity to human a α- glucosidase crystal structure compared to acarbose. Furthermore, 2 and 5 demonstrated high potency against AChE. The virtual screening results were further supported by in vitro assays, which assessed the compounds' effects on α-glucosidase, cholinesterases, and their antioxidant activities. 5-Hydroxy-7-(4-hydroxy-3-methoxyphenyl)-1-phenylheptan-3-one (2) showed potent antioxidant effect in both ABTs and ORAC assays, while p-hydroxy cinnamic acid (6) was the most potent in the ORAC assay. In contrary, kaempferide (4) and galangin (5) showed the most potent effect in metal chelation assay. 5-Hydroxy-1,7-diphenylhepta-4,6-dien-3-one (3) and 6 revealed the most potent effect as α-glucosidase inhibitors where compound 3 showed more potent effect compared to acarbose. Galangin (5) revealed a higher selectivity to BChE, while 2 showed the most potent activity to (AChE).

Phytochemicals are now increasingly exploited as remedial agents for the management of diabetes due to side effects attributable to commercial antidiabetic agents. This study investigated the structural and molecular mechanisms by which betulinic acid exhibits its antidiabetic effect via in vitro and computational techniques. In vitro antidiabetic potential was analysed via on α-amylase, α-glucosidase, pancreatic lipase and α-chymotrypsin inhibitory assays. Its structural and molecular inhibitory mechanisms were investigated using Density Functional Theory (DFT) analysis, molecular docking and molecular dynamics (MD) simulation. Betulinic acid significantly (p < 0.05) inhibited α-amylase, α-glucosidase, pancreatic lipase and α-chymotrypsin enzymes with IC₅₀ of 70.02 μg/mL, 0.27 μg/mL, 1.70 μg/mL and 8.44 μg/mL, respectively. According to DFT studies, betulinic acid possesses similar reaction in gaseous phase and water due to close values observed for highest occupied molecular orbital (HOMO) and lowest occupied molecular orbital (LUMO) and the chemical descriptors. The dipole moment indicates that betulinic acid has high polarity. Molecular electrostatic potential surface revealed the electrophilic and nucleophilic attack-prone atoms of the molecule. Molecular dynamic studies revealed a stable complex between betulinic acid and α-amylase, α-glucosidase, pancreatic lipase and α-chymotrypsin. The study elucidated the potent antidiabetic properties of betulinic acid by revealing its conformational inhibitory mode of action on enzymes involved in the onset of diabetes.

This study aims to comprehensively characterize 576 inhibitors targeting Spleen Tyrosine Kinase (SYK), a non-receptor tyrosine kinase primarily found in haematopoietic cells, with significant relevance to B-cell receptor function. The objective is to gain insights into the structural requirements essential for potent activity, with implications for various therapeutic applications. Through chemoinformatic analyses, we focus on exploring the chemical space, scaffold diversity, and structure-activity relationships (SAR). By leveraging ECFP4 and MACCS fingerprints, we elucidate the relationship between chemical compounds and visualize the network using RDKit and NetworkX platforms. Additionally, compound clustering and visualization of the associated chemical space aid in understanding overall diversity. The outcomes include identifying consensus diversity patterns to assess global chemical space diversity. Furthermore, incorporating pairwise activity differences enhances the activity landscape visualization, revealing heterogeneous SAR patterns. The dataset analysed in this work has three activity cliff generators, CHEMBL3415598, CHEMBL4780257, and CHEMBL3265037, compounds with high affinity to SYK are very similar to compounds analogues with reasonable potency differences. Overall, this study provides a critical analysis of SYK inhibitors, uncovering potential scaffolds and chemical moieties crucial for their activity, thereby advancing the understanding of their therapeutic potential.

The autoignition temperature (AIT) serves as a crucial indicator for assessing the potential hazards associated with a chemical substance. In order to gain deeper insights into model performance and facilitate the establishment of effective methodological practices for AIT predictions, this study conducts a benchmark investigation on Quantitative Structure-Property Relationship (QSPR) modelling for AIT. As novelties of this work, three significant advancements are implemented in the AIT modelling process, including explicit consideration of data quality, utilization of state-of-the-art feature engineering workflows, and the innovative application of graph-based deep learning techniques, which are employed for the first time in AIT prediction. Specifically, three traditional QSPR models (multi-linear regression, support vector regression, and artificial neural networks) are evaluated, alongside the assessment of a deep-learning model employing message passing neural network architecture supplemented by graph-data augmentation techniques.

Transfer learning is a machine learning technique that works well with chemical endpoints, with several papers confirming its efficiency. Although effective, because the choice of source/assistant tasks is non-trivial, the application of this technique is severely limited by the domain knowledge of the modeller. Considering this limitation, we developed a purely data-driven approach for source task selection that abstracts the need for domain knowledge. To achieve this, we created a supervised learning setting in which transfer outcome (positive/negative) is the variable to be predicted, and a set of six transferability metrics, calculated based on information from target and source datasets, are the features for prediction. We used the ChEMBL database to generate 100,000 transfers using random pairing, and with these transfers, we trained and evaluated our transferability prediction model (TP-Model). Our TP-Model achieved a 135-fold increase in precision while achieving a sensitivity of 92%, demonstrating a clear superiority against random search. In addition, we observed that transfer learning could provide considerable performance increases when applicable, with an average Matthews Correlation Coefficient (MCC) increase of 0.19 when using a single source and an average MCC increase of 0.44 when using multiple sources.

In this study, a methodology is proposed, combining ligand- and structure-based virtual screening tools, for the identification of phosphorus-containing compounds as inhibitors of zinc metalloproteases. First, we use Dragon molecular descriptors to develop a Linear Discriminant Analysis classification model, which is widely validated according to the OECD principles. This model is simple, robust, stable and has good discriminating power. Furthermore, it has a defined applicability domain and it is used for virtual screening of the DrugBank database. Second, docking experiments are carried out on the identified compounds that showed good binding energies to the enzyme thermolysin. Considering the potential toxicity of phosphorus-containing compounds, their toxicological profile is evaluated according to Protox II. Of the five molecules evaluated, two show carcinogenic and mutagenic potential at small LD₅₀, not recommended as drugs, while three of them are classified as non-toxic, and could constitute a starting point for the development of new vasoactive metalloprotease inhibitor drugs. According to molecular dynamics simulation, two of them show stable interactions with the active site maintaining coordination with the metal. A high agreement is evident between QSAR, docking and molecular dynamics results, demonstrating the potentialities of the combination of these tools.

Excessive use of chemicals is the outcome of the industrialization of agricultural sectors which leads to disturbance of ecological balance. Various agrochemicals are widely used in agricultural fields, urban green areas, and to protect from various pest-associated diseases. Due to their long-term health and environmental hazards, chronic toxicity assessment is crucial. Since in vivo and in vitro toxicity assessments are costly, lengthy, and require a large number of animal experiments, in silico toxicity approaches are better alternatives to save time, cost, and animal experimentation. We have developed the first regression-based 2D-QSAR models using different sub-chronic and chronic toxicity data of pesticides against dogs employing 2D descriptors. From the statistical results ($n\mathrm{train}=53-62,\hspace{0.17em}{r}^2$ = 0.614 to 0.754, $Q_{LOO}^2$ = 0.501 to 0.703 and $\hspace{0.17em}{Q}_{\left(F1\right)}^2$ = 0.531 to 0.718, $Q_{\left(F2\right)}^2=0.523-0.713$), it was concluded that the models are robust, reliable, interpretable, and predictive. Similarity-based read-across algorithm was also used to improve the predictivity ($Q_{\left(F1\right)}^2=0.595-0.813,Q_{\left(F2\right)}^2=0.573-0.809$) of the models. 5132 chemicals obtained from the CPDat and 1694 pesticides obtained from the PPDB database were also screened using the developed models, and their predictivity and reliability were checked. Thus, these models will be helpful for eco-toxicological data-gap filling, toxicity prediction of untested pesticides, and development of novel, safer & eco-friendly pesticides.

A Disintegrin and Metalloproteinase 17 (ADAM17), a Zn²⁺-dependent metalloenzyme of the adamalysin family of the metzincin superfamily, is associated with various pathophysiological conditions including rheumatoid arthritis and cancer. However, no specific inhibitors have been marketed yet for ADAM17-related disorders. In this study, 94 quinolinyl methoxyphenyl sulphonyl-based hydroxamates as ADAM17 inhibitors were subjected to classification-based molecular modelling and binding pattern analysis to identify the significant structural attributes contributing to ADAM17 inhibition. The statistically validated classification-based models identified the importance of the P1' substituents such as the quinolinyl methoxyphenyl sulphonyl group of these compounds for occupying the S1' - S3' pocket of the enzyme. The quinolinyl function of these compounds was found to explore stable binding of the P1' substituents at the S1' - S3' pocket whereas the importance of the sulphonyl and the orientation of the P1' moiety also revealed stable binding. Based on the outcomes of the current study, four novel compounds of different classes were designed as promising ADAM17 inhibitors. These findings regarding the crucial structural aspects and binding patterns of ADAM17 inhibitors will aid the design and discovery of novel and effective ADAM17 inhibitors for therapeutic advancements of related diseases.

To explore novel terpenoid repellents, 22 candidate terpenoid derivatives were synthesized and tested for their electroantennogram (EAG) responses and repellent activities against Aedes albopictus. The results from the EAG experiments revealed that 5-(2-hydroxypropan-2-yl)-2-methylcyclohex-2-en-1-yl formate (compound 1) induced distinct EAG responses in female Aedes albopictus. At concentrations of 0.1, 1, 10, 100, and 1000 mg/L, the EAG response values for compound 1 were 179.59, 183.99, 190.38, 193.80, and 196.66 mV, demonstrating comparable or superior effectiveness to DEET. Repellent activity analysis indicated significant repellent activity for compound 1, closest to the positive control DEET. The in silico assessment of the ADMET profile of compound 1 indicates that it successfully passed the ADMET evaluation. Molecular docking studies exhibited favourable binding of compound 1 to the active site of the odorant binding protein (OBP) of Aedes albopictus, involving hydrophobic forces and hydrogen bond interactions with residues in the OBP pocket. The QSAR model highlighted the influential role of hydrogen-bonding receptors, positively charged surface area of weighted atoms, polarity parameters of molecules, and maximum nuclear-nuclear repulsion force of carbon-carbon bonds on the relative EAG response values of the tested compounds. This study holds substantial significance for the advancement of new terpenoid repellents.

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.