SAR and QSAR in Environmental Research最新文献

Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating symptoms of dementia that downgrades to cholinergic neuronal loss in Alzheimer's disease. We developed a QSAR model to facilitate the identification of effective butyrylcholinesterase inhibitors. The model employs multi-feature selection and feature learning, improving the in silico screening efficiency and accelerating drug discovery efforts. This study aims to integrate Human Intestinal Absorption (HIA) values of butyrylcholinesterase (BChE) target inhibitors and their 50% inhibitory concentration (IC₅₀) with machine learning tools. The model was developed using chemical descriptors in combination with supervised machine learning classification algorithms. Random Forest Classifier algorithm proved to be the ultimate best fit for classification model metrics including log loss probability (0.04225), accuracy score (98.88%) and Matthew's correlation coefficient (0.98). Furthermore, a subset of the active dataset was used to study the regression based on HIA values using multi-feature selection and feature learning. The models were validated using precision, recall and F1 score for regression modelling. After integrating HIA data with existing machine learning algorithms, we observed a significant reduction of 89.63% in the number of inhibitors. The findings provide valuable pharmacological insights that can help in future design of drug development schemes different from conventional methods.

Methyllysine reading protein Spindlin 1 (SPIN1) plays a crucial role in histone post-translational modifications and serves as an effective target for the treatment of various malignant tumours. Although several inhibitors targeting SPIN1 expression have been identified, the atomic-level interactions between SPIN1 and inhibitors remain unclear. In this study, six potential SPIN1 inhibitors A366, EML631, MS31, MS8535, vinspinln, and XY49-92B were selected for molecular docking with SPIN1. Conformational changes in SPIN1 induced by these inhibitors, as well as their interactions, were investigated using molecular dynamics simulation (MD) and energy prediction methods including molecular mechanics generalized Born surface area (MM-GBSA) and solvation interaction energy (SIE). The findings indicate that the binding pockets within domain II, specifically Phe141, Trp151, Tyr170, and Tyr177, engage in cation-π interactions with these inhibitors, while also contributing to van der Waals hydrophobic interactions of varying strengths. These van der Waals hydrophobic interactions are critical for their binding affinity, while electrostatic interactions are significantly counterbalanced by polar solvation effects. In addition, through virtual screening and molecular docking, a new lead compound CXY49 was found presenting an effective binding to SPIN1. The structural and energetic changes identified in this study provide valuable insights for the development of new SPIN1 inhibitors.

A scheme for constructing models of the 'structure-glass transition temperature of a polymer' is proposed. It involves the representation of the molecular structure of a polymer through the architecture of monomer units represented through a simplified molecular input-line entry system (SMILES) and the fragments of local symmetry (FLS). The statistical quality of such models is quite good: the determination coefficient values for active training set, passive training set, calibration set, and validation set are 0.711, 0.715, 0.859, and 0.884, respectively. The reliability of the approach was assessed for three random distributions of experimental data in the training and validation sets. Machine learning technique was used for a structured training sample distributed in so-called active and passive learning, combined with a calibration set. The optimal descriptors for developed the models were calculated by the Monte Carlo technique.

Pesticides are crucial in modern agriculture, significantly enhancing crop productivity by managing pests. It is important to evaluate their toxicity to minimize health risks to bird species and preserve ecosystem balance. Traditional parameters including lethal concentration (LC₅₀) or median lethal dose (LD₅₀) often underestimate hazards due to limited data and uncertainty about the most sensitive species tested. This limitation can be addressed using extrapolation factors like HD₅ accounting for 50% mortality of the most sensitive 5% of bird species. In this research, a QSTR model was developed utilizing a diverse set of 480 pesticides using partial least squares (PLS) regression with 2D descriptors. Additionally, a PLS-based quantitative read-across structure-toxicity relationship (q-RASTR) and classification based models were constructed. The q-RASTR model outperformed traditional QSTR approaches, achieving robust statistical performance with internal validation metrics r² = 0.623, Q² = 0.569 and external validation metrics Q²_F1 = 0.541, Q²_F2 = 0.540. Key factors influencing avian toxicity were identified. The q-RASTR model was used to screen the Pesticide Properties Database (PPDB) to recognize the most and least toxic pesticides for avian species, aligning well with real-world data. This work provides a more economical and ethical alternative to conventional in vivo testing methods, aiding regulatory bodies and industries in developing safer, environmentally friendly pesticides.

Protein arginylation mediated by arginyltransferase 1 is a crucial regulator of cellular processes in eukaryotes by affecting protein stability, function, and interaction with other macromolecules. This enzyme and its targets are of immense interest for modulating cellular processes in diseased states like obesity and cancer. Despite being an important target molecule, no highly potent drug against this enzyme exists. Therefore, this study focuses on discovering potential inhibitors of human arginyltransferase 1 by computational approaches where screening of over 300,000 compounds from natural and synthetic databases was done using a pharmacophore model based on common features among known inhibitors. The drug-like properties and potential toxicity of the compounds were also assessed in the study to ensure safety and effectiveness. Advanced methods, including molecular simulations and binding free energy calculations, were performed to evaluate the stability and binding efficacy of the most promising candidates. Ultimately, three compounds were identified as potent inhibitors, offering new avenues for developing therapies targeting arginyltransferase 1.

Diabetes mellitus (DM) affects over 77 million adults in India, with cases expected to reach 134 million by 2045. Current treatments, including sulfonylureas and thiazolidinediones, are inadequate, underscoring the need for novel therapeutic strategies. This study investigates marine natural products (MNPs) as alternative therapeutic agents targeting SIK2, a key enzyme involved in DM. The structural stability of the predicted SIK2 model was validated using computational methods and subsequently employed for structure-based virtual screening (SBVS) of over 38,000 MNPs. This approach identified five promising candidates: CMNPD21753 and CMNPD13370 from the Comprehensive Marine Natural Product Database, MNPD10685 from the Marine Natural Products Database, and SWMDRR053 and SWMDRR052 from the Seaweed Metabolite Database. The identified compounds demonstrated docking scores ranging from -7.64 to -11.95 kcal/mol and MMGBSA binding scores between -33.29 and -68.29 kcal/mol, with favourable predicted pharmacokinetic and toxicity profiles. Molecular dynamics simulations (MDS) revealed stronger predicted binding affinity for these compounds compared to ARN-3236, a known SIK2 inhibitor. Principal component (PC)-based free energy landscape (FEL) analysis further supported the stable binding of these compounds to SIK2. These computational findings highlight the potential of these leads as novel SIK2 inhibitors, warranting future in vitro and in vivo validation.

Ras is identified as a human oncogene which is frequently mutated in human cancers. Among its three isoforms (K, N, and H), KRas is the most frequently mutated. Mutant Ras exhibits reduced GTPase activity, leading to the prolonged activation of its conformation. This extended activation promotes Ras-dependent signalling, contributing to cancer cell survival and growth. In this study, we conducted structure-based virtual screening of 11698 phytochemicals in the IMPPAT 2.0 database to identify inhibitors of KRas. We identified two phytochemicals with fair binding affinity, and their binding patterns with KRas were analysed in detail. Additionally, we performed 200 ns molecular dynamics (MD) simulations of each complex to understand the interaction mechanism of KRas with the newly identified compounds, such as Samaderine E and Bismurrayaquinone A. These phytochemicals bind to the binding site residues ARG41 and ASP54, causing conformational changes in KRas. The RMSD, RMSF, Rg, SASA, hydrogen bond, and secondary structure analysis studies suggested the potential of the selected phytochemicals. The identification of Samaderine E and Bismurrayaquinone A as phytochemicals binding to a functional pocket on KRas, supported by PCA and FEL analysis, highlights their potential as effective therapeutic inhibitors of the KRas oncoprotein.

A comprehensive computational strategy that combined QSAR modelling, molecular docking, and ADMET analysis was used to discover potential inhibitors for β-secretase 1 (BACE-1). A dataset of 1,138 compounds with established BACE-1 inhibitory activities was used to build a QSAR model using mol2vec descriptors and support vector regression. The obtained model demonstrated strong predictive performance (training set: r² = 0.790, RMSE = 0.540, MAE = 0.362; test set: r² = 0.705, RMSE = 0.641, MAE = 0.495), indicating its reliability in identifying potent BACE-1 inhibitors. By applying this QSAR model together with molecular docking, seven compounds (ZINC8790287, ZINC20464117, ZINC8878274, ZINC96116481, ZINC217682404, ZINC217786309 and ZINC96113994) were identified as promising candidates, exhibiting predicted log IC₅₀ values ranging from 0.361 to 1.993 and binding energies ranging from -10.8 to -10.7 kcal/mol. Further analysis using ADMET studies and molecular dynamics simulations provided further support for the potential of compound 279 (ZINC96116481) and compound 945 (ZINC96113994) as drug candidates. However, since our study is purely theoretical, further experimental validation through in vitro and in vivo studies is essential to confirm these promising findings.

Bromodomain-containing protein 4 (BRD4) plays an important role in gene transcription in a variety of diseases, including inflammation and cancer. However, the mechanism by which the BRD4 inhibitors bind selectively to its bromodomain 1 (BRD4-BD1) and bromodomain 2 (BRD4-BD2) remains unclear. Studying the interaction mechanism between bromodomain of BRD4 and inhibitors will provide new ideas for drug development and disease treatment. To explore the molecular mechanism of selective binding of three novel phenoxypyridone Cpd11, Cpd14, and Cpd23 to BRD4-BD1 and BRD4-BD2, respectively, molecular docking, molecular dynamics (MD) simulation, and free energy calculation containing molecular mechanics generalized born surface area (MM-GBSA) and solvation interaction energy (SIE) were achieved. The results show that these three inhibitors have different effects on the internal dynamics of BRD4-BD1 and BRD4-BD2, but the key interactions are similar. Key residues of BRD4-BD1 and BRD4-BD2, Ile146/Val439, Trp81/Trp374, Phe83/Phe375, Val87/Val380, Leu92/Leu385, Leu94/Leu387, Tyr97/Tyr390, and Asn140/Asn433, play a key role in selective binding of BRD4-BD1 and BRD4-BD2 to these three inhibitors. At the same time, non-polar interactions, especially van der Waals interactions, are the main drivers of the interactions of these three inhibitors with BRD4-BD1 and BRD4-BD2. These results provide useful dynamic and energy information for the development of novel highly selective phenoxypyridone inhibitors targeting BRD4-BD2.

The Nipah virus (NiV) is an emerging pathogenic paramyxovirus that causes severe viral infection with a high mortality rate. This study aimed to model the effectual binding of marine sponge-derived natural compounds (MSdNCs) towards RNA-directed RNA polymerase (RdRp) of NiV. Based on the functional relevance, RdRp of NiV was selected as the prospective molecular target and 3D-structure, not available in its native form, was modelled. The effectual binding of selected MSdNCs that fulfilled the pharmacokinetics properties were docked against RdRp and the binding energy (BE) of the interaction was compared with the BE of the interaction between standard antiviral compound Remdesivir and RdRp. The stability of the best-docked pose was further confirmed by molecular dynamics (MD) simulation and binding free energy calculations. The current study revealed that the hypothetical RdRp model showed ideal stereochemical features. Molecular docking, dynamic and energy calculations suggested that Hamigeran-B (1R,3aR,9bR)-7- bromo-6-hydroxy-3a,8-dimethyl-1-propan-2-yl-1,2,3,9b-tetrahydrocyclopenta[a]naphthalene-4,5-dione) is a potent binder (BE: -6.35 kcal/mol) to RdRp when compared with the BE of Remdesivir and RdRp (-4.98 kcal/mol). This study suggests that marine sponge-derived Hamigeran-B is a potential binder to NiV-RdRp and that the present in silico model provides insight for future drug discovery against NiV infections.