Search Results (17)

Artificial intelligence transforms lubricant research by linking molecular modeling, diagnostics, and industrial operations into predictive systems. In this regard, machine learning methods such as Bayesian optimization and neural-based Quantitative Structure–Property/Tribological Relationship (QSPR/QSTR) modeling help to accelerate additive design and formulation development. Moreover, deep learning and hybrid physics–AI frameworks are now capable to predict key lubricant properties such as viscosity, oxidation stability, and wear resistance directly from molecular or spectral data, reducing the need for long-duration field trials like fleet or engine endurance tests. With respect to condition monitoring, convolutional neural networks automate wear debris classification, multimodal sensor fusion enables real-time oil health tracking, and digital twins provide predictive maintenance by forecasting lubricant degradation and optimizing drain intervals. AI-assisted blending and process control platforms extend these advantages into manufacturing, reducing waste and improving reproducibility. This article sheds light on recent progress in AI-driven formulation, monitoring, and maintenance, thus identifying major barriers to adoption such as fragmented datasets, limited model transferability, and low explainability. Moreover, it discusses how standardized data infrastructures, physics-informed learning, and secure federated approaches can advance the industry toward adaptive, sustainable lubricant development under the principles of Industry 5.0. Full article

Pharmaceutical and Personal Care Products (PPCPs) have become a significant environmental concern due to their widespread use, persistence, and potential toxicity, often referred to as forever chemicals. This study aims to develop and validate robust in silico models for predicting the aquatic toxicity of PPCPs. To do so, we resorted to the ECOTOX database and employed a Python-based tool to prepare and curate the dataset. Multitasking Quantitative Structure–Toxicity Relationship (mt-QSTR) models were then developed employing the Box–Jenkins moving average approach, incorporating both linear and non-linear frameworks based on diverse feature selection algorithms and machine learning techniques. To further improve the external predictivity, a consensus modeling approach was also implemented. The most accurate model achieved an overall predictive accuracy exceeding 85%, providing valuable insights into the structural features influencing PPCP toxicity. Key factors contributing to high aquatic toxicity included high lipophilicity, mass density, molecular mass, and reduced electronegativity. This work offers a foundation for designing safer PPCPs with reduced environmental impact, aligning with sustainable chemical development goals. Full article

Salinity is one of the major abiotic stresses that abate the yield of several crop species including rice. Several studies were conducted to identify quantitative trait loci (QTLs) for traits associated with salinity tolerance, mostly at the seedling stage of crop growth. However, the reproductive stage of development is highly sensitive to salt stress, and hence, better QTLs must be developed. QTLs have been identified in the present study for salt tolerance of the reproductive stage in rice using recombinant inbred lines (RILs). Thirty-day-old rice seedlings of 184 RILs derived from a cross between a salt sensitive RP Bio226 (indica), and a salt-tolerant Jarava (indica), were used to identify QTLs linked to salinity tolerance in moderate (field) and severe (pot) stress conditions. One hundred polymorphic simple sequence repeat (SSR) markers were used to construct a genetic linkage map that covered a 1349.4 cm genome with an average distance of 13.5 cm between loci. Eighteen new QTLs [logarithm of odds (LOD) 2.5 and above] were identified on chromosomes 1, 2, 6, 10, 11, and 12 using composite interval mapping with the phenotypic variation explained by QTL (PVE) as high as >42% with an LOD value of 5.2. qYLSt-12 with an LOD of 2.8 and a phenotypic variance (PV) of 6.4%, flanked by RM27940-RM27971, was identified for yield in moderate stress conditions. The qSTR-2 detected for salinity tolerance on chromosome 2 with 8.9% of the PV is the most significant finding of the present research. No QTL for salinity component traits has been reported in the region of RM110-RM423. The other salinity trait QTLs identified are qSN-11, qSN-12 for Na⁺ concentration with a total PVE% of 13.9 and qSNK-12.1, qSNK-12.2 for the Na⁺/K⁺ ratio showing a total of 26.7% of the PV. The QTLs for yield component traits viz. plant height, panicle number, panicle length, and biomass were also identified in the present study. Previous studies reported QTLs for salinity tolerance in rice on chromosome 1 but none of the QTLs in our study were on qSaltol or nearby position; therefore, Jarava conferred salinity tolerance in RILs due to novel QTLs. Fine mapping of these novel QTLs is suggested and could be helpful to enhance the level of tolerance through marker-assisted selection for the pyramiding of different QTLs in one background. Full article

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzo-p-furans (PCDD/Fs) are a group of organic chemicals containing three-ring structures that can be substituted with one to eight chlorine atoms, leading to 75 dioxin and 135 furan congeners. As endocrine-disrupting chemicals (EDCs), they can alter physiological processes causing a number of disorders. In this study, quantitative structure–toxicity relationship (QSTR) studies were used to determine the correlations between the PCDD/Fs’ molecular structures and various toxicity endpoints. Strong QSTR models, with the coefficients of determination (r²) values greater than 0.95 and ANOVA p-values less than 0.0001 were established between molecular descriptors and the endpoints of bioconcentration, fathead minnow LC50, and Daphnia magna LC50. The ability of PCDD/Fs to bind to several nuclear receptors was investigated via molecular docking studies. The results show comparable, and in some instances better, binding affinities of PCDD/Fs toward the receptors relative to their natural agonistic and antagonistic ligands, signifying possible interference with the receptors’ natural biological activities. These studies were accompanied by the molecular dynamics simulations of the top-binding PCDD/Fs to show changes in the receptor–ligand complexes during binding and provide insights into these compounds’ ability to interfere with transcription and thereby modify gene expression. This introspection of PCDD/Fs at the molecular level provides a deeper understanding of these compounds’ toxicity and opens avenues for future studies. Full article

Polychlorinated biphenyls (PCBs) are organic chemicals consisting of a biphenyl structure substituted with one to ten chlorine atoms, with 209 congeners depending on the number and position of the chlorine atoms. PCBs are widely known to be endocrine-disrupting chemicals (EDCs) and have been found to be involved in several diseases/disorders. This study takes various molecular descriptors of these PCBs (e.g., molecular weight) and toxicity endpoints as molecular activities, investigating the possibility of correlations via the quantitative structure–toxicity relationship (QSTR). This study then focuses on molecular docking and dynamics to investigate the docking behavior of the strongest-binding PCBs to nuclear receptors and compares these to the docking behavior of their natural ligands. Nuclear receptors are a family of transcription factors activated by steroid hormones, and they have been investigated to consider the impact of PCBs on humans in this context. It has been observed that the docking affinity of PCBs is comparable to that of the natural ligands, but they are inferior in terms of stability and interacting forces, as shown by the RMSD and total energy values. However, it is noted that most nuclear receptors respond to PCBs similarly to how they respond to their natural ligands—as shown in the RMSF plots—the most similar of which are seen in the ER, THR-β, and RAR-α. However, this study is performed purely in silico and will need experimental verification for validation. Full article

In this study, a systematic test of 36 organic liquid compounds as lubricants in the SiC/PI friction pair was conducted to investigate their friction-reducing performance. The back propagation neural network (BPNN) method was employed to establish a quantitative structure tribo-ability relationship (QSTR) model for the friction performance of these lubricants. The developed BPNN-QSTR model exhibited excellent fitting and predictive accuracy, with R² = 0.9700, R² (LOO) = 0.6570, and q² = 0.8606. The impact of different descriptors in the model on the friction-reducing performance of the lubricants was explored. The results provide valuable guidance for the design and optimization of lubricants in SiC/PI friction systems, contributing to the development of high-performance lubrication systems. Full article

Biotransformation refers to the metabolic conversion of endogenous and xenobiotic chemicals into more hydrophilic substances. Xenobiotic biotransformation is accomplished by a restricted number of enzymes with broad substrate specificities. The biotransformation of xenobiotics is catalyzed by various enzyme systems that can be divided into four categories based on the reaction they catalyze. The primary concentration is in cytochrome P450, while the CYP enzymes responsible for xenobiotic biotransformation are located within the hepatic endoplasmic reticulum (microsomes). Cytochrome P450 (CYP450) enzymes are also present in extrahepatic tissues. Enzymes catalyzing biotransformation reactions often determine the intensity and duration of the action of drugs and play a key role in chemical toxicity and chemical tumorigenesis. The structure of a given biotransforming enzyme may differ among individuals, which can cause differences in the rates of xenobiotic biotransformation. The study of the molecular mechanisms underlying chemical liver injury is fundamental for preventing or devising new modalities of treatment for liver injury using chemicals. Active metabolites arise from the biotransformation of a parent drug compound using one or more xenobiotic-processing enzymes to generate metabolites with different pharmacological or toxicological properties. Understanding how exogenous chemicals (xenobiotics) are metabolized, distributed, and eliminated is critical to determining the impact of these compounds on human health. Computational tools such as Biotransformer have been developed to predict all the possible metabolites of xenobiotic and enzymatic profiles that are linked to the production of metabolites. The construction of xenobiotic metabolism maps can predict enzymes catalyzing metabolites capable of binding to DNA. Full article

In recent years, lubricant research has developed from empirical to theoretical, from descriptive to rational, from qualitative to quantitative, and from macroscopic to microscopic studies. This review presents the new concept of the quantitative structure tribo-ability relationship (QSTR) derived from the basic principles of quantitative structure activity relationship (QSAR) theory and reviews the latest advances in research on basic problems of the QSTR of lubricants. Specifically, it highlights a series of recent studies conducted by Chinese scholars and future prospects related to these works. It is noted that the study of lubricants involves many related issues, such that there may be omissions in this review. Additionally, the research topics of the quantitative tribo-ability relationship of lubricants covered in this review are mainly mentioned to introduce various modeling methods, and there may be many similar works that are not covered in this review. Despite these limitations, it is hoped that the described QSTR method will become a useful tool and serve as a reference for tribology research groups in the future. Full article

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure–Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD₅₀). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R² = 0.88), validation set (R² = 0.95), and true external test set (R² = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors. Full article

One of the most widespread representatives of mosses in the temperate and boreal latitudes of the Northern Hemisphere is common haircap (Polytrichum commune), which is known as the largest moss in the world and widely used in traditional herbal medicine. Polyphenolic compounds constitute one of the most important groups of biologically active secondary metabolites of P. commune, however, the available information on their chemical composition is still incomplete and contradictory. In the present study, a group of dihydrochalcone polyphenolic derivatives that were not previously found in mosses was isolated from P. commune biomass using pressurized liquid extraction with aqueous acetone. The combination of two-dimensional NMR spectroscopy and high-performance liquid chromatography–high-resolution mass spectrometry allowed for identifying them as 3-hydroxyphloretin oligomers formed through a carbon–carbon bond between phloroglucinol and pyrocatechol moieties (“head-to-tail” coupling), with a polymerization degree of 2–5. The individual compounds isolated by preparative reverse-phase HPLC had a purity of 71 to 97% and demonstrated high radical scavenging activity (17.5–42.5% with respect to Trolox) determined by the photochemiluminescence method. Along with the low toxicity predicted by QSAR/QSTR algorithms, this makes 3-hydroxyphloretin oligomers a promising source for the production of biologically active food additives and pharmaceuticals. Full article

Compounds containing carbamate moieties and their derivatives can generate serious public health threats and environmental problems due their high potential toxicity. In this study, a quantitative structure–toxicity relationship (QSTR) model has been developed by using one hundred seventy-eight carbamate derivatives whose toxicities in rats (oral administration) have been evaluated. The QSRT model was rigorously validated by using either tested or untested compounds falling within the applicability domain of the model. A structure-based evaluation by docking from a series of carbamates with acetylcholinesterase (AChE) was carried out. The toxicity of carbamates was predicted using physicochemical, structural, and quantum molecular descriptors employing a DFT approach. A statistical treatment was developed; the QSRT model showed a determination coefficient (R²) and a leave-one-out coefficient (Q²_LOO) of 0.6584 and 0.6289, respectively. Full article

The neurotoxic, neuroprotective and MAO-B inhibitory effects of series N′-substituted 3-(1,3,7-trimethyl-xanthin-8-ylthio)propanehydrazides are evaluated. The results indicate compounds N′-(2,3-dimethoxybenzylidene)-3-(1,3,7-trimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purin-8-ylthio)propanehydrazide (6k) and N′-(2-hydroxybenzylidene)-3-(1,3,7-trimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purin-8-ylthio)propanehydrazide (6l) as most perspective. The performed QSTR analysis identified that the decreased lipophilicity and smaller dipole moments of the molecules are the structural features ensuring lower neurotoxicity. The obtained results may be used as initial information in the further design of (xanthinyl-8-ylthio)propanhydrazides with potential hMAOB inhibitory effect and pronounced neuroprotection. Full article

The human telomere region is known to contain guanine-rich repeats and form a guanine-quadruplex (G4) structure. As telomeres play a role in the regulation of cancer progression, ligands that specifically bind and stabilize G4 have potential therapeutic applications. However, as the human telomere sequence can form G4 with various topologies due to direct interaction by ligands and indirect interaction by the solution environment, it is of great interest to study the topology-dependent control of replication by ligands. In the present study, a DNA replication assay of a template with a human telomere G4 sequence in the presence of various ligands was performed. Cyclic naphthalene diimides (cNDI1 and cNDI2) efficiently increased the replication stall of the template DNA at G4 with an anti-parallel topology. This inhibition was stability-dependent and topology-selective, as the replication of templates with hybrid or parallel G4 structures was not affected by the cNDI and cNDI2. Moreover, the G4 ligand fisetin repressed replication with selectivity for anti-parallel and hybrid G4 structures without stabilization. Finally, the method used, referred to as quantitative study of topology-dependent replication (QSTR), was adopted to evaluate the correlation between the replication kinetics and the stability of G4. Compared to previous results obtained using a modified human telomere sequence, the relationship between the stability of G4 and the effect on the topology-dependent replication varied. Our results suggest that native human telomere G4 is more flexible than the modified sequence for interacting with ligands. These findings indicate that the modification of the human telomeric sequence forces G4 to rigidly form a specific structure of G4, which can restrict the change in topology-dependent replication by some ligands. Full article

Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = −9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = −6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = −5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine. Full article

A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure–toxicity relationship (QSTR) of these compounds. For the prediction research, a Protein-Protein Interaction (PPI) network was built from the extraction of useful information about protein interactions connected with aconitine cardiotoxicity, based on nearly a decade of literature and the STRING database. The software Cytoscape and the PharmMapper server were utilized to screen for essential proteins in the constructed network. The Calcium-Calmodulin-Dependent Protein Kinase II alpha (CAMK2A) and gamma (CAMK2G) were identified as potential targets. To obtain a deeper insight on the relationship between the toxicity and the structure of aconitine alkaloids, the present study utilized QSAR models built in Sybyl software that possess internal robustness and external high predictions. The molecular dynamics simulation carried out here have demonstrated that aconitine alkaloids possess binding stability for the receptor CAMK2G. In conclusion, this comprehensive method will serve as a tool for following a structural modification of the aconitine alkaloids and lead to a better insight into the cardiotoxicity induced by the compounds that have similar structures to its derivatives. Full article