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This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure-activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure-activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context.

Brown rot of stone fruit, a disease caused by the ascomycete fungus Monilinia fructicola, has caused significant losses to the agricultural industry. In order to explore and discover potential fungicides against M. fructicola, thirty-one novel mandelic acid derivatives containing piperazine moieties were designed and synthesized based on the amide skeleton. Among them, target compound Z 31 exhibited obvious in vitro antifungal activity with the EC 50 value of 11.8 mg/L, and significant effects for the postharvest pears (79.4 % protective activity and 70.5 % curative activity) at a concentration of 200 mg/L. Antifungal activity for the target compounds was found to be significantly improved by the large steric hindrance of the R 1 groups and the electronegative of the piperazines in the molecular structure, according to a three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis. Further mechanism studies have demonstrated that the compound Z 31 can disrupt cell membrane integrity, resulting in increased membrane permeability, release of intracellular electrolytes, and affect the normal growth of hyphae. Additional, morphological study also indicated that Z 31 may disrupt the integrity of the membrane by inducing generate excess endogenous reactive oxygen species (ROS) and resulting in the peroxidation of cellular lipids, which was further verified by the detection of malondialdehyde (MDA) content. These studies have provided the basis for the creation of novel fungicides to prevent brown rot in stone fruits., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Cationic antimicrobial peptides (AMPs) possess broad-spectrum biological activities with less inclination to inducing antibiotic resistance. Herein a battery of amphiphilic amidines were designed by mimicking the characteristics of AMPs. The antifungal activities and the effects to the hyphal morphology and membrane permeability were investigated., Results: The results indicated the inhibitory rates of ten compounds were over 80% to Botrytis cinerea and ten compounds over 90% to Valsa mali Miyabe et Yamada at 50 mg L -1 . The half maximal effective concentration (EC 50 ) values of compound 5g and 6g to V. mali were 1.21 and 1.90 mg L -1 respectively. The protective rate against apple canker of compound 5g reached 93.4% at 100 mg L -1 on twigs, superior to carbendazim (53.3%). When treated with 5g, the cell membrane permeability and leakage of content of V. mali increased, accompanied with the decrease of superoxide dismutase (SOD) and catalase (CAT) level. Concurrently, the mycelial hyphae contracted, wrinkled, and collapsed, providing evidence of membrane perturbation. A three-dimensional quantitative structure-activity relationship (3D-QSAR) between the topic compounds and the EC 50 to V. mali was established showing good predictability (r 2  = 0.971)., Conclusion: Amphiphilic amidines can acquire antifungal activities by acting on the plasmic membrane. Compound 5g could be a promising lead in discovering novel fungicidal candidates. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

Human glutaminyl cyclase (hQC) inhibitors have great potential to be used as anti- Alzheimer's disease (AD) agents by reducing the toxic pyroform of β-amyloid in the brains of AD patients. The four-dimensional quantitative structure activity relationship (4D-QSAR) model of N-substituted urea/thioureas was established with satisfying predictive ability and statistical reliability (Q 2 = 0.521, R 2 = 0.933, R 2 prep = 0.619). By utilizing the developed 4D-QSAR model, a set of new N-substituted urea/thioureas was designed and evaluated for their Absorption Distribution Metabolism Excretion and Toxicity (ADMET) properties. The results of molecular dynamics (MD) simulations, Principal component analysis (PCA), free energy landscape (FEL), dynamic cross-correlation matrix (DCCM) and molecular mechanics generalized Born Poisson-Boltzmann surface area (MM-PBSA) free energy calculations, revealed that the designed compounds were remained stable in protein binding pocket and compounds b ∼ f (-35.1 to -44.55 kcal/mol) showed higher binding free energy than that of compound 14 (-33.51 kcal/mol). The findings of this work will be a theoretical foundation for further research and experimental validation of urea/thiourea derivatives as hQC inhibitors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The use of salicylates as flavoring agents in food and beverages is common, but their potential to disrupt the endocrine system remains unclear. Human placental 3β-hydroxysteroid dehydrogenase 1 (h3β-HSD1) plays a role in progesterone synthesis and is the potential target. This study evaluated the inhibition of 13 salicylates on h3β-HSD1, structure-activity relationship (SAR) and compared with rat placental homolog r3β-HSD4. Salicylates inhibited h3β-HSD1, depending on carbon chain number in the alcohol moiety and the IC 50 values for hexyl, ethylhexyl, homomenthyl, and menthyl salicylates were 53.27, 15.78, 2.35, and 2.31 μM, as mixed inhibitors, respectively, while methyl to benzyl salicylates were ineffective at 100 μM. Interestingly, only hexyl salicylate inhibited r3β-HSD4 with IC 50 of 31.05 μM. Bivariate analysis revealed a negative correlation between IC 50 and hydrophobicity (LogP), molecular weight, heavy atoms, and carbon number in the alcohol moiety against h3β-HSD1. Docking analysis demonstrated that these salicylates bind to cofactor binding sites or between the steroid and cofactor binding sites. Additionally, 3D-QSAR showed distinct binding via hydrogen bond donors and hydrophobic regions. In conclusion, the inhibition of h3β-HSD1 by salicylates appears to be dependent on factors such as LogP, molecular weight, heavy atoms, and carbon-chain length and there is species-dependent inhibition sensitivity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Succinate dehydrogenase inhibitor (SDHI) fungicides play important roles in the control of plant fungal diseases. However, they are facing serious challenges from issues with resistance and cross-resistance, primarily attributed to their frequent application and structural similarities. There is an urgent need to design and develop SDHI fungicides with novel structures., Results: Aiming to discover novel potent SDHI fungicides, 31 innovative pyrazole β-ketonitrile derivatives with diphenyl ether moiety were rationally designed and synthesized, which were guided by a 3D-QSAR model from our previous study. The optimal target compound A23 exhibited not only outstanding in vitro inhibitory activities against Rhizoctonia solani with a half-maximal effective concentration (EC 50 ) value of 0.0398 μg mL -1 comparable to that for fluxapyroxad (EC 50  = 0.0375 μg mL -1 ), but also a moderate protective efficacy in vivo against rice sheath blight. Porcine succinate dehydrogenase (SDH) enzymatic inhibitory assay revealed that A23 is a potent inhibitor of SDH, with a half-maximal inhibitory concentration of 0.0425 μm. Docking study within R. solani SDH indicated that A23 effectively binds into the ubiquinone site mainly through hydrogen-bonds, and cation-π and π-π interactions., Conclusion: The identified β-ketonitrile compound A23 containing diphenyl ether moiety is a potent SDH inhibitor, which might be a good lead for novel fungicide research and optimization. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

Amomum dealbatum Roxb. parts have been traditionally used as remedies for joint pain, diabetes, muscular rheumatism, antiseptic, and abscesses in Arunachal Pradesh, Assam, and Tripura. Ethyl acetate sub-fraction E3 had significantly inhibited the α-glucosidase (IC 50 5.385 μg/mL). The molecular docking revealed quercetin-3-O-galactoside to be the most potent α-glucosidase inhibitor (binding energy -43.214 kcal/mol). Using the QSAR model, the pIC 50 values of myricetin, gallic acid, quercetin-3-O-galactoside, and acarbose were predicted to be 5.65235, 4.39858, 5.65235, and 6.03058, respectively. For the first time, quercetin-3-O-galactoside, myricetin, and gallic acid have been isolated from the flowers of A. dealbatum (ADF). E3 decreased blood glucose level to a near-normal concentration (100.60 ± 2.94 mg/dL) in comparison to diabetic control rats (575.20 ± 24.80 mg/dL). The results have strongly suggested the potential of ADF in treating diabetes. This lesser-known plant has the potential to uncover its full medicinal properties through further in-depth research.

Acute myeloid leukemia, a serious condition affecting stem cells, drives uncontrollable myeloblast proliferation, leading to accumulation. Extensive research seeks rapid, effective chemotherapeutics. A potential option is a BRD4 inhibitor, known for suppressing cell proliferation. Sulfonamide derivatives probed essential structural elements for potent BRD4 inhibitors. To achieve this goal, we employed 3D-QSAR molecular modeling techniques, including CoMFA, CoMSIA, and HQSAR models, along with molecular docking and molecular dynamics simulations. The validation of the 2D/3D QSAR models, both internally and externally, underscores their robustness and reliability. The contour plots derived from CoMFA, CoMSIA, and HQSAR analyses played a pivotal role in shaping the design of effective BRD4 inhibitors. Importantly, our findings highlight the advantageous impact of incorporating bulkier substituents on the pyridinone ring and hydrophobic/electrostatic substituents on the methoxy-substituted phenyl ring, enhancing interactions with the BRD4 target. The interaction mode of the new compounds with the BRD4 receptor (PDB ID: 4BJX) was investigated using molecular docking simulations, revealing favorable binding energies, supported by the formation of hydrogen and hydrophobic bonds with key protein residues. Moreover, these novel inhibitors exhibited good oral bioavailability and demonstrated non-toxic properties based on ADMET analysis. Furthermore, the newly designed compounds along with the most active one from series 58, underwent a molecular dynamics simulation to analyze their behavior. The simulation provided additional evidence to support the molecular docking results, confirming the sustained stability of the analyzed molecules over the trajectory. This outcome could serve as a valuable reference for designing and developing novel and effective BRD4 inhibitors.Communicated by Ramaswamy H. Sarma.

Tuberculosis (TB) is an infectious disease that primarily affects the lungs of humans and accounts for Mycobacterium tuberculosis (Mtb) bacteria as the etiologic agent. In this study, we introduce a computational framework designed to identify the important chemical features crucial for the effective inhibition of Mtb β-CAs. Through applying a mechanistic model, we elucidated the essential features pivotal for robust inhibition. Using this model, we engineered molecules that exhibit potent inhibitory activity and introduce relevant novel chemistry. The designed molecules were prioritized for synthesis based on their predicted pKi values via the QSAR (Quantitative Structure-Activity Relationship) model. All the rationally designed and synthesized compounds were evaluated in vitro against different carbonic anhydrase isoforms expressed from the pathogen Mtb; moreover, the off-target and widely human-expressed CA I and II were also evaluated. Among the reported derivatives, 2, 4, and 5 demonstrated the most valuable in vitro activity, resulting in promising candidates for the treatment of TB infection. All the synthesized molecules exhibited favorable pharmacokinetic and toxicological profiles based on in silico predictions. Docking analysis confirmed that the zinc-binding groups bind effectively into the catalytic triad of the Mtb β-Cas, supporting the in vitro outcomes with these binding interactions. Furthermore, molecules with good prediction accuracies according to previously established mechanistic and QSAR models were utilized to delve deeper into the realm of systems biology to understand their mechanism in combating tuberculotic pathogenesis. The results pointed to the key involvement of the compounds in modulating immune responses via NF-κβ1, SRC kinase, and TNF-α to modulate granuloma formation and clearance via T cells. This dual action, in which the pathogen's enzyme is inhibited while modulating the human immune machinery, represents a paradigm shift toward more effective and comprehensive treatment approaches for combating tuberculosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The mesenchymal-epithelial transition factor (c-Met) is a tyrosine kinase receptor protein, and excessive cell transformation can lead to cancer. Therefore, there is an urgent need to develop novel receptor tyrosine kinase inhibitors by inhibiting the activity of c-Met protein. In this study, 41 compounds are selected from the reported literature, and the interactions between phenoxy pyridine derivatives and tumor-associated proteins are systematically investigated using a series of computer-assisted drug design (CADD) methods, aiming to predict potential c-Met inhibitors with high activity. The Topomer CoMFA (q 2 =0.620, R 2 =0.837) and HQSAR (q 2 =0.684, R 2 =0.877) models demonstrate a high level of robustness. Further internal and external validation assessments show high applicability and accuracy. Based on the results of the Topomer CoMFA model, structural fragments with higher contribution values are identified and randomly combined using a fragment splice technique, result in a total of 20 compounds with predicted activities higher than the template molecules. Molecular docking results show that these compounds have good interactions and van der Waals forces with the target proteins. The results of molecular dynamics and ADMET predictions indicate that compounds Y4, Y5, and Y14 have potential as c-Met inhibitors. Among them, compound Y14 exhibits superior stability with a binding free energy of -165.18 KJ/mol. These studies provide a reference for the future design and development of novel compounds with c-Met inhibitory activity., (© 2024 Wiley-VHCA AG, Zurich, Switzerland.)

Targeting cancer cells through drug-based treatment or combination therapy protocols involving chemical compounds can be challenging due to multiple factors, including their resistance to bioactive compounds and the potential of drugs to damage healthy cells. This study aims to investigate the relationship between the structure of novel sulfur-containing shikonin oxime compounds and the corresponding cytotoxicity against four cancer types, namely colon, gastric, liver, and breast cancers, through computational chemistry tools. This investigation is suggested to help build insights into how the structure of the compounds influences their activity and understand the mechanisms behind it and subsequently might be used in multi-cancer drug design process to propose novel optimized compounds that potentially exhibit the desired activity. The findings showed that the cytotoxic activity against the four cancer types was accurately predictable (R 2  > 0.7, NRMSE <20%) by a combination of search and machine learning algorithms, based on the information on the structure of the compounds, including their lipophilicity, surface area, and volume. Overall, this study is supposed to play a crucial role in effective multi-cancer drug design in cancer research areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Malaria is a disease caused mostly by Plasmodium falciparum, affects millions of people each year. The kinases are validated targets for malaria infection. In this study, we investigate for real and hypothetical compounds that can inhibit cyclic guanosine monophosphate (CGMP)-dependent protein kinase using molecular docking via combined similarity analysis, molecular dynamics simulations, quantitative structure activity relationship (QSAR). Using Tanimoto similarity scores, ∼8.4 million compounds were screened. Compounds that have at least 70% similarity are used in further analysis. These compounds are assessed by means of docking, MMBPSA, MMGBSA and ANI&#95;LIE. Based on consensus of different free energy methods and docking we revealed two potential inhibitors that can be useful for treatment of malaria. Apart from screening of real compounds, we have also selected the 10 most plausible hypothetical compounds by performing QSAR. By QSAR proposed pharmacophores, we generated over 247 hypothetical compounds and among them 19 molecules with lower QSAR predicted IC50 values and high docking scores were selected for further analysis. We selected the top 10 inhibitor candidates and performed MD simulations for free energy calculations like the protocol applied for real compounds. According to the free energy calculations, we suggest 2 real (C 34 H 29 F 5 N 8 O 4 S and C 30 H 27 F 2 N 7 O 2 S 2 , PubChem IDs: 140564801 and 89035196, respectively) and 2 hypothetical (C 23 H 27 FN 6 O 2 S, MOL3 and C 23 H 25 FN 6 O 2 S, MOL4) compounds that can be effective inhibitors against the protein kinase of Plasmodium falciparum.Communicated by Ramaswamy H. Sarma.

Fibroblast Growth Receptor Factor (FGFR) are a family of proteins which are, in addition to their biological role, are involved in various pathological functions, such as cancer cellular proliferation, and metastasis. Deregulation of FGFRs at various points could result in malignancy. A conformational transition of the DFG (Asp-Phe-Gly) motif can switch the enzyme from a catalytically active (DFG-in) to an inactive (DFG-out) state. There are a few FDFR inhibitors which have received approval from the FDA, but these have adverse side effects. Hence, there is a demand for safer alternatives. With this aim, Ligand and Structure based virtual screening was carried to identify suitable lead molecule. In this process, Four Featured atom-based 3D Pharmacophore with quantitative structure-activity relationship analysis (3D-QSAR) was developed. The External validation of the hypothesis was carried invoking criteria such as Area under the ROC curve. Natural plant compound databases such as the Traditional Chinese medicine, NPACT and the ZINC Natural databases were chosen for pharmacophore filtering, which was followed by virtual screening against FGFR isoforms. The compound Sanggenol B was identified as the most suitable lead molecule. Structural stability of the protein-ligand complex and interactions of the ligand (Sanggenol B & the reference compound Ponatinib) with FGFR were analysed for 1000 ns (triplicate) by means of molecular simulation and the binding free energy was calculated using MMGBSA. Sanggenol B (PubChem CID: 15233694) binds effectively at the active site with favourable energies and is proposed as a safe alternative from a natural source.Communicated by Ramaswamy H. Sarma.

Within the realm of pharmacological strategies for cardiovascular diseases (CVD) like hypertension, stroke, and heart failure, targeting the angiotensin-converting enzyme I (ACE-I) stands out as a significant treatment approach. This study employs QSAR modeling using Monte Carlo optimization techniques to investigate a range of compounds known for their ACE-I inhibiting properties. The modeling process involved leveraging local molecular graph invariants and SMILES notation as descriptors to develop conformation-independent QSAR models. The dataset was segmented into distinct sets for training, calibration, and testing to ensure model accuracy. Through the application of various statistical analyses, the efficacy, reliability, and predictive capability of the models were evaluated, showcasing promising outcomes. Additionally, molecular fragments derived from SMILES notation descriptors were identified to elucidate the activity changes observed in the compounds. The validation of the QSAR model and designed inhibitors was carried out via molecular docking, aligning well with the QSAR results. To ascertain the drug-worthiness of the designed molecules, their physicochemical properties were computed, aiding in the prediction of ADME parameters, pharmacokinetic attributes, drug-likeness, and medicinal chemistry compatibility., Competing Interests: Declaration of Competing Interest We have no conflicts of interest to disclose., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Thymidylate synthase (TS) is a crucial target of cancer drug discovery and is mainly involved in the De novo synthesis of the DNA precursor thymine. In the present study, to generate reliable models and identify a few promising molecules, we combined QSAR modelling with the pharmacophore hypothesis-generating technique. Input molecules were clustered on their similarity, and a cluster of 74 molecules with a pyrimidine moiety was chosen as the set for 3D-QSAR and pharmacophore modelling. Atom-based and field-based 3D-QSAR models were generated and statistically validated with R 2 > 0.90 and Q 2 > 0.75. The common pharmacophore hypothesis(CPH) generation identified the best six-point model ADHRRR. Using these best models, a library of FDA-approved drugs was screened for activity and filtered via molecular docking, ADME profiling, and molecular dynamics simulations. The top ten promising TS-inhibiting candidates were identified, and their chemical features profitable for TS inhibitors were explored.Communicated by Ramaswamy H. Sarma.

Polycyclic aromatic compounds (PACs) exhibit rat aryl hydrocarbon receptor (rAhR) activities, leading to diverse biological or toxic effects. In this study, the key amino residues and molecular interactions that govern the rAhR activity of PACs were investigated using in silico strategies. The homology model of rAhR was first docked with 90 PACs to yield complexes, and the results of the molecular dynamics simulations of 16 typical complexes showed that the binding energies of the complexes range from -7.37 to -26.39 kcal/mol. The major contribution to the molecular interaction comes from van der Waals forces, and Pro295 and Arg316 become the key residues involved in most complexes. Two QSAR models were further developed to predict the rAhR activity of PACs (in terms of log IEQ for PACs without halogen substitutions and log%-TCDD-max for halogenated PACs). Both models have good predictive ability, robustness, and extrapolation ability. Molecular polarizability, electronegativity, size, and nucleophilicity are identified as the important factors affecting the rAhR activity of PACs. The developed models could be employed to predict the rAhR activity of other reactive PACs. This work provides insight into the mechanisms and interactions of the rAhR activity of PACs and assists in the assessment of their fate and risk in organisms.

Context: Alzheimer's disease (AD) is the leading cause of dementia around the world, totaling about 55 million cases, with an estimated growth to 74.7 million cases in 2030, which makes its treatment widely desired. Several studies and strategies are being developed considering the main theories regarding its origin since it is not yet fully understood. Among these strategies, the 5-HT 6 receptor antagonism emerges as an auspicious and viable symptomatic treatment approach for AD. The 5-HT 6 receptor belongs to the G protein-coupled receptor (GPCR) family and is closely implicated in memory loss processes. As a serotonin receptor, it plays an important role in cognitive function. Consequently, targeting this receptor presents a compelling therapeutic opportunity. By employing antagonists to block its activity, the 5-HT 6 receptor's functions can be effectively modulated, leading to potential improvements in cognition and memory., Methods: Addressing this challenge, our research explored a promising avenue in drug discovery for AD, employing Artificial Neural Networks-Quantitative Structure-Activity Relationship (ANN-QSAR) models. These models have demonstrated great potential in predicting the biological activity of compounds based on their molecular structures. By harnessing the capabilities of machine learning and computational chemistry, we aimed to create a systematic approach for analyzing and forecasting the activity of potential drug candidates, thus streamlining the drug discovery process. We assembled a diverse set of compounds targeting this receptor and utilized density functional theory (DFT) calculations to extract essential molecular descriptors, effectively representing the structural features of the compounds. Subsequently, these molecular descriptors served as input for training the ANN-QSAR models alongside corresponding biological activity data, enabling us to predict the potential efficacy of novel compounds as 5-hydroxytryptamine receptor 6 (5-HT 6 ) antagonists. Through extensive analysis and validation of ANN-QSAR models, we identified eight new promising compounds with therapeutic potential against AD., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In the realm of crop protection products, ensuring the safety of pollinators stands as a pivotal aspect of advancing sustainable solutions. Extensive research has been dedicated to this crucial topic as well as new approach methodologies in toxicity testing. Hence, within the agricultural and chemical industries, prioritizing pollinator safety remains a constant objective during the development of predictive tools. One of these tools includes computational models like quantitative structure-activity relationships (QSARs) that are valuable in predicting the toxicity of chemicals. This research uses bee toxicity data to develop artificial neural network classification models for predicting honey bee acute toxicity. Bee toxicity data from 1542 compounds were used to develop models; the sensitivity and specificity of the best model were 0.90 and 0.91, respectively. These in silico models can aid in the discovery of next-generation crop protection products. These tools can guide the screening and selection of next-generation crop protection molecules with high margins of safety to pollinators, and candidates with favorable sustainability profiles can be identified at the early discovery stage as precursors to in vivo data generation.

Post-translational modifications such as protein N-glycosylation, significantly influence cellular processes. Dysregulated N-glycosylation, exemplified in Grp94, a member of the Hsp90 family, leads to structural changes and the formation of epichaperomes, contributing to pathologies. Targeting N-glycosylation-induced conformations offers opportunities for developing selective chemical tools and drugs for these pathologic forms of chaperones. We here demonstrate how a specific Grp94 conformation induced by N-glycosylation, identified previously via molecular dynamics simulations, rationalizes the distinct behavior of similar ligands. Integrating dynamic ligand unbinding information with SAR development, we differentiate ligands productively engaging the pathologic Grp94 conformers from those that are not. Additionally, analyzing binding site stereoelectronic properties and QSAR models using cytotoxicity data unveils relationships between chemical, conformational properties, and biological activities. These findings facilitate the design of ligands targeting specific Grp94 conformations induced by abnormal glycosylation, selectively disrupting pathogenic protein networks while sparing normal mechanisms., (© 2024 The Author(s). Chemistry - A European Journal published by Wiley-VCH GmbH.)

Bioluminescence (BL) generated by luciferase-coelenterazine (CTZ) reactions is broadly employed as an optical readout in bioassays and in vivo molecular imaging. In this study, we demonstrate a systematic approach to elucidate the luciferase-CTZ binding chemistry with a full set of regioisomeric CTZ analogs, where all the functional groups were regiochemically modified. When the chemical structures were categorized into Groups 1-6, the even-numbered Groups (2, 4, and 6) of the CTZ analogs are found to be exceptionally bright with NanoLuc enzyme. A CTZ analogue M2 was the brightest with NanoLuc and the reason was deciphered by a computational analysis of the binding modes. We also report that (i) the regioisomeric CTZ analogs collectively create unique intensity patterns according to each marine luciferase, (ii) the quantitative structure-activity relationship analysis revealed the roles of respective functional groups of CTZ analogs, and (iii) the regioisomeric CTZ analogs also exert red shifts of the BL spectra and color variation: that is, the λ max values are near 500 nm with NanoLuc, near 530 nm with ALuc16, and near 570 nm with RLuc86SG. The advantages of the regioisomeric CTZ analogs were finally demonstrated using (i) a dual-luciferase system with M2 -specific NanoLuc and native CTZ-specific ALuc16, (ii) an estrogen activatable single-chain BL probe by imaging, and (iii) BL imaging of live mice bearing tumors expressing NanoLuc and RLuc8.6SG. This study is the first systematic approach to elucidate the regiochemistry in BL imaging studies. This study provides new insights into how CTZ analogs regiochemically work in BL reporter systems and guides the specific applications to molecular imaging.

Salicylic esters (SEs), the widely used ultraviolet (UV) absorbers in sunscreen products, have been found to have health risks such as skin sensitization and estrogenic effects. This study aims to design SE substitutes that maintain high UV absorbance while reducing estrogenicity. Using molecular docking and Gaussian09 software for initial assessments and further application of a combination of two-dimensional and three-dimensional quantitative structure-activity relationships (2D-QSAR and 3D-QSAR, respectively) models, we designed 73 substitutes. The best-performing molecules, ethylhexyl salicylate (EHS)-5 and EHS-15, significantly reduced estrogenicity (44.54 % and 17.60 %, respectively) and enhanced UV absorbance (249.56 % and 46.94 %, respectively). Through screening for human health risks, we found that EHS-5 and EHS-15 were free from skin sensitivity and eye irritation and exhibited reduced skin permeability compared with EHS. Furthermore, the photolysis and synthetic pathways of EHS-5 and EHS-15 were deduced, demonstrating their good photodegradability and potential synthesizability. In addition, we analyzed the mechanisms underlying the changes in estrogenic effects and UV absorption properties. We identified covalent hydrogen bond basicity and acidity Propgen value for atomic molecular properties and the highest occupied molecular orbital eigenvalue as the main factors affecting the estrogenic effect and UV absorbance of SEs, respectively. This study focuses on the design and screening of SEs, exhibiting enhanced functionality, reduced health risks, and synthetic feasibility., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The global outbreak of the COVID-19 pandemic caused by the SARS-CoV-2 virus had led to profound respiratory health implications. This study focused on designing organoselenium-based inhibitors targeting the SARS-CoV-2 main protease (M pro ). The ligand-binding pathway sampling method based on parallel cascade selection molecular dynamics (LB-PaCS-MD) simulations was employed to elucidate plausible paths and conformations of ebselen, a synthetic organoselenium drug, within the M pro catalytic site. Ebselen effectively engaged the active site, adopting proximity to H41 and interacting through the benzoisoselenazole ring in a π-π T-shaped arrangement, with an additional π-sulfur interaction with C145. In addition, the ligand-based drug design using the QSAR with GFA-MLR, RF, and ANN models were employed for biological activity prediction. The QSAR-ANN model showed robust statistical performance, with an r 2 training exceeding 0.98 and an RMSE test of 0.21, indicating its suitability for predicting biological activities. Integration the ANN model with the LB-PaCS-MD insights enabled the rational design of novel compounds anchored in the ebselen core structure, identifying promising candidates with favorable predicted IC 50 values. The designed compounds exhibited suitable drug-like characteristics and adopted an active conformation similar to ebselen, inhibiting M pro function. These findings represent a synergistic approach merging ligand and structure-based drug design; with the potential to guide experimental synthesis and enzyme assay testing., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Polycystic kidney disease is the most prevalent hereditary kidney disease globally and is mainly linked to the overexpression of a gene called PKD1. To date, there is no effective treatment available for polycystic kidney disease, and the practicing treatments only provide symptomatic relief. Discovery of the compounds targeting the PKD1 gene by inhibiting its expression under the disease condition could be crucial for effective drug development. In this study, a molecular docking and molecular dynamic simulation, QSAR, and MM/GBSA-based approaches were used to determine the putative inhibitors of the Pkd1 enzyme from a library of 1379 compounds. Initially, fourteen compounds were selected based on their binding affinities with the Pkd1 enzyme using MOE and AutoDock tools. The selected drugs were further investigated to explore their properties as drug candidates and the stability of their complex formation with the Pkd1 enzyme. Based on the physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, and toxicity profiling, two compounds including olsalazine and diosmetin were selected for the downstream analysis as they demonstrated the best drug-likeness properties and highest binding affinity with Pkd1 in the docking experiment. Molecular dynamic simulation using Gromacs further confirmed the stability of olsalazine and diosmetin complexes with Pkd1 and establishing interaction through strong bonding with specific residues of protein. High biological activity and binding free energies of two complexes calculated using 3D QSAR and Schrodinger module, respectively further validated our results. Therefore, the molecular docking and dynamics simulation-based in-silico approach used in this study revealed olsalazine and diosmetin as potential drug candidates to combat polycystic kidney disease by targeting Pkd1 enzyme., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Salmons are crucial to ecosystems and economic activities like commercial fishing and aquaculture, while also serving as an important source of nutrients, underscoring their ecological significance and the need for sustainable management. To better understand the toxicity and biological interactions between the salmon and industrial chemicals in the aquatic environment, we utilized the ToxValDB database to develop first ever computational toxicity models for six salmon subspecies (covering Atlantic and Pacific salmon) across two genera, employing Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) methods. For three smaller datasets (Oncorhynchus nerka, Oncorhynchus keta, and Oncorhynchus gorbuscha), we created mathematical models using the entire datasets where QSAR models demonstrated superior statistical quality compared to q-RASAR. Conversely, the three larger datasets (Oncorhynchus kisutch, Oncorhynchus tshawytscha, and Salmon salar) were divided into training and test sets, the q-RASAR models yielded better results compared to QSAR models. Mechanistic interpretations of these models revealed that descriptors such as Burden eigenvalues (BCUT), autocorrelation of topological structure (ATSC), and molecular polarizability were significant predictors of toxicity. For instance, higher polarizability and certain topological features were associated with increased toxicity as per the developed models. Statistically superior models for each subspecies were used to predict the aquatic toxicity of 1085 untested organic chemicals for toxicity data gap filling and risk assessment considering the applicability domain (AD). These insights are pivotal for designing safer chemicals and emphasize the need for sustainable management of salmon populations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Bitter peptides are small molecular peptides produced by the hydrolysis of proteins under acidic, alkaline, or enzymatic conditions. These peptides can enhance food flavor and offer various health benefits, with attributes such as antihypertensive, antidiabetic, antioxidant, antibacterial, and immune-regulating properties. They show significant potential in the development of functional foods and the prevention and treatment of diseases. This review introduces the diverse sources of bitter peptides and discusses the mechanisms of bitterness generation and their physiological functions in the taste system. Additionally, it emphasizes the application of bioinformatics in bitter peptide research, including the establishment and improvement of bitter peptide databases, the use of quantitative structure-activity relationship (QSAR) models to predict bitterness thresholds, and the latest advancements in classification prediction models built using machine learning and deep learning algorithms for bitter peptide identification. Future research directions include enhancing databases, diversifying models, and applying generative models to advance bitter peptide research towards deepening and discovering more practical applications.

Schistosomiasis, also known as bilharzia or snail fever, is a tropical parasitic disease resulting from flatworms of the Schistosoma genus. This often overlooked disease has significant impacts in affected regions, causing enduring morbidity, hindering child development, reducing productivity, and creating economic burdens. Praziquantel (PZQ) is currently the only treatment option for schistosomiasis. Given the potential rise of drug resistance and the limited treatment choices available, there is a need to develop more effective inhibitors for this neglected tropical disease (NTD). In view of this, quantitative structure-activity relationship studies (QSAR), molecular docking, molecular dynamics simulations, drug-likeness, and ADMET predictions were applied to 31 inhibitors of Schistosoma mansoni Dihydroorotate dehydrogenase (SmDHODH). The designed QSAR model demonstrated robust statistical parameters including an R2 of 0.911, R2adj of 0.890, Q2cv of 0.686, R2pred of 0.807, and cR2p of 0.825, confirming its robustness. Compound 26, identified as the most active derivative, emerged as a lead candidate for new potential inhibitors through ligand-based drug design. Subsequently, 12 novel compounds (26A-26L) were designed with enhanced inhibition activity and binding affinity. Molecular docking studies revealed strong and stable interactions, including hydrogen bonding and hydrophobic interactions, between the designed compounds and the target receptor. Molecular dynamics simulations over 100 nanoseconds and MM-PBSA free binding energy (ΔGbind) calculations validated the stability of the two best-designed molecules (26A and 26L). Furthermore, drug-likeness and ADMET prediction analyses affirmed the potential of these designed compounds, suggesting their promise as innovative agents for treating schistosomiasis., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ja’afaru et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Oleanolic acid, a naturally occurring triterpenoid compound, has garnered significant attention in the scientific community due to its diverse pharmacological properties. Continuing our previous work on the synthesis of oleanolic acid dimers (OADs), a simple, economical, and safe acetylation reaction was performed. The newly obtained derivatives (AcOADs, 3a - 3n ) were purified using two methods. The structures of all acetylated dimers ( 3a - 3n ) were determined based on spectral methods (IR, NMR). For all AcOADs ( 3a - 3n ), the relationship between the structure and the expected directions of pharmacological activity was determined using a computational method (QSAR computational analysis). All dimers were also tested for their cytotoxic activity on the SKBR-3, SKOV-3, PC-3, and U-87 cancer cell lines. HDF cell line was applied to evaluate the Selectivity Index of the tested compounds. All cytotoxic tests were performed with the application of the MTT assay. Finally, all dimers of oleanolic acid were subjected to DPPH and CUPRAC tests to evaluate their antioxidant activity. The obtained results indicate a very high level of cytotoxic activity (IC 50 for most AcOADs below 5.00 µM) and a fairly high level of antioxidant activity (Trolox equivalent in some cases above 0.04 mg/mL)., Competing Interests: The authors declare no conflicts of interest.

With the exponential progress in the field of cheminformatics, the conventional modeling approaches have so far been to employ supervised and unsupervised machine learning (ML) and deep learning models, utilizing the standard molecular descriptors, which represent the structural, physicochemical, and electronic properties of a particular compound. Deviating from the conventional approach, in this investigation, we have employed the classification Read-Across Structure-Activity Relationship (c-RASAR), which involves the amalgamation of the concepts of classification-based quantitative structure-activity relationship (QSAR) and Read-Across to incorporate Read-Across-derived similarity and error-based descriptors into a statistical and machine learning modeling framework. ML models developed from these RASAR descriptors use similarity-based information from the close source neighbors of a particular query compound. We have employed different classification modeling algorithms on the selected QSAR and RASAR descriptors to develop predictive models for efficient prediction of query compounds' hepatotoxicity. The predictivity of each of these models was evaluated on a large number of test set compounds. The best-performing model was also used to screen a true external data set. The concepts of explainable AI (XAI) coupled with Read-Across were used to interpret the contributions of the RASAR descriptors in the best c-RASAR model and to explain the chemical diversity in the dataset. The application of various unsupervised dimensionality reduction techniques like t-SNE and UMAP and the supervised ARKA framework showed the usefulness of the RASAR descriptors over the selected QSAR descriptors in their ability to group similar compounds, enhancing the modelability of the dataset and efficiently identifying activity cliffs. Furthermore, the activity cliffs were also identified from Read-Across by observing the nature of compounds constituting the nearest neighbors for a particular query compound. On comparing our simple linear c-RASAR model with the previously reported models developed using the same dataset derived from the US FDA Orange Book ( https://www.accessdata.fda.gov/scripts/cder/ob/index.cfm ), it was observed that our model is simple, reproducible, transferable, and highly predictive. The performance of the LDA c-RASAR model on the true external set supersedes that of the previously reported work. Therefore, the present simple LDA c-RASAR model can efficiently be used to predict the hepatotoxicity of query chemicals., (© 2024. The Author(s).)

The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC 50 ) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R 2 > 0.6 and Q LOO 2 > 0.6) and external predictivity (R test 2 > 0.7, Q Fn 2 >0.7, and CCC test > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Fluoroquinolone antibiotics (FQs) have been used worldwide due to their extended antimicrobial spectrum. However, the overuse of FQs leads to frequent detection in the environment and cannot be efficiently removed. Microalgae-based constructed wetland systems have been proven to be a relatively proper method to treat FQs, mainly by microalgae, plants, microorganisms, and sediments. To improve the removal efficiency of microalgae-constructed wetland, a systematic molecular design, screening, functional, and risk evaluation method was developed using three-dimensional quantitative structure-activity relationship models, molecular dynamics simulation, molecular docking, and TOPKAT approaches. Five designed ciprofloxacin alternatives with improved bactericidal effects and lower human health risks were found to be more easily degraded by microalgae (16.11-167.88 %), plants (6.72-58.86 %), microorganisms (9.10-15.02 %), and sediments (435.83 %-1763.51 %) compared with ciprofloxacin. According to the mechanism analysis, the removal effect of the FQs can be affected via changes in the number, bond energy, and molecular descriptors of favorable and unfavorable amino acids. To the best of our knowledge, this is the first comprehensive study of improving the microalgae, plants, microorganisms, and sediment removal efficiency of FQs in constructed wetlands, which provides theoretical support for the treatment of FQ pollution., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

C-X-C chemokine receptor type 4 (CXCR4) exerts considerable influence on the pathogenesis of inflammatory disorders and offers a potent avenue for drug intervention. This research utilizes a hybrid virtual screening methodology constructed using computer-aided drug design to discover novel CXCR4 inhibitors for the treatment of inflammation. First, a compound library was screened by Lipinski's five rules and adsorption, distribution, metabolism, excretion and toxicity properties. Second, the HypoGen algorithm was used in constructing a 3D-QSAR pharmacophore model and verify it layer by layer, and the obtained optimal pharmacophore 1 (Hypo 1) was used as a 3D query for compound screening. Then, hit compounds were obtained through molecular docking (Libdock and CDOCKER). The toxicity of the compounds to MDA-MB-231 cells was evaluated in vitro, and their binding affinity to the target was evaluated according to how they compete with 12G5 antibody for CXCR4 on the surfaces of the MDA-MB-231 cells. Compound Hit14 showed the strongest binding affinity among the hit compounds and inhibited cell migration and invasion in Matrigel invasion and wound healing assay at a concentration of 100 nM, demonstrating a better effect than AMD3100. Western Blot experiments further showed that Hit14 blocked the CXCR4/CXCL12-mediated phosphorylation of Akt. Meanwhile, cellular thermal displacement assay analysis showed that CXCR4 protein bound to Hit14 had high thermal stability. Finally, through in vivo experiments, we found that Hit14 inhibited mouse ear inflammation and reduced ear swelling and damage. Therefore, Hit14 is a promising drug for the further development of CXCR4 inhibitors for inflammation treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Dihydropyrimidines are widely recognized for their diverse biological properties and are often synthesized by the Biginelli reactions. In this backdrop, a novel series of Biginelli dihydropyrimidines were designed, synthesized, purified, and analyzed by FT-IR, 1 H NMR, 13 C NMR, and mass spectrometry. Anticancer activity against MCF-7 breast cancer cells was evaluated as part of their cytotoxicity in comparison with the normal Vero cells. The cytotoxicity of dihydropyrimidines ranges from moderate to significant. Among the 38 dihydropyrimidines screened, compounds 16, 21, and 39 exhibited significant cytotoxicity. These 3 compounds were subjected to flow cytometry studies and EGFR wt Kinase inhibition assay using lapatinib as a standard. The study included evaluation for the inhibition of EGFR and HER2 expression at five different concentrations. At a concentration of 1000 nM compound 21 showed 98.51 % and 96.79 % inhibition of EGFR and HER2 expression. Moreover, compounds 16, 21 and 39 significantly inhibited EGFR wt activity with IC 50  = 69.83, 37.21 and 76.79 nM, respectively. In addition, 3D-QSAR experiments were conducted to elucidate Structure activity relationships in a 3D grid space by comparing the experimental and predicted cytotoxic activities. Molecular docking studies were performed to validate the results by in silico method. All together, we developed a new series of Biginelli dihydropyrimidines as dual EGFR/HER2 inhibitors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

A new series of thiazolidine-2,4-dione tethered 1,2,3-triazole derivatives were designed, synthesized and screened for their α-amylase inhibitory potential employing in vitro and in silico approaches. The target compounds were synthesized with the help of Cu (I) catalyzed [3 + 2] cycloaddition of terminal alkyne with numerous azides, followed by unambiguously characterizing the structure by employing various spectroscopic approaches. The synthesized derivatives were assessed for their in vitro α-amylase inhibition and it was found that thiazolidine-2,4-dione derivatives 6e, 6j, 6o, 6u and 6x exhibited comparable inhibition with the standard drug acarbose. The compound 6e with a 7-chloroquinolinyl substituent on the triazole ring exhibited significant inhibition potential with IC 50 value of 0.040 μmol mL -1 whereas compound 6c (IC 50  = 0.099 μmol mL -1 ) and 6h (IC 50  = 0.098 μmol mL -1 ) were poor inhibitors. QSAR studies revealed the positively correlating descriptors that aid in the design of novel compounds. Molecular docking was performed to investigate the binding interactions with the active site of the biological receptor and the stability of the complex over a period of 100 ns was examined using molecular dynamics studies. The physiochemical properties and drug-likeliness behavior of the potent derivatives were investigated by carrying out the ADMET studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Recently, seven dihalohydroxybenzonitriles (diHHBNs) have been determined as concerning nitrogenous aromatic disinfection byproducts (DBPs) in drinking water. Herein, eight new monohalohydroxybenzonitriles (monoHHBNs), including 3-chloro-2-hydroxybenzonitrile, 5-chloro-2-hydroxybenzonitrile, 3-chloro-4-hydroxybenzonitrile, 3-bromo-2-hydroxybenzonitrile, 5-bromo-2-hydroxybenzonitrile, 3-bromo-4-hydroxybenzonitrile, 5-iodo-2-hydroxybenzonitrile, and 3-iodo-4-hydroxybenzonitrile, were detected and identified in drinking water for the first time. Thereafter, the relative concentration-cytotoxicity contribution of each HHBN was calculated based on the acquired occurrence level and cytotoxicity data in this study, the genome-scale cytotoxicity mechanism was explored, and a quantitative structure-activity relationship (QSAR) model was developed. Results indicated that new monoHHBNs were present in drinking water at concentrations of 0.04-1.83 ng/L and exhibited higher cytotoxicity than some other monohalogenated aromatic DBPs. Notably, monoHHBNs showed concentration - cytotoxicity contribution comparable to diHHBNs, which have been previously identified as potential toxicity drivers in drinking water. Transcriptomic analysis revealed immunotoxicity and genotoxicity as dominant cytotoxicity mechanisms for HHBNs in Chinese hamster ovary (CHO-K1) cells, with potential carcinogenic effects. The QSAR model suggested oxidative stress and cellular uptake efficiency as important factors for their cytotoxicity, highlighting the importance of potential iodinated HHBNs in drinking water, such as 3,5-diiodo-2-hydroxybenzonitrile, for future studies. These findings are meaningful for better understanding the health risk and toxicological significance of HHBNs in drinking water.

Background: Staphylococcus aureus is a widely distributed and highly pathogenic zoonotic bacterium. Sortase A represents a crucial target for the research and development of novel antibacterial drugs., Objective: This study aims to establish quantitative structure-activity relationship models based on the chemical structures of a class of benzofuranene cyanide derivatives. The models will be used to screen new antibacterial agents and predict the properties of these molecules., Method: The compounds were randomly divided into a training set and a test set. A large number of descriptors were calculated using the software, and then the appropriate descriptors were selected to build the models through the heuristic method and the gene expression programming algorithm., Results: In the heuristic method, the determination coefficient, determination coefficient of cross-validation, F-test, and mean squared error values were 0.530, 0.395, 9.006, and 0.047, respectively. In the gene expression programming algorithm, the determination coefficient and the mean squared error values in the training set were 0.937 and 0.008, respectively, while in the test set, they were 0.849 and 0.035. The results showed that the minimum bond order of a C atom and the relative number of benzene rings had a significant positive contribution to the activity of compounds., Conclusion: In this study, two quantitative structure-activity relationship models were successfully established to predict the inhibitory activity of a series of compounds targeting Staphylococcus aureus Sortase A, providing insights for further development of novel anti-Staphylococcus aureus drugs., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

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.

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 IC 50 values. Remarkably, one of the identified leads possesses a novel chemotype compared to known ALK inhibitors.

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.

We report a new scoring method for rating the performance of ligands on same protein, using their extensive dynamic flexibility properties, binding with protein and impact on receptor protein. Based on molecular dynamics (MD), this method is more accurate than single-point energy calculations. This method identified an ideal FDA-approved drug as β-tubulin microtubule inhibitor with improved attributes compared to commercial microtubule disassembly inhibitor, Paclitaxel (PTX). We started with virtual screening (VS) of FDA-approved drugs inside PTX's binding pocket (A) of human β-tubulin protein. Screened ligands (>80% score) were evaluated for non-permeation through blood-brain barrier (BBB) as targets were body cancers, gastrointestinal absorption, Lipinski, non-efflux from central nervous system (CNS) by p-glycoprotein (Pgp), and ADMET analysis. This identified FDA-approved Naloxegol drug with superior attributes compared to PTX. Pocket (A) specific docking of chain length variable derivatives of Naloxegol gave docked poses that underwent MD run to give a range of properties and their descriptors (RMSD, RMSF, RoG, H-bonds, hydrophobic interaction and SASA). QSPR validated that MD properties dependent upon [-CH 2 -CH 2 -O-] n=0-7 chain length of Naloxegol. MD data underwent normalization, PCA analysis and scoring against PTX. One Naloxegol derivative scored higher than PTX as a potential microtubule disassembly inhibitor.

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.

The widespread prevalence of polychlorinated biphenyls (PCBs) in the environment has raised major concerns due to the associated risks to human health, wildlife, and ecological systems. Here, we investigated the degradation kinetics, Bayesian network (BN), quantitative structure-activity relationship-density functional theory (QSAR-DFT), artificial neural network (ANN), molecular docking (MD), and molecular dynamics stimulation (MS) of PCB biodegradation, i.e., PCB-10, PCB-28, PCB-52, PCB-138, PCB-153, and PCB-180 in the soil system using fungi isolated from the transformer oil-contaminated sites. Results revealed that the efficacy of PCB biodegradation best fits the first-order kinetics (R 2  ≥ 0.93). The consortium treatment (29.44-74.49%) exhibited more efficient degradation of PCBs than those of Aspergillus tamarii sp. MN69 (27.09-71.25%), Corynespora cassiicola sp. MN69 (23.76-57.37%), and Corynespora cassiicola sp. MN70 (23.09-54.98%). 3'-Methoxy-2, 4, 4'-trichloro-biphenyl as an intermediate derivative was detected in the fungal consortium treatment. The BN analysis predicted that the biodegradation efficiency of PCBs ranged from 11.6 to 72.9%. The ANN approach showed the importance of chemical descriptors in decreasing order, i.e., LUMO > MW > IP > polarity no. > no. of chlorine > Wiener index > Zagreb index > HOMU > Pogliani index > APE in PCB removal. Furthermore, the QSAR-DFT model between the chemical descriptors and rate constant (log K) exhibited a high fit and good robustness of R 2  = 99.12% in predicting ability. The MD and MS analyses showed the lowest binding energy through normal mode analysis (NMA), implying stability in the interactions of the docked complexes. These findings provide crucial insights for devising strategies focused on natural attenuation, holding substantial potential for mitigating PCB contamination within the environment., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Nitroaromatic compounds (NACs) stand out as pervasive organic pollutants, prompting an imperative need to investigate their hazardous effects. Computational chemistry methods play a crucial role in this exploration, offering a safer and more time-efficient approach, mandated by various legislations. In this study, our focus lay on the development of transparent, interpretable, reproducible, and publicly available methodologies aimed at deriving quantitative structure-activity relationship models and testing them by modelling the mutagenicity of NACs against the Salmonella typhimurium TA100 strain. Descriptors were selected from Mordred and RDKit molecular descriptors, along with several quantum chemistry descriptors. For that purpose, the genetic algorithm (GA), as the most widely used method in the literature, and three alternative algorithms (Boruta, Featurewiz, and ForwardSelector) combined with the forward stepwise selection technique were used. The construction of models utilized the multiple linear regression method, with subsequent scrutiny of fitting and predictive performance, reliability, and robustness through various statistical validation criteria. The models were ranked by the Multi-Criteria Decision Making procedure. Findings have revealed that the proposed methodology for descriptor selection outperforms GA, with Featurewiz showing a slight advantage over Boruta and ForwardSelector. These constructed models can serve as valuable tools for the quick and reliable prediction of NACs mutagenicity., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The bioconcentration factor (BCF) is an important parameter that gives information regarding the ability of a contaminant to be taken up by organisms from the water. Per- and polyfluoroalkyl substances (PFAS) are widespread in the environment, causing concern regarding their impact on human health. Due to the lack of available bioaccumulation data for most compounds in the PFAS group, we developed a quantitative structure-property relationship (QSPR) model to predict the log BCF for fish (taxonomic class Teleostei), based on experimental data available for the most studied 33 representatives of this group of compounds. Furthermore, we implemented the developed model to predict log BCF for an external dataset of 2209 PFAS. Consequently, 1045 PFAS were found not to be bioaccumulative, 208 were classified as bioaccumulative, and 956 were predicted to be very bioaccumulative. Finally, we obtained the high correlation (R 2  = 0.844) between the log BCFs obtained in laboratory and field studies for 13 PFAS. In silico analyses indicate that PFAS bioconcentration depends on the size (chain length - number of CF 2 groups in alkyl tail/chain) of a molecule, as well as on the atomic distribution properties. In general, long-chain PFAS - above 8 and 6 carbon atoms for perfluorinated carboxylic acids (PFCAs)and perfluorinated sulfonic acids (PFSAs), respectively - tend to bioconcentrate more compared to the short-chain ones. In conclusion, predicting BCF on fish is possible for a wide range of fluorinated compounds, which can be further used for estimating PFAS behavior in the environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Liquid crystal monomers (LCMs) are a new class of emerging pollutants with high octanol-water partition coefficients; however, their transformation behavior and associated risk to environments with high organic matter content has rarely been reported. In this study, we investigated the photodegradation kinetics, mechanism, and toxicity variation of 23 LCMs on leaf wax models (e.g., organic solvents methanol and n-hexane). The order of the photolysis rates of these LCMs were biphenylethyne LCMs > phenylbenzoate LCMs > diphenyl/terphenyl LCMs under simulated sunlight, while the phenylcyclohexane LCMs were resistant to photodegradation. The phenylbenzoate and biphenylethyne LCMs mainly undergo direct photolysis, while the diphenyl/terphenyl LCMs mainly undergo self-sensitized photolysis. The main photolysis pathways are the cleavage of ester bonds for phenylbenzoate LCMs, the addition, oxidation and cleavage of alkynyl groups for biphenylethyne LCMs, and the cleavage/oxidation of chains attached to phenyls and the benzene ring opening for diphenyl/terphenyls LCMs. Most photolysis products remained toxic to aquatic organisms to some degree. Additionally, two quantitative structure-activity relationship models for predicting k obs of LCMs in methanol and n-hexane were developed, and employed to predict k obs of 93 LCMs to fill the k obs data gap in systems mimicking leaf surfaces. These results can be helpful for evaluating the fate and risk of LCMs in environments with high content of organic phase., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Computer-aided drug discovery plays a vital role in developing novel medications for various diseases. The COVID-19 pandemic has heightened the need for innovative approaches to design lead compounds with the potential to become effective drugs. Specifically, designing promising inhibitors of the SARS-CoV-2 main protease (Mpro) is crucial, as it plays a key role in viral replication. Phytochemicals, primarily flavonoids and flavonols from medicinal plants, were screened. Fifty small molecules were selected for molecular docking analysis against SARS-CoV-2 Mpro (PDB ID: 6LU7). Binding energies and interactions were analyzed and compared to those of the anti-SARS-CoV-2 inhibitor Nirmatrelvir. Using these 50 structures as a training set, a QSAR model was built employing simple, reversible topological descriptors. An inverse-QSAR analysis was then performed on 2⁹ = 512 hydroxyl combinations at nine possible positions on the flavone and flavonol scaffold. The model predicted three novel, promising compounds exhibiting the most favorable binding energies (-8.5 kcal/mol) among the 512 possible hydroxyl combinations: 3,6,7,2',4'-pentahydroxyflavone (PF9), 6,7,2',4'-tetrahydroxyflavone (PF11), and 3,6,7,4'-tetrahydroxyflavone (PF15). Molecular dynamics (MD) simulations demonstrated the stability of the PF9/Mpro complex over 300 ns of simulation. These predicted structures, reported here for the first time, warrant synthesis and further evaluation of their biological activity through in vitro and in vivo studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Non-alcoholic fatty liver disease (NAFLD) is a growing global health concern due to its potential to progress into severe liver diseases. Targeting the bile acid receptor FXR has emerged as a promising strategy for managing NAFLD. Building upon our previous research on FXR partial agonism, the present study investigates a series of 1,3,4-trisubstituted-pyrazol amide derivatives as FXR antagonists, aiming to delineate the structural features for antagonism. By means of 2D-QSAR (quantitative structure-activity relationships) modelling techniques, we elucidated the key structural elements responsible for the antagonistic properties of these derivatives. We then employed QPhAR, an open-access software, to identify key molecular features within the compounds that enhance their antagonistic activity. Additionally, 3D-QSAR modelling allowed us to analyse the steric and electrostatic fields of aligned 3D structures, further refining our understanding of structure-activity relationships. Subsequent molecular dynamics simulations provided insights into the binding mode interactions between the compounds and FXR, with varying potencies, confirming and complementing the findings from 2D-QSAR, pharmacophore, and 3D-QSAR modelling. Particularly, our study highlighted the significance of hydrophobic interactions in conferring potent antagonism by the 1,3,4-trisubstituted-pyrazol amide derivatives against FXR. Overall, this work underscores the potential of 1,3,4-trisubstituted-pyrazol amides as FXR antagonists for NAFLD treatment. Notably, our reliance on open-access software fosters reproducibility and broadens the accessibility of our findings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

As a pivotal node in modulating various cell behaviors, Rho-associated kinase-1 (ROCK1) has attracted significant attention as a promising therapeutic target in a variety of diseases. Benzamide has been widely reported as a ROCK1 inhibitors in recent years. To better understand its pharmacological properties and to explore its potential inhibitors, a series of ROCK1 inhibitors derived from N-methyl-4-(4-pyrazolidinyl) benzamides (MPBs) were investigated by using three-dimensional quantitative structure-activity relationship (3D-QSAR) models, pharmacophore models, molecular docking, and molecular dynamics (MD) simulation. The comparative Molecular Field Analysis (CoMFA) model ( q 2 = 0.616, R 2 = 0.972, ONC = 4, and r 2 pred = 0.983) and the best Comparative Molecular Similarity Indices Analysis (CoMSIA) model ( q 2 = 0.740, R 2 = 0.982, ONC = 6, and r 2 pred = 0.824) exhibited reliable predictability with satisfactory validation parameters. In the subsequent virtual screening, VS03 and VS05 were identified to have superior predicted activities and higher docking scores, meanwhile they demonstrated to be reasonably stable in the binding pocket through MD simulations. These results provide a significant theoretical direction for the rational design and development of novel ROCK1 inhibitors.Communicated by Ramaswamy H. Sarma.

Models of toxicity to tadpoles have been developed as single parameters based on special descriptors which are sums of correlation weights, molecular features, and experimental conditions. This information is presented by quasi-SMILES. Fragments of local symmetry (FLS) are involved in the development of the model and the use of FLS correlation weights improves their predictive potential. In addition, the index of ideality correlation ( IIC ) and correlation intensity index ( CII ) are compared. These two potential predictive criteria were tested in models built through Monte Carlo optimization. The CII was more effective than IIC for the models considered here.

A completely green protocol was developed for the synthesis of a series of arylaminonaphthol derivatives in the presence of N-ethylethanolamine (NEEA) as a catalyst under ultrasonic irradiation and solventless conditions. The major assets of this methodology were the use of non-toxic organic medium, available catalyst, mild reaction condition, and good to excellent yield of desired products. All of the synthesized products were screened for their in vitro antioxidant activity using DPPH, ABTS, and Ferric-phenanthroline assays and it was found that most of them are potent antioxidant agents. Also, their butyrylcholinesterase inhibitory activity has been investigated in vitro. All tested compounds exhibited potential inhibitory activity toward BuChE when compared to standard reference drug galantamine, however, compounds 4r, 4u, 4 g and 4x gave higher butyrylcholinesterase inhibitory with IC 50 values of 14.78 ± 0.65 µM, 16.18 ± 0.50 µM, 20.00 ± 0.50 µM, and 20.28 ± 0.08 µM respectively. On the other hand, we employed density functional theory (DFT), calculations to analyze molecular geometry and global reactivity descriptors, and MESP analysis to predict electrophilic and nucleophilic attacks. A quantitative structure-activity relationship (QSAR) investigation was conducted on the antioxidant and butyrylcholinesterase properties of 25 arylaminonaphthol derivatives, resulting in robust and satisfactory models. To evaluate their anti-Alzheimer's activity, compounds 4 g, 4q, 4r, 4u, and 4x underwent docking simulations at the active site of the acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), revealing why these compounds displayed superior activity, consistent with the biological findings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)