Quantitative Structure-activity Relationship Models Research Articles

How safe are wild-caught salmons exposed to various industrial chemicals? First ever in silico models for salmon toxicity data gaps filling

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.

Evaluating ionic liquid toxicity with machine learning and structural similarity methods

Ionic liquids (ILs) have garnered significant interest owing to their distinct physicochemical traits. Nonetheless, their extensive application is curtailed by ecotoxicity concerns. This study aimed to develop a quantitative structure-activity relationship (QSAR) model for predicting the toxicity of ILs in biological cells. Toxicity data of ILs on leukemia rat cell line IPC-81, Escherichia coli (E. coli), and acetylcholinesterase (AChE) were collected from open-source databases, and two integrated models, random forest (RF) and gradient boosted decision tree (GBDT), were used to train the data. The molecular structures of the ILs were represented by three different methods, namely molecular descriptor (MD), molecular fingerprint (MF), and molecular identifier (MI), respectively. The Tanimoto similarity coefficients indicate that MD has a stronger ability to recognize structural similarity. Statistical metrics of model performance showed that the two models (MD-RF and MD-GBDT) with MD as an input feature performed better in the three datasets. The application of the SHapley Additive exPlanations (SHAP) method explains the importance of different features. Specifically, reducing the carbon chain length and the number of fluorine atoms in the structure of ILs can effectively reduce their toxic effects on biological cells. This study employs machine learning to grasp better how the structure of ILs relates to inhibiting biotoxicity, offering insights for crafting safer, eco-friendly IL designs.

Revolutionizing Drug Discovery: A Comprehensive Review of Computer-Aided Drug Design Approaches

Abstract: Computer-Aided Drug Design (CADD) has significantly advanced the drug discovery process, offering tools to enhance efficiency and reduce costs. This review explores essential CADD methodologies, including molecular docking, virtual screening, ADMET profiling, homology modeling, and Quantitative Structure-Activity Relationship (QSAR) models. Molecular docking predicts interactions between drugs and targets, while virtual screening evaluates large compound libraries to identify promising candidates. ADMET profiling assesses pharmacokinetic and toxicological properties early in development. Homology modeling constructs three-dimensional protein models to aid target identification, and QSAR models predict biological activities based on chemical structures. These integrated approaches streamline drug development, providing a robust framework for modern pharmaceutical research.

In silico insights into the design of novel NR2B-selective NMDA receptor antagonists: QSAR modeling, ADME-toxicity predictions, molecular docking, and molecular dynamics investigations

Based on a structural family of thirty-two NR2B-selective N-Methyl-D-Aspartate receptor (NMDAR) antagonists, two phenylpiperazine derivatives labeled C37 and C39 were conceived thanks to molecular modeling techniques, as novel NMDAR inhibitors exhibiting the highest analgesic activities (of pIC50 order) against neuropathic pain, with excellent ADME-toxicity profiles, and good levels of molecular stability towards the targeted protein of NMDA receptor. Initially, the quantitative structure-activity relationships (QSARs) models were developed using multiple linear regression (MLR), partial least square regression (PLSR), multiple non-linear regression (MNLR), and artificial neural network (ANN) techniques, revealing that analgesic activity was strongly correlated with dipole moment, octanol/water partition coefficient, Oxygen mass percentage, electronegativity, and energy of the lowest unoccupied molecular orbital, whose the correlation coefficients of generated models were: 0.860, 0.758, 0.885 and 0.977, respectively. The predictive capacity of each model was evaluated by an external validation with correlation coefficients of 0.703, 0.851, 0.778, and 0.981 respectively, followed by a cross-validation technique with the leave-one-out procedure (CVLOO) with Q2cv of 0.785, more than Y-randomization test, and applicability domain (AD), in addition to Fisher’s and Student’s statistical tests. Thereafter, ten novel molecules were designed based on MLR QSAR model, then predicted with their ADME-Toxicity profiles and subsequently examined for their similarity to the drug candidates. Finally, two of the most active compounds (C37 and C39) were chosen for molecular docking and molecular dynamics (MD) investigations during 100 ns of MD simulation time in complex with the targeted protein of NMDA receptor (5EWJ.pdb).

Discovery of Novel Acethydrazide-Containing Flavonol Derivatives as Potential Antifungal Agents.

In this study, a series of novel hydrazide-containing flavonol derivatives was designed, synthesized, and evaluated for antifungal activity. In the in vitro antifungal assay, most of the target compounds exhibited potent antifungal activity against seven tested phytopathogenic fungi. In particular, compound C32 showed the best antifungal activity against Rhizoctonia solani (EC50 = 0.170 μg/mL), outperforming carbendazim (EC50 = 0.360 μg/mL) and boscalid (EC50 = 1.36 μg/mL). Compound C24 exhibited excellent antifungal activity against Valsa mali, Botrytis cinerea, and Alternaria alternata with EC50 values of 0.590, 0.870, and 1.71 μg/mL, respectively. The in vivo experiments revealed that compounds C32 and C24 were potential novel agricultural antifungals. 3D quantitative structure-activity relationship (3D-QSAR) models were used to analyze the structure-activity relationships of these compounds. The analysis results indicated that introducing appropriate electronegative groups at position 4 of a benzene ring could effectively improve the anti-R. solani activity. In the antifungal mechanism study, scanning electron microscopy and transmission electron microscopy analyses revealed that C32 disrupted the normal growth of hyphae by affecting the structural integrity of the cell membrane and cellular respiration. Furthermore, compound C32 exhibited potent succinate dehydrogenase (SDH) inhibitory activity (IC50 = 8.42 μM), surpassing that of the SDH fungicide boscalid (IC50 = 15.6 μM). The molecular dynamics simulations and docking experiments suggested that compound C32 can occupy the active site and form strong interactions with the key residues of SDH. Our findings have great potential for aiding future research on plant disease control in agriculture.

In vitro antioxidant activity, QSAR, in silico toxicity prediction, molecular docking studies, and molecular dynamics of a series of N-ferrocenylmethylanilines as potent antioxidant agents

The superoxide anion radical scavenging activities of a series of twenty-eight ferrocenylmethylaniline derivatives were measured in an in vitro study followed by in silico studies which included quantitative structure-activity relationship (QSAR), toxicity prediction, molecular docking studies, and molecular dynamics simulations. Multilinear regression analysis was employed to establish a quantitative correlation between the scavenging activity of superoxide anion radicals and various structural properties. The QSAR model obtained underwent validation, demonstrating statistical significance with a squared fitting correlation coefficient (R2) of 0.934, an adjusted squared correlation coefficient (R2 adj) value of 0.849, and a cross-validation coefficient (Q2) value of 0.873. Additionally, the calculated Fisher ratio (F) value of 36.525 indicates the model's strong predictability. Toxicity perdition studies demonstrated that among the five most potent compounds, FMA27 and FMA12 were predicted to be non-toxic. The molecular Docking study revealed that compound FMA23 exhibited the least activity against the glutathione reductase enzyme, with the highest inhibitory concentration recorded at 4.16 × 10–6 M and the lowest docking scores observed at −7.34 kcal.mol-1. Interactions between the compound and the amino acid residues of glutathione reductase were primarily hydrophobic and π-cation in nature. Analysis of root mean square deviation (RMSD), radius of gyration (Rg), and root mean square fluctuation (RMSF), from molecular dynamics study, revealed stable fluctuations in protein backbone conformations, consistent compactness of the complex, and minimal fluctuations in most protein residues, respectively.

Data set of fraction unbound values in the in vitro incubations for metabolic studies for better prediction of human clearance.

In vitro-in vivo extrapolation is a commonly applied technique for liver clearance prediction. Various in vitro models are available such as hepatocytes, human liver microsomes, or recombinant cytochromes P450. According to the free drug theory, only the unbound fraction (fu) of a chemical can undergo metabolic changes. Therefore, to ensure the reliability of predictions, both specific and nonspecific binding in the model should be accounted. However, the fraction unbound in the experiment is often not reported. The study aimed to provide a detailed repository of the literature data on the compound's fu value in various in vitro systems used for drug metabolism evaluation and corresponding human plasma binding levels. Data on the free fraction in plasma and different in vitro models were supplemented with the following information: the experimental method used for the assessment of the degree of drug binding, protein or cell concentration in the incubation, and other experimental conditions, if different from the standard ones, species, reference to the source publication, and the author's name and date of publication. In total, we collected 129 literature studies on 1425 different compounds. The provided data set can be used as a reference for scientists involved in pharmacokinetic/physiologically based pharmacokinetic modelling as well as researchers interested in Quantitative Structure-Activity Relationship models for the prediction of fraction unbound based on compound structure. Database URL: https://data.mendeley.com/datasets/3bs5526htd/1.

Innovative Multistage ML-QSAR Models for Malaria: From Data to Discovery.

Malaria presents a significant challenge to global public health, with around 247 million cases estimated to occur annually worldwide. The growing resistance of Plasmodium parasites to existing therapies underscores the urgent need for new and innovative antimalarial drugs. This study leveraged artificial intelligence (AI) to tackle this complex challenge. We developed multistage Machine Learning Quantitative Structure-Activity Relationship (ML-QSAR) models to effectively analyze large datasets and predict the efficacy of chemical compounds against multiple life cycle stages of Plasmodium parasites. We then selected 16 compounds for experimental evaluation, six of which showed at least dual-stage inhibitory activity and one inhibited all life cycle stages tested. Moreover, explainable AI (XAI) analysis provided insights into critical molecular features influencing model predictions, thereby enhancing our understanding of compound interactions. This study not only empowers the development of advanced predictive AI models but also accelerates the identification and optimization of potential antiplasmodial compounds.

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery.

Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to understand the structure-activity relationships that exist between biomolecules and druggable targets. More recently, these methods have also seen application for phenotypic screening data for neglected diseases such as tuberculosis and malaria. Herein, we apply machine learning to build quantum Quantitative Structure Activity Relationship models from antimalarial data sets. There is a continual need for new antimalarials to address drug resistance, and the readily available in vitro data sets could be utilized with newer machine learning approaches as these develop. Furthermore, quantum machine learning is a relatively new method that uses a quantum computer to perform the calculations. First, we present a classical-quantum hybrid computational approach by building a Latent Bernoulli Autoencoder machine learning model for compressing bit-vector descriptors to a size that can be adapted to quantum computers for classification tasks with limited loss of embedded information. Second, we apply our method for feature map compression to quantum classification algorithms, including a completely novel machine learning algorithm with no analogy in classical computers: the Quantum Fourier Transform Classifier. We apply both these approaches to build quantum machine learning models for small-molecule antimalarials with quantum simulation software and then benchmark these quantum models against classical machine learning approaches. While there are many challenges currently facing the development of reliable quantum computers, our results demonstrate that there is potential for the use of this technology in the field of drug discovery.

QSAR, ADMET, molecular docking, and dynamics studies of 1,2,4-triazine-3(2H)-one derivatives as tubulin inhibitors for breast cancer therapy

Breast cancer remains a leading cause of cancer-related deaths among women globally, necessitating the development of more effective therapeutic agents with minimal side effects. This study explores novel 1,2,4-triazine-3(2H)-one derivatives as potential inhibitors of Tubulin, a pivotal protein in cancer cell division, highlighting a targeted approach in cancer therapy. Using an integrated computational approach, we combined quantitative structure–activity relationship (QSAR) modeling, ADMET profiling, molecular docking, and molecular dynamics simulations to evaluate and predict the efficacy and stability of these compounds. Our QSAR models, developed through rigorous statistical analysis, revealed that descriptors such as absolute electronegativity and water solubility significantly influence inhibitory activity, achieving a predictive accuracy (R2) of 0.849. Molecular docking studies identified compounds with high binding affinities, particularly Pred28, which exhibited the best docking score of − 9.6 kcal/mol. Molecular dynamics simulations conducted over 100 ns provided further insights into the stability of these interactions. Pred28 demonstrated notable stability, with the lowest root mean square deviation (RMSD) of 0.29 nm and root mean square fluctuation (RMSF) values indicative of a tightly bound conformation to Tubulin. The novelty of this work lies in its methodological rigor and the integration of multiple advanced computational techniques to pinpoint compounds with promising therapeutic potential. Our findings advance the current understanding of Tubulin inhibitors and open avenues for the synthesis and experimental validation of these compounds, aiming to offer new solutions for breast cancer treatment.

Random Forests Regression and Rescoring Strategy for Identifying Inhibitors of Ubiquitin Specific Protease-7

Ubiquitin-specific protease-7 (USP7) is a potential target for cancer therapy. In this work, the evaluation and design of USP7 inhibitors are conducted employing a comprehensive computational approach. Initially, a large database of compounds undergoes preliminary screening via similarity and pharmacophore analyses. Subsequently, a secondary selection process is executed based on both quantitative structure–activity relationship modeling and molecular docking. The final selection is refined through rescoring treatments via consensus scores and MM/GBSA binding free energy. A total of 138 experimental USP7 inhibitors were collected for the construction of random forest regression models using four different feature selection methods. 17 top experimentally active inhibitors were used as models in similarity searching, and eight representative USP7-ligand binding models were used in pharmacophore screening, performed over a database of 14 million compounds. The rescoring approach applied in this study offers an alternative and beneficial method for the ranking and selection of desired molecules. Based on the molecular scaffolds of the highly active inhibitors, some new derivatives have been designed and received high predicted scores for inhibition.

Elucidating bis-pyrimidines as new and efficient mushroom tyrosinase inhibitors: synthesis, SAR, kinetics and computational studies.

In this study, a series of novel bis-pyrimidine derivatives (1P-8P) were designed, synthesized, characterized, and investigated for their in vitro inhibitory activity against mushroom tyrosinase, an enzyme critical in melanin biosynthesis and implicated in various hyperpigmentation disorders. To the best of our knowledge, the bispyrimidine scaffold has been evaluated for the first time for its tyrosinase inhibitory activity. Their inhibitory activities were assessed, revealing inhibition with IC50 values in the micromolar range. Additionally, this series of compounds were found to inhibit tyrosinase activity in a mixed-type manner, with IC50 values ranging from 12.36 ± 1.24 to 86.67 ± 3.08 μM. To further elucidate the binding interactions, molecular docking simulations were performed, identifying key residues in the active site responsible for binding affinity. Furthermore, molecular dynamics (MD) simulations were conducted to assess the dynamic behavior, stability, and binding affinity of the most potent inhibitor, compound 6P. Quantitative Structure-Activity Relationship (QSAR) models were developed to correlate the structural features of the bis-pyrimidines with their inhibitory activity, providing insights into the structure-activity relationships (SAR) that govern their potency. The experimental and theoretical findings demonstrated excellent agreement. These findings pave the way for the development of novel bis-pyrimidine-based therapeutic agents for treating hyperpigmentation and related conditions.

In Silico and In Vitro Studies of Terpenes from the Fabaceae Family Using the Phenotypic Screening Model against the SARS-CoV-2 Virus.

In 2019, the emergence of the seventh known coronavirus to cause severe illness in humans triggered a global effort towards the development of new drugs and vaccines for the SARS-CoV-2 virus. These efforts are still ongoing in 2024, including the present work where we conducted a ligand-based virtual screening of terpenes with potential anti-SARS-CoV-2 activity. We constructed a Quantitative Structure-Activity Relationship (QSAR) model from compounds with known activity against SARS-CoV-2 with a model accuracy of 0.71. We utilized this model to predict the activity of a series of 217 terpenes isolated from the Fabaceae family. Four compounds, predominantly triterpenoids from the lupane series, were subjected to an in vitro phenotypic screening in Vero CCL-81 cells to assess their inhibitory activity against SARS-CoV-2. The compounds which showed high rates of SARS-CoV-2 inhibition along with substantial cell viability underwent molecular docking at the SARS-CoV-2 main protease, papain-like protease, spike protein and RNA-dependent RNA polymerase. Overall, virtual screening through our QSAR model successfully identified compounds with the highest probability of activity, as validated using the in vitro study. This confirms the potential of the identified triterpenoids as promising candidates for anti-SARS-CoV-2 therapeutics.

The role of physicochemical and topological parameters in drug design

Quantitative structure activity relationship (QSAR) is a widely used tool in rational drug design that establishes relationships between the physicochemical and topological descriptors of ligands and their biological activities. Obtained QSAR models help identify descriptors that play pivotal roles in the biological activity of ligands. This not only helps the prediction of new compounds with desirable biological activities but also helps with the design of new compounds with better activities and low toxicities. QSAR commonly uses lipophilicity (logP), hydrophobicity (logD), water solubility (logS), the acid–base dissociation constant (pKa), the dipole moment, the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), molecular weight (MW), molar volume (MV), molar refractivity (MR), and the kappa index as physicochemical parameters. Some commonly used topological indices in QSAR are the Wiener index, Platt index, Hosoya index, Zagreb indices, Balaban index, and E-state index. This review presents a brief description of the significance of the most extensively used physicochemical and topological parameters in drug design.

Photochemical transformation of liquid crystal monomers in simulated environmental media: Kinetics, mechanism, toxicity variation and QSAR modeling

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 kobs of LCMs in methanol and n-hexane were developed, and employed to predict kobs of 93 LCMs to fill the kobs 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.

QSAR Studies on a Series of Pyrazole Azabicyclo [3.2.1] Octane Sulfonamides N-acylethanolamine-hydrolyzing Acid Amidase Inhibitors

Background: Inflammation is a common and intractable disease for humans. Current antiinflammatory drugs have a lot of side effects, which cause irreversible damage to the body. Objective: We predict the activity of the N-acylethanolamine-hydrolyzing acid amidase (NAAA) inhibitor to find more effective compounds. Methods: We established a quantitative structure-activity relationship (QSAR) model by gene expression programming to predict the IC50 values of natural compounds. The NAAA inhibitor, as a cysteine enzyme, plays an important role in the therapy of pain, anti-inflammatory effects and application of other diseases. A total of 36 NAAA inhibitors were optimized by the heuristic method in the CODESSA program to build a linear model. The 27 compounds and 9 compounds were in train and test sets. On this basis, we selected three descriptors and used them to build nonlinear models in gene expression programming. Results: The best model in the gene expression programming method was found, the square of correlation coefficients of R2 and mean square error for the training set were 0.79 and 0.14, testing set was 0.78 and 0.20, respectively. Conclusion: From this method, the activity of molecules could be predicted, and the best method was found. Therefore, this model has a stronger predictive ability to develop NAAA inhibitors.

A QSAR-based application for the prediction of lethal blood concentration of new psychoactive substances

The rapid development and introduction of new psychoactive substances (NPS) into illegal markets present an enormous challenge for forensic toxicologists, as there is limited knowledge about their toxicity in humans. To strengthen forensic interpretation of NPS intoxication cases, we have developed a predictive model for estimating human lethal blood concentrations (LBC) of various NPS. This quantitative structure-activity relationship (QSAR) model focuses on opioids, designer benzodiazepines, synthetic cathinones, synthetic cannabinoids, and phenethylamines. Utilising linear regression and multilayer perceptron algorithms, the models was trained using data from the existing literature. A toxicological significance-based approach have been applied to refine the selection of training data. The model demonstrated satisfactory performance metrics through cross-validation (R ≈ 0.8, MAE ≈ 0.6) and comparison with experimental data (R ≈ 0.9). A Python-based web application have been developed to facilite the use of the created model in predicting LBC of NPS. Despite the model's reliability, limitations due to data availability, quality and the complexities of post-mortem toxicology mean that its predictions should be interpreted with caution.

Development of an effective QSAR-based hazard threshold prediction model for the ecological risk assessment of aromatic hydrocarbon compounds.

The insufficient hazard thresholds of specific individual aromatic hydrocarbon compounds (AHCs) with diverse structures limit their ecological risk assessment. Thus, herein, quantitative structure-activity relationship (QSAR) models for estimating the hazard threshold of AHCs were developed based on the hazardous concentration for 5% of species (HC5) determined using the optimal species sensitivity distribution models and on the molecular descriptors calculated via the PADEL software and ORCA software. Results revealed that the optimal QSAR model, which involved eight descriptors, namely, Zagreb, GATS2m, VR3_Dzs, AATSC2s, GATS2c, ATSC2i, ω, and Vm, displayed excellent performance, as reflected by an optimal goodness of fit (R2adj = 0.918), robustness (Q2LOO = 0.869), and external prediction ability (Q2F1 = 0.760, Q2F2 = 0.782, and Q2F3 = 0.774). The hazard thresholds estimated using the optimal QSAR model were approximately close to the published water quality criteria developed by different countries and regions. The quantitative structure-toxicity relationship demonstrated that the molecular descriptors associated with electrophilicity and topological and electrotopological properties were important factors that affected the risks of AHCs. A new and reliable approach to estimate the hazard threshold of ecological risk assessment for various aromatic hydrocarbon pollutants was provided in this study, which can be widely popularised to similar contaminants with diverse structures.

Synthetic Modification and Insecticidal Activity of 4-epi-cis-Dihydroagarofuran Derivatives.

To synthesize the fundamental framework of dihydroagarofuran, a novel strategy was devised for constructing the C-ring through a dearomatization reaction using 6-methoxy-1-tetralone as the initial substrate. Subsequently, the dihydroagarofuran skeleton was assembled via two consecutive Michael addition reactions. The conjugated diene and trans-dihydroagarofuran skeleton were modified. The insecticidal activities of 33 compounds against Mythimna separata were evaluated. Compounds 11-5 exhibited an LC50 value of 0.378 mg/mL. The activity exhibited a remarkable 29-fold increase compared to positive control Celangulin V, which was widely recognized as the most renowned natural dihydroagarofuran polyol ester insecticidal active compound. Docking experiments between synthetic compounds and target proteins revealed the shared binding sites with Celangulin V. Structure-activity relationship studies indicated that methyl groups at positions C4 and C10 significantly improved insecticidal activity, while ether groups with linear chains displayed enhanced activity; in particular, the allyl ether group demonstrated optimal efficacy. Furthermore, a three-dimensional quantitative structure-activity relationship model was established to investigate the correlation between the skeletal structure and activity. These research findings provide valuable insights for discovering and developing dihydroagarofuran-like compounds.

4D-QSAR, ADMET properties, and molecular dynamics simulations for designing N-substituted urea/thioureas as human glutaminyl cyclase inhibitors

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 (Q2 = 0.521, R2 = 0.933, R2prep = 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.