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This article presents the development of a quantitative structure‐activity relationship (QSAR) model for predicting the inhibitory activity of inducible nitric oxide synthase (iNOS) by specific compounds used in Alzheimer's disease treatment. iNOS is a vital enzyme involved in nitric oxide (NO) production, contributing to neuroinflammation and neuronal damage in Alzheimer's disease. The QSAR model was developed using a dataset of 90 compounds with known iNOS inhibition activity. Molecular descriptors representing the compounds' structural and physicochemical properties were calculated and employed as input variables. Five descriptors (MATS6p, Chi1_EA(dm), Mor17 s, NsssCH, and SHED_AL) were selected as optimal for developing the classification model. The Random Forest algorithm was chosen as the classifier, implemented using WEKA software. The model underwent rigorous internal and external validation to assess its performance. The resulting QSAR model exhibited outstanding predictive capabilities with an overall accuracy of 88.8 %, a high correlation coefficient, and minimal prediction errors. It effectively forecasts iNOS inhibition activity of the chosen compounds, offering valuable insights for potential Alzheimer's disease treatments. This model significantly contributes to drug discovery, providing a rapid and cost‐effective means of screening and prioritizing compounds with iNOS inhibitory potential. [ABSTRACT FROM AUTHOR]

Background: Primary aromatic amines (PAAs) present significant challenges in the prediction of mutagenicity using current standard quantitative structure activity relationship (QSAR) systems, which are knowledge-based and statistics-based, because of their low positive prediction values (PPVs). Previous studies have suggested that PAAs are metabolized into genotoxic nitrenium ions. Moreover, ddE, a relative-energy based index derived from quantum chemistry calculations that measures the stability nitrenium ions, has been correlated with mutagenicity. This study aims to further examine the ability of the ddE-based approach in improving QSAR mutagenicity predictions for PAAs and to develop a refined method to decrease false positive predictions. Results: Information on 1,177 PAAs was collected, of which 420 were from public databases and 757 were from in-house databases across 16 laboratories. The total dataset included 465 Ames test-positive and 712 test-negative chemicals. For internal PAAs, detailed Ames test data were scrutinized and final decisions were made using common evaluation criteria. In this study, ddE calculations were performed using a convenient and consistent protocol. An optimal ddE cutoff value of -5 kcal/mol, combined with a molecular weight ≤ 500 and ortho substitution groups yielded well-balanced prediction scores: sensitivity of 72.0%, specificity of 75.9%, PPV of 65.6%, negative predictive value of 80.9% and a balanced accuracy of 74.0%. The PPV of the ddE-based approach was greatly reduced by the presence of two ortho substituent groups of ethyl or larger, as because almost all of them were negative in the Ames test regardless of their ddE values, probably due to steric hindrance affecting interactions between the PAA and metabolic enzymes. The great majority of the PAAs whose molecular weights were greater than 500 were also negative in Ames test, despite ddE predictions indicating positive mutagenicity. Conclusions: This study proposes a refined approach to enhance the accuracy of QSAR mutagenicity predictions for PAAs by minimizing false positives. This integrative approach incorporating molecular weight, ortho substitution patterns, and ddE values, substantially can provide a more reliable basis for evaluating the genotoxic potential of PAAs. [ABSTRACT FROM AUTHOR]

A total of 16 organic sunscreens and over 160 products of their degradation in biotic and abiotic conditions were investigated in the context of their safety during pregnancy. Drug-likeness and the ability of the studied compounds to be absorbed from the gastrointestinal tract and cross the human placenta were predicted in silico using the SwissADME software (for drug-likeness and oral absorption) and multiple linear regression and "ARKA" models (for placenta permeability expressed as fetus-to-mother blood concentration in the state of equilibrium), with the latter outperforming the MLR models. It was established that most of the studied compounds can be absorbed from the gastrointestinal tract. The drug-likeness of the studied compounds (expressed as a binary descriptor, Lipinski) is closely related to their ability to cross the placenta (most likely by a passive diffusion mechanism). The organic sunscreens and their degradation products are likely to cross the placenta, except for very bulky and highly lipophilic 1,3,5-triazine derivatives; an avobenzone degradation product, 1,2-bis(4-tert-butylphenyl)ethane-1,2-dione; diethylamino hydroxybenzoyl hexyl benzoate; and dimerization products of sunscreens from the 4-methoxycinnamate group. [ABSTRACT FROM AUTHOR]

Muscular dystrophies encompass a heterogeneous group of rare neuromuscular diseases characterized by progressive muscle degeneration and weakness. Among these, Duchenne muscular dystrophy (DMD) stands out as one of the most severe forms. The present study employs an integrative approach combining network pharmacology, quantitative structure-activity relationship (QSAR) modeling, molecular dynamics (MD) simulations, and free energy calculations to identify potential therapeutic targets and natural compounds for DMD. Upon analyzing the GSE38417 dataset, it was found that individuals with DMD exhibited 290 upregulated differentially expressed genes (DEGs) compared to healthy controls. By utilizing gene ontology (GO) and protein-protein interaction (PPI) network analysis, this study provides insights into the functional roles of the identified DEGs, identifying ten hub genes that play a critical role in the pathology of DMD. These key genes include DMD, TTN, PLEC, DTNA, PKP2, SLC24A, FBXO32, SNTA1, SMAD3, and NOS1. Furthermore, through the use of ligand-based pharmacophore modeling and virtual screening, three natural compounds were identified as potential inhibitors. Among these, compounds 3874518 and 12314417 have demonstrated significant promise as an inhibitor of the SMAD3 protein, a crucial factor in the fibrotic and inflammatory mechanisms associated with DMD. The therapeutic potential of the compounds was further supported by molecular dynamics simulation and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analysis. These findings suggest that the compounds are viable candidates for experimental validation against DMD. [ABSTRACT FROM AUTHOR]

Nanoporous carbon-based materials are being investigated as potential electrical double-layer-based ultracapacitors. The electrochemical properties of nanoporous carbon materials strongly depend upon their structure. Carbide-derived carbon (CDC) materials are considered promising carbon-based energy storage because of their diverse structural variations with high content of micropores, surface area, and pore size distribution. Considering all these facts, therefore, for the first time, a multi-target quantitative nanostructure property relationship (Mt-QnSPR) approach was used on a set of carbide-derived carbon materials to predict the electrical double-layer capacitance in non-aqueous electrolyte. Here, the volumetric capacitance of these carbon electrodes was predicted in terms of cathodic capacitance (CvNEG) and anodic capacitance (CvPOS) values using a single multi-target ANN model. Two models, one with experimentally derived structure descriptors and the other with descriptors derived from the Monte Carlo method, were developed and tested using an external test set. The prediction abilities of both models were compared using various statistical parameters. The results showed that both models were quite robust and reliable (R2test = 0.962, ΔRm2 = 0.038. CCC = 0.977, IIC = 0.872, RMSE = 2.610 for 5–5-2 ANN model; R2test = 0.858, ΔRm2 = 0.044, CCC = 0.925, IIC = 0.708, RMSE = 4.77 for 2–2-2 ANN model). But 5–5–2 model outperforms 2–2–2 model in terms of training and prediction ability. The applicability domain of these models was also verified using the leverage approach. [ABSTRACT FROM AUTHOR]

AbstractCasein kinase 2 (CK2) is an ubiquitous, essential, and highly pleiotropic protein kinase whose abnormally high constitutive activity is suspected to underlie its pathogenic potential in neoplasia and other diseases. Recently, there has been a notable increase in interest in the use of casein kinase 2 (CK2) inhibitors to improve the treatment of a specific form of cancer while minimizing the risk of undesirable side effects. Recently, using different virtual screening approaches, we have identified several novel CK2 inhibitors. In particular, we have discovered that the coumarin moiety can be considered an attractive CK2 inhibitor scaffold. In the present work, quantitative structure-activity relationship (QSAR) analysis has been employed to envisage the inhibitory effects of 32 coumarin derivatives on the CK2 protein. The most efficient model is found by using multiple linear regression (MLR). Its capability is considered by the external and internal validation values found (R2 = 0.884, Q2cv = 0.822, R2pred = 0.821, and R2p = 0.811), which aligned well with Tropsha and Golbraikh’s approach. The highest docking score founded for the newly designed coumarins is −7.50 kcal mol-1, which indicates that candidates can bind to the CK2 receptor with greater affinity. Based on the results of the ADMET properties and drug similarity analyses, a DFT investigation was conducted to confirm the stability of the newly explored compounds. It appears that the most stable complexes are those of compound with the highest binding affinity with a lower risk of toxicité. [ABSTRACT FROM AUTHOR]

Pseudomonas aeruginosa is a versatile opportunistic bacterium that presents a considerable risk in medical environments because of its strong adaptability and resistance to multiple medications. Targeting the LasR quorum sensing system, which plays a crucial role in controlling virulence factors and biofilm formation, is a key intervention point. In this study, in silico molecular docking, machine learning‐based Quantitative Structure‐Activity Relationship (QSAR) techniques along with molecular dynamics simulation were employed to screen phytochemical compounds for their ability to inhibit the LasR QS system, a key regulator of virulence in Pseudomonas aeruginosa. This study screened 1652 phytochemicals using the ML‐based QSAR model to identify 52 phytochemicals that had better activity than the control (N‐{[3,5‐dibromo‐2‐(methoxymethoxy)phenyl]methyl}‐2‐nitrobenzamide). The in silico molecular docking approach that targeted LasR identified compounds 5281647, 57331045, and 5281672 with high binding affinity and hydrogen bonds that were comparable to the control (docking score=−10.3 kcal/mol and hydrogen bonds=4). In the 200 ns post‐molecular dynamics simulation, 5281647 exhibited a stable RMSD of 0.25 nm, which was comparable to the control. The maximum number of hydrogen bonds was exhibited by 57331045, while 5281647 and 5281672 consistently exhibited four hydrogen bonds. Overall, the Principal component analysis (PCA) and Free Energy Landscape (FEL) analyses of the complexes demonstrated that these three compounds were in stable states. In comparison to the control (ΔGTOTAL=−39.58 kcal/mol), the cumulative binding free energy (ΔGTOTAL) for 5281647 and 57331045 was −39.95 kcal/mol and −39.25 kcal/mol, respectively. This further confirms the superior binding affinity of the two compounds. Both 5281647 and 57331045 were identified as potent inhibitors of the LasR transcription factor, which is essential for the quorum sensing of Pseudomonas aeruginosa, in the present investigation. These findings underscore the importance of further exploration and optimization of phytochemicals for combating bacterial infections, offering promising avenues for future drug discovery efforts targeting this resilient pathogen. [ABSTRACT FROM AUTHOR]

The probable cytotoxic activity, probable hematotoxicity, and probable acute toxicity on oral administration to mice (LD50 mouse oral ASNN) of previously synthesized 5-substituted (Br, Cl, NO2) derivatives of N1-carboxymethyl-5-fluorouracil were calculated. The calculation data showed that all compounds exhibited high cytotoxic activity and low probable acute toxicity on oral administration to mice. It should be noted that in vitro investigation of cytotoxic activity (PrestoBlue, Invitrogen) showed that these compounds do not exhibit cytotoxic activity against cell lines of presumptively normal and tumor origin. The low cytotoxicity of these compounds indicated by the results of in vitro experiments will allow them to be used at quite high concentrations as compared with 5-fluorouracil for future in vivo studies. [ABSTRACT FROM AUTHOR]

The utilization of pharmaceuticals in medical and veterinary treatment has not only improved human and animal health but has also boosted food-production and economic welfare. However, the release of pharmaceuticals in the environment through various pathways, such as manufacturing, human excretion, and substandard disposal, can have detrimental effects on ecosystems and various biological entities associated with these systems. High levels of pharmaceutical residues have been detected further downstream of manufacturing facilities, and untreated veterinary medication leftovers can end up in waterbodies. Methods utilizing artificial intelligence (AI) and machine learning (ML) have been employed to establish connections between chemical structure and biological activity, referred to as quantitative structure--activity relationships (QSARs) for the compounds. QSAR models use chemical structures to predict hazardous activity when experimental data is lacking, thereby helping prioritize chemicals for testing and compilation. Combinatorial chemistry, by enabling high-throughput compound synthesis, accelerates the generation of targeted molecules for testing across various fields. Green chemistry helps in creating, designing, and implementing chemical products and procedures with the aim of minimizing or eradicating the generation and subsequent utilization of harmful substances. In addition, pharmaceutical sensor technologies (PST) are critical tools in modern medicine, enabling precise detection and monitoring of various biochemical and physiological markers and parameters. The synergy between AI, ML, QSAR modeling, and the implementation of combinatorial and green chemistry methodologies is pivotal in driving the development of innovative products and PST in pharmaceutics. This interdisciplinary approach is crucial for creating solutions with reduced toxicity in pharmaceutical processes, thereby ensuring enhanced public safety and promoting the sustainability of environmental resources. By integrating these advanced methodologies, the pharmaceutical industry can achieve greater detection accuracy, efficiency in production of eco-friendly products, ultimately leading to safer pharmaceutics and a healthier planet. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is a malignant tumor with global incidence and death ranking fifth and fourth, respectively. GC patients nevertheless have a poor prognosis despite the effectiveness of more advanced chemotherapy and surgical treatment options. The second most frequently mutated gene in GC is PI3Kalpha, a confirmed oncogene that results in abnormal PI3K/AKT/mTOR signaling, causing enhanced translation, proliferation, and survival, and is mutated in 7–25% of GC patients. The protein PI3Kalpha was targeted in the present study by utilizing machine learning (ML), molecular docking, and simulation. A total of 9214 molecules from the DrugBank database were chosen for the first screening. A training set for 6770 compounds tested against PI3Kalpha was assessed to create a quantitative structure-activity relationship-based machine learning model using five different classification algorithms: random forest, random tree, J48 pruned tree, decision stump, and REPTree. Furthermore, consideration was given to the random forest classifier for screening based on its performance index (Kappa statistics, ROC, and MCC). Overall, 1539 of the 9214 drug bank compounds were predicted to be active. Thereafter, three pharmacological filters, Lipinski's rule, Ghose filter, and Veber rule, were applied to test the drug-like properties of the screened compounds. Twenty-six of 1593 compounds showed excellent drug-like properties and were further considered for molecular docking. Thereafter, two compounds were screened as hits because they possessed the molecular docked position with the lowest binding energy and an excellent bonding profile. The binding stability of the selected compounds was further assessed through molecular dynamics simulations for up to 100 ns. Furthermore, compound 1-(3-(2,4-dimethylthiazol-5-YL)-4-oxo-2,4-dihydroindeno[1,2-C]pyrazol-5-YL)-3-(4-methylpiperazin-1-YL) urea was selected as a potential hit in the final screening by analyzing a number of parameters, including the Rg, RMSD, RMSF, H bonding, and SASA profile. Therefore, we conclude that compound 1-(3-(2, 4-dimethylthiazol-5-YL)-4-oxo-2,4-dihydroindeno[1,2-C]pyrazol-5-YL)-3-(4-methylpiperazin-1-YL) urea has efficient inhibitory potential against PI3Kalpha protein and could be utilized for the development of effective drugs against GC. [ABSTRACT FROM AUTHOR]

ADME (Absorption, Distribution, Metabolism, Excretion) properties are key parameters to judge whether a drug candidate exhibits a desired pharmacokinetic (PK) profile. In this study, we tested multi‐task machine learning (ML) models to predict ADME and animal PK endpoints trained on in‐house data generated at Boehringer Ingelheim. Models were evaluated both at the design stage of a compound (i. e., no experimental data of test compounds available) and at testing stage when a particular assay would be conducted (i. e., experimental data of earlier conducted assays may be available). Using realistic time‐splits, we found a clear benefit in performance of multi‐task graph‐based neural network models over single‐task model, which was even stronger when experimental data of earlier assays is available. In an attempt to explain the success of multi‐task models, we found that especially endpoints with the largest numbers of data points (physicochemical endpoints, clearance in microsomes) are responsible for increased predictivity in more complex ADME and PK endpoints. In summary, our study provides insight into how data for multiple ADME/PK endpoints in a pharmaceutical company can be best leveraged to optimize predictivity of ML models. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) requires the provision of learnable data to successfully deliver requisite prediction power. In this article, it is demonstrable that standard physico-chemical parameters, while useful, are insufficient for the development of powerful antimicrobial prediction algorithms. Initial models that focussed solely on the values extractable from the knowledge on electrotopological, structural and constitutional descriptors did not meet the acceptance criteria for classifying antimicrobial activity. In contrast, efforts to conceptually define the diametric opposite of an antimicrobial compound helped to advance the predicted category as a learnable trait. Remarkably, the inclusion of ligand–receptor interactions using the ability of the molecules to stimulate transmembrane TAS2Rs receptor helped to increase the ability to distinguish the antimicrobial molecules from the inactive ones, confirming the hypothesis of a predictor–predicted synergy behind bitterness psychophysics and antimicrobial activity. Therefore, in a single bio–endogenic psychophysical vector representation, this manuscript helps demonstrate the contribution to parametrization and the identification of relevant chemical manifolds for molecular design and (re-)engineering. This novel approach to the development of AI models accelerated molecular design and facilitated the selection of newer, more powerful antimicrobial agents. This is especially valuable in an age where antimicrobial resistance could be ruinous to modern health systems. [ABSTRACT FROM AUTHOR]

Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental in this transformation is Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided drug design tool uses machine learning to predict the biological activity of new compounds based on the numerical representation of chemical structures against various biological targets. With diabetes mellitus becoming a significant health challenge in recent times, there is intense research interest in modulating antidiabetic drug targets. α-Glucosidase is an antidiabetic target that has gained attention due to its ability to suppress postprandial hyperglycaemia, a key contributor to diabetic complications. This review explored a detailed approach to developing QSAR models, focusing on strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical machine learning algorithms to modern deep learning algorithms. We also highlighted studies that have used these approaches to develop predictive models for α-glucosidase inhibitors to modulate this critical antidiabetic drug target.

Abstract Background Primary aromatic amines (PAAs) present significant challenges in the prediction of mutagenicity using current standard quantitative structure activity relationship (QSAR) systems, which are knowledge-based and statistics-based, because of their low positive prediction values (PPVs). Previous studies have suggested that PAAs are metabolized into genotoxic nitrenium ions. Moreover, ddE, a relative-energy based index derived from quantum chemistry calculations that measures the stability nitrenium ions, has been correlated with mutagenicity. This study aims to further examine the ability of the ddE-based approach in improving QSAR mutagenicity predictions for PAAs and to develop a refined method to decrease false positive predictions. Results Information on 1,177 PAAs was collected, of which 420 were from public databases and 757 were from in-house databases across 16 laboratories. The total dataset included 465 Ames test-positive and 712 test-negative chemicals. For internal PAAs, detailed Ames test data were scrutinized and final decisions were made using common evaluation criteria. In this study, ddE calculations were performed using a convenient and consistent protocol. An optimal ddE cutoff value of -5 kcal/mol, combined with a molecular weight ≤ 500 and ortho substitution groups yielded well-balanced prediction scores: sensitivity of 72.0%, specificity of 75.9%, PPV of 65.6%, negative predictive value of 80.9% and a balanced accuracy of 74.0%. The PPV of the ddE-based approach was greatly reduced by the presence of two ortho substituent groups of ethyl or larger, as because almost all of them were negative in the Ames test regardless of their ddE values, probably due to steric hindrance affecting interactions between the PAA and metabolic enzymes. The great majority of the PAAs whose molecular weights were greater than 500 were also negative in Ames test, despite ddE predictions indicating positive mutagenicity. Conclusions This study proposes a refined approach to enhance the accuracy of QSAR mutagenicity predictions for PAAs by minimizing false positives. This integrative approach incorporating molecular weight, ortho substitution patterns, and ddE values, substantially can provide a more reliable basis for evaluating the genotoxic potential of PAAs.

1,2,4-Triazole derivatives are of researchers’ significant interest due to their diverse biological properties, such as antimicrobial, anti-inflammatory, anticancer, and antioxidant activities. The integration of a 2-bromo-4-fluorophenyl fragment into the triazole structure can significantly enhance these activities. However, the evaluation of the toxicity of such compounds remains a critically important aspect for their practical application. To reduce the time and cost of experimental studies, QSAR (Quantitative Structure-Activity Relationship) methods are actively used, allowing the prediction of toxicity based on the molecular structure of compounds. Aim of the study. To assess the toxicity of new S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-R-1,2,4-triazol-3-thiols using the QSAR method, specifically to predict acute toxicity parameters (LD50), and to determine the influence of different (length) radicals on the toxicity of these compounds. Materials and methods. The objects of the virtual study were derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols. They were evaluated at the Department of Toxicological and Inorganic Chemistry of the Zaporizhzhia State Medical and Pharmaceutical University. The toxicity assessment was conducted using the nearest neighbor method via the Toxicity Estimation Software Tool (TEST). The prediction of the lethal dose (LD50) for rats was based on the structural similarity of the studied compounds with known substances, for which experimental toxicity data are available. Results. The conducted QSAR analysis demonstrated that structural changes in S-derivatives of 5-(2-bromo-4-fluorophenyl)-4-ethyl-1,2,4-triazol-3-thiols significantly affect the predicted toxicity. The primary factor influencing the changes in LD50 values is the variation of radicals at the 5th position of the triazole ring. Conclusions. The results of the study showed, that the toxicity of new S-alkyl derivatives of triazol-3-thiols depends on the type of alkyl substituent. Compounds with propyl to heptyl fragments exhibit increased toxicity, while derivatives with thiol, octyl, nonyl, and decyl residues are characterized by lower toxicity.

New derivatives of 1,2,4-triazole are promising research targets due to their unique biological properties, including antimicrobial, antifungal, antitumor, and antioxidant activities. The introduction of the 2-bromo-5-methoxyphenyl fragment into the triazole structure potentially enhances these properties. However, the issue of toxicity for such compounds remains a critical factor for their further application. To reduce experimental costs and time, QSAR (Quantitative Structure-Activity Relationship) methods are widely applied, allowing to predict compounds toxicity based on their molecular structure. The aim of this study was to evaluate the toxicity of new derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols, their acids, and esters using the QSAR method to predict parameters of acute toxicity (LD50) and to assess the influence of various radicals on the toxicity of the compounds. Materials and methods. The objects of this study were derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols, synthesized at the Department of Toxicological and Inorganic Chemistry of Zaporizhzhia State Medical and Pharmaceutical University. The nearest neighbor method was used for toxicity evaluation, applying the Toxicity Estimation Software Tool (TEST). The prediction of rats lethal dose (LD50) was based on the structural similarity of the studied compounds with known substances that have experimental toxicity data. Results. The QSAR analysis revealed that structural modifications in the derivatives of 5-(2-bromo-5-methoxyphenyl)-4-R-1,2,4-triazole-3-thiols significantly influence their toxicity. Specifically, increasing the size of the radicals, especially through the introduction of aromatic fragments, contributed to the enhanced safety of the compounds, as evidenced by the increase in LD50 values. The highest LD50 values were observed for compounds containing phenyl radicals. Conclusions. The results of this study indicate the feasibility of using QSAR models to predict the toxicity of 1,2,4-triazole derivatives containing a 2-bromo-5-methoxyphenyl fragment. The observed trend of increasing safety with the introduction of larger aromatic radicals can be used for the rational design of new compounds with improved toxicological properties.

Abstract Tyrosinase is an enzyme crucial for the progression of melanogenesis. Immoderate production of melanin may be the cause of hyperpigmentation and darkening leading to skin diseases. Tyrosinase is the most researched target for suppressing melanogenesis since it catalyzes the rate-limiting stage of melanin production. Thiosemicarbazones have been reported to possess strong inhibition capability against tyrosinase. We have designed and synthesized eighteen N-tosyl substituted indole-based thiosemicarbazones as competitive tyrosinase inhibitors in the current work. All the compounds exhibited outstanding to good potency with half maximal inhibitory concentration in the range of 6.40 ± 0.21 µM to 61.84 ± 1.47 µM. The compound 5r displayed the top-tier inhibition amongst the entire series with IC50 = 6.40 ± 0.21 µM. Compounds, 5q and 5r exhibited competitive inhibitions in concentration dependent manner with Ki = 3.42 ± 0.03 and 10.25 ± 0.08 µM respectively. The binding mode of 5r was evaluated through in silico molecular dynamics simulations and molecular docking, while ADME assessment studies predicted the drug-like characteristics of the derivatives. The newly synthesized derivatives may serve as a structural guide for designing and developing novel tyrosinase inhibitors.

Abstract Migraine is considered one of the debilitating primary headache conditions with an estimated worldwide occurrence of approximately 14–15%, contributing highly to factors responsible for global disability. Calcitonin gene-related peptide (CGRP) is a neuropeptide that plays a crucial role in the pathophysiology of migraines and thus, its inhibition can help relieve migraine symptoms. However, conventional process of CGRP drug development has been laborious and time-consuming with incurred costs exceeding one billion dollars. On the other hand, machine learning (ML)-based approaches that are capable of accurately identifying CGRP inhibitors could greatly facilitate in expediting the discovery of novel CGRP drugs. Therefore, this study proposes a novel and high-accuracy meta-model, namely MetaCGRP, that can precisely identify CGRP inhibitors. To the best of our knowledge, MetaCGRP is the first SMILES-based approach that has been developed to identify CGRP inhibitors without the use of 3D structural information. In brief, we initially employed different molecular representation methods coupled with popular ML algorithms to construct a pool of baseline models. Then, all baseline models were optimized and used to generate multi-view features. Finally, we employed the feature selection method to optimize the multi-view features and determine the best feature subset to enable the construction of the meta-model. Both cross-validation and independent tests indicated that MetaCGRP clearly outperforms several conventional ML classifiers, with accuracies of 0.898 and 0.799 on the training and independent test datasets, respectively. In addition, MetaCGRP in conjunction with molecular docking was utilized to identify five potential natural product candidates from Thai herbal pharmacopoeia and analyze their binding affinity and interactions to CGRP. To facilitate community-wide efforts in expediting the discovery of novel CGRP inhibitors, a user-friendly web server for MetaCGRP is freely available at https://pmlabqsar.pythonanywhere.com/MetaCGRP .

Objective: The objective of this investigation is to create a trustworthy Quantitative Structure-Activity Relationship (QSAR) model that generates little to no side effects and is low-cost for treating colon cancer using experimental data obtained from the literature. Methods: ChemDraw software was used for creating molecular structures, which were then optimized using Spartan 14 software to generate quantum chemical descriptors. Data pre-treatment and data division were performed using specific software packages. Additionally, analysis and validation tasks were carried out using software tools such as Discovery Studio Visualizer, PyRx for docking, SwissADME for pharmacokinetics studies, and Desmond for molecular dynamic (MD) simulation. Results: The developed QSAR model demonstrates good predictive quality with a Mean Absolute Error (MAE) of 1.3313 and high internal validation metrics (R2 ​= ​0.9407, adjusted R2 ​= ​0.9329). External validation on a test set yields satisfactory results (R2 ​= ​0.9012, adjusted R2 ​= ​0.8436, CCC ​= ​0.9229). Docking analysis identifies compounds 111 and 112 as having the lowest binding affinity of −10.4 kJ/mol, characterized by specific molecular properties. Additionally, MD simulation provides insights into the dynamic behavior and interaction types of the protein-ligand complex, contributing to a deeper understanding of their stability and fluctuations. Conclusion: The model validation parameters confirm the reliability and robustness of the model. The pharmacokinetics study validates the drug-likeness of the drug candidate through various parameters. The MD simulation sheds light on the dynamic behavior and interaction types of the protein-ligand complex, enhancing our understanding of their stability and fluctuations.

Employing quantitative structure–activity relationship (QSAR)/ quantitative structure–property relationship (QSPR) models, this study explores the application of fullerene derivatives as nanocarriers for breast cancer chemotherapy drugs. Isolated drugs and two drug–fullerene complexes (i.e., drug–pristine C60 fullerene and drug–carboxyfullerene C60–COOH) were investigated with the protein CXCR7 as the molecular docking target. The research involved over 30 drugs and employed Pearson’s hard–soft acid–base theory and common QSAR/QSPR descriptors to build predictive models for the docking scores. Energetic descriptors were computed using quantum chemistry at the density functional-based tight binding DFTB3 level. The results indicate that drug–fullerene complexes interact more with CXCR7 than isolated drugs. Specific binding sites were identified, with varying locations for each drug complex. Predictive models, developed using multiple linear regression and IBM Watson artificial intelligence (AI), achieved mean absolute percentage errors below 12%, driven by AI-identified key variables. The predictive models included mainly quantitative descriptors collected from datasets as well as computed ones. In addition, a water-soluble fullerene was used to compare results obtained by DFTB3 with a conventional density functional theory approach. These findings promise to enhance breast cancer chemotherapy by leveraging fullerene-based drug nanocarriers.

Metal oxide nanoparticles (MONPs) are widely used in medicine and environmental remediation because of their unique properties. However, their size, surface area, and reactivity can cause toxicity, potentially leading to oxidative stress, inflammation, and cellular or DNA damage. In this study, a nano-quantitative structure–toxicity relationship (nano-QSTR) model was initially developed to assess zebrafish toxicity for 24 MONPs. Previously established 23 first- and second-generation periodic table descriptors, along with five newly proposed third-generation descriptors derived from the periodic table, were employed. Subsequently, to enhance the quality and predictive capability of the nano-QSTR model, a nano-quantitative read across structure–toxicity relationship (nano-qRASTR) model was created. This model integrated read-across descriptors with modeled descriptors from the nano-QSTR approach. The nano-qRASTR model, featuring three attributes, outperformed the previously reported simple QSTR model, despite having one less MONP. This study highlights the effective utilization of the nano-qRASTR algorithm in situations with limited data for modeling, demonstrating superior goodness-of-fit, robustness, and predictability (R2 = 0.81, Q2LOO = 0.70, Q2F1/R2PRED = 0.76) compared to simple QSTR models. Finally, the developed nano-qRASTR model was applied to predict toxicity data for an external dataset comprising 35 MONPs, addressing gaps in zebrafish toxicity assessment.

Thermally activated delayed fluorescence (TADF) materials have garnered significant attention in developing high-efficiency organic light-emitting diodes (OLEDs) for next-generation displays. Despite the progress in TADF research, the increasing complexity of molecular structures poses challenges in material design and synthesis. This study explores the potential of quantitative structure–activity relationship (QSAR) calculations, particularly AutoQSAR, to streamline TADF OLED material selection and design. By leveraging the predictive capabilities of QSAR, we aimed to enhance the accuracy and efficiency of material development. We employed computational modeling, synthesis, and device fabrication to evaluate the performance of a newly developed green TADF dopant material. Our findings indicate that QSAR-guided design predicts material properties effectively and optimizes OLED performance. The synthesized TADF material demonstrated promising efficiency, highlighting the advantages of integrating QSAR calculations into the material discovery process. This study underscores the feasibility of QSAR methodologies in the context of OLED materials, suggesting a pathway for faster and more accurate development. Future improvements in QSAR techniques and collaborative efforts between computational and experimental research will be essential in driving further advancements in OLED technology.

Context: In the last decade, antimicrobial resistance cases have been widespread. The discovery and development of new drugs need to be done to overcome the case. Some research has found that some compounds, which are curcumin transformation derivatives, are able to inhibit the growth of Staphylococcus aureus. Aims: To evaluate the development of antimicrobial candidates of curcumin versus S. aureus. Methods: The in silico approach method, along with the QSAR technique, plays an important role in the process of discovery and development of new drugs. In this study, we focused on developing curcumin transformation derivatives that are much more potent by making the best QSAR equation of curcumin and eight curcumin transformation derivatives that have been tested in vitro for their antimicrobial activity against Staphylococcus aureus. Results: The best QSAR equation was obtained from curcumin transformation derivatives as antimicrobial activity against S. aureus, with pMIC = 0.812 (± 0.162)EHOMO +5.443 (± 1.659) (n = 9; Sig = 0.002; R = 0.884; R2 = 0.782; F = 25.153; Q2 = 0.57. Conclusions: In this study, an increase in the antimicrobial activity of curcumin transformation derivatives against S. aureus by increasing EHOMO was observed. The best QSAR equation can be a tool to obtain a more potential new chemical structure model and reduce trials and errors.

الملخص: أهداف البحث: بحلول عام 2030، من المتوقع أن يتسبب سرطان البروستاتا في 1.7 مليون حالة جديدة و499 ألف حالة وفاة. أهداف هذا البحث هي إنشاء نموذج يربط بين تصرفات ثيوسيميكاربازون-إندول كعامل مضاد للسرطان ضد خط خلايا بي سي 3، وإجراء تحليل الالتحام بين المركبات والإنزيم المستهدف، والتنبؤ بالحركية الدوائية والتشابه الدوائي للمركبات قيد التحقيق. طريقة البحث: استخدمت الطريقة العلاقة الكمية بين البنية والنشاط لبناء النموذج، وأجرت الالتحام الجزيئي بين المركبات والإنزيم المستهدف، وفحصت تشابهها مع الأدوية وتحليل الحرائك الدوائية للمركبات المثبطة. النتائج: تم استخدام منهج الانحدار متعدد الخطوط لخوارزمية الوظيفة الجينية في بناء نموذج العلاقة بين الهيكل الكمي والنشاط. المعلمات التالية من نموذج البناء الأول، كأفضل، آر2 (معامل التحديد) = 0.972517، ''رادج'' (المعدل آر- التربيعي) = 0.964665، ''سي آر بي 2'' = 0,780922، و ''إل أو إف'' (التحقق من صحة التقاطع لمرة واحدة) = 0.076524، ظهر بقوة على الواصفات الجزيئية. كانت ''إس إتش بي دي'' و ''إس إس سي إتش 3'' و ''جاي جي آي 2'' و ''أر دي إف 60 بي'' تعتمد بشكل كبير على النشاط التكاثري. تتمتع المركبات ذات المعرفين 7 و22 بالقدرة على العمل كمثبطات لمستقبلات الأندروجين، كما اقترحت دراسات الالتحام الجزيئي بين الأدوية والإنزيمات المستهدفة. تظهر المركبات ذات المعرفين 7 و22 قيد التحقيق درجات ربط تبلغ -8.5 سعرة حرارية/مول و-8.8 سعرة حرارية/مول، على التوالي. كانت الجزيئات ذات المعرفين 7 و22 ضمن الحد الأقصى المقبول لجزيئات الدواء لتكون متاحة بيولوجيا عن طريق الفم. الاستنتاجات: يقدم هذا البحث رؤى قيمة حول العلاقة بين الواصفات الجزيئية والمثبطات المحتملة والخصائص الدوائية في علاج بي سي 3. تساهم هذه النتائج في فهم الخيارات العلاجية الجديدة لمرضى سرطان البروستاتا وتطويرها المحتمل. Abstract: Objectives: By 2030, prostate cancer is estimated to account for 1.7 million new cases and 499,000 deaths. The objectives of this research were to create a model revealing the activity of thiosemicarbazone-indole compounds as anticancer agents against the PC3 cell line; perform docking analysis between the compounds and the target enzyme; and predict the pharmacokinetics and drug-likeness of the compounds under investigation. Methods: The quantitative structureactivity relationship (QSAR) method was used to build the model; molecular docking between the compounds and the target enzyme was performed; and the drug-likeness and pharmacokinetics of the inhibiting compounds was examined. Results: The genetic function algorithm-multilinear regression approach was used for building the QSAR model. Build model 1 had the best performance, with R2 (coefficient of determination) = 0.972517, Radj (adjusted R-squared) = 0.964665, (CRp2) = 0.780922, and LOF (leave-one-out cross-validation) = 0.076524, demonstrated strongly indicated by the molecular descriptors. SHBd, SsCH3, JGI2, and RDF60P were highly dependent on proliferative activity. Compounds ID 7 and 22 had the potential to act as androgen receptor inhibitors, as suggested by molecular docking studies between the drugs and their target enzymes. Compounds ID 7 and 22 exhibited binding scores of −8.5 kcal/mol and −8.8 kcal/mol, respectively. The approved maximum medication molecules for oral bioavailability included the molecules with IDs 7 and 22. Conclusion: This research provides valuable insights into the relationships among molecular descriptors, potential inhibitors, and pharmacokinetic properties in the treatment of PC3. These findings may contribute to the understanding and potential development of new therapeutic options for prostate cancer patients.

Alizarin is a polycyclic compound isolated from roots of Rubia tinctorum that has potential as a breast anticancer candidate. Increasing anticancer activity can be done through structural modification to produce derivatives in the form of group substitution in the meta position using acyl. The purpose of this work is to forecast the anticancer activity of alizarin and its derivatives on the MMP-9 receptor using. Important biological activity factors will be identified by Quantitative Structure Activity molecular docking Relationship (QSAR) and projected absorption, distribution, metabolism, elimination, and toxicity (ADMET). Using Molegro Virtual Docker (MVD), molecular docking was carried out on the MMP 9 receptor (4WZV.pdb). LogP, Etot, and MR are the physicochemical parameters that are examined in order to produce QSAR. Statistical Package for the Social Science (SPSS) was used for the QSAR analysis. The pkCSM was utilized to determine ADMET prediction. The acyl alizarin derivatives have a lower rerank score than alizarin, according to the docking results so that they are predicted to have more potent anticancer activity. The QSAR analysis's findings indicated that logP and Etot had the greatest effects on the alizarin compound's and its derivatives' activity. The results of the ADMET prediction indicate that acyl alizarin is less harmful and superior to alizarin. Research findings show that it is possible to synthesize acyl alizarin derivatives, especially alizarin octanoate, which will then be tested in vitro or in vivo to determine its anti-breast cancer activity and toxicity.

Diabetes mellitus (DM) is a chronic metabolic disorder posing a significant health risk. Effective treatments are continually sought. Coumarin derivatives with oxime ester groups have shown potential as antidiabetic agents by inhibiting the α-glucosidase enzyme, a key player in glycoprotein metabolism and postprandial hyperglycemia control. This makes lysosomal α-glucosidase a promising therapeutic target. A study used 28 coumarin derivatives with known α-glucosidase inhibitory IC50 values for computer-assisted drug design (CADD) through quantitative structure-activity relationship (QSAR) analysis, yielding a statistically robust equation for guiding new compound development. Subsequently, eleven new coumarin derivatives with oxime ester groups were synthesized, showing enhanced α-glucosidase inhibitory activity compared to the initial set. Molecular docking assays indicated that compounds 32, 37, 38, and 39 had lower free energy values than the native ligand, suggesting higher stability in target protein interactions. Notably, compound 38 had the lowest free energy (-8.351) and demonstrated lower root mean square deviation (RMSD) and root mean square fluctuation (RMSF) than the original ligand, indicating greater stability. The QSAR equation derived is: Log IC50 = 2.886 - 0.054 (LUMO) + 0.073 (μ) – 0.148 (α) – 0.046 (RD) + 0.046 (BM) + 0.001 (Vvdw) – 0.421 (qC2) + 1.138 (qC8) – 0.092 (qC9) + 2.61 (qC10) + 1.354 (qN1) (Eq 1) n=28; R=0.918; R2=0.843; SD=0.196; F hit/F tab=3.169; Sig =

Quantitative structure–activity relationship (QSAR) models are routinely used to predict the properties and biological activity of chemicals to direct synthetic advances, perform massive screenings, and even to register new substances according to international regulations. Currently, nanoscale QSAR (nano-QSAR) models, adapting this methodology to predict the intrinsic features of nanomaterials (NMs) and quantitatively assess their risks, are blooming. One of the challenges is the characterization of the NMs. This cannot be done with a simple SMILES representation, as for organic molecules, because their chemical structure is complex, including several layers and many inorganic materials, and their size and geometry are key features. In this review, we survey the literature for existing predictive models for NMs and discuss the variety of calculated and experimental features used to define and describe NMs. In the light of this research, we propose a classification of the descriptors including those that directly describe a component of the nanoform (core, surface, or structure) and also experimental features (related to the nanomaterial’s behavior, preparation, or test conditions) that indirectly reflect its structure.

Abstract 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.

AbstractTuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb) infection, stands as a global infectious disease presenting substantial public health challenges due to its high incidence and mortality rates. The prolonged use of conventional anti-TB therapies has led to the emergence of severe drug resistance in Mtb, resulting in extended treatment durations, increased costs, poor patient compliance, and reduced cure rates. This phenomenon has posed a significant burden on global TB prevention and control efforts, necessitating a shift in research focus toward the exploration of novel anti-TB drugs. In this context, utilizing QSAR modeling methods, our study systematically investigated the relationship between the chemical structures of 36 aryl fluorosulfate derivatives and their inhibitory activity against Mtb. Robust and predictive Topomer CoMFA and HQSAR models were developed, featuring Topomer CoMFA model parameters: q2 = 0.659, r2 = 0.969, F = 102.877, N = 6, SEE = 0.138; HQSAR model parameters: q2 = 0.705, r2 = 0.873, SEE = 0.264, HL = 199, N = 4. Leveraging these models, structural modifications were applied to the compounds using the ZINC15 database, leading to the successful design and screening of three novel compounds with desirable inhibitory activity. Molecular docking and ADMET performance prediction results indicated that these three new compounds exhibit strong binding capabilities and promising pharmaceutical potential. This study provides valuable insights and research directions for the development of aryl fluorosulfate derivatives as potential agents for tuberculosis treatment and as novel drugs. [ABSTRACT FROM AUTHOR]

WD repeat‐containing protein 5 (WDR5) and mixed lineage leukemia (MLL) are critical for maintaining tumorigenesis in human cancers. Disruption of the MLL1–WDR5 interaction has been viewed as a promising therapeutic strategy for the treatment of leukemia. Here, we report a series of protein–protein binding interaction modes targeting MLL1‐WDR5 inhibitors using 3D‐QSAR, and molecular docking. CoMFA with q2 = 0.724, r2 = 0.957, rpred2 = 0.874 and CoMSIA with q2 = 0.826, r2 = 0.995, rpred2 = 0.827. Topomer CoMFA results show q2 = 0.807, r2 = 0.976, rpred2 = 0.789, and HQSAR results show q2 = 0.873, r2 = 0.982, rpred2 = 0.794. The 3D‐QSAR model with high validation and prediction performance is successfully constructed. Contour maps and molecular docking results according to the four models, 26 new compounds are finally designed on the computer after molecular screening. Further molecular dynamics simulations (MD) of compounds G01, G04, G09, G43, and G44 with high predicted activity are carried out to explore their possible binding modes. ILE305, PHE133, CYS261, and ALA176 are found to play crucial roles in stabilizing the inhibitor. ADMET predictions are also performed for these 26 new compounds. These results serve as references for the design of effective WDR5‐MLL1 inhibitors in the future. [ABSTRACT FROM AUTHOR]

Pinostrobin, an isolated compound from Boesenbergia rotunda, has been shown to have potent anti‐proliferation and apoptosis effects in cancer stem‐like cells. However, its hydrophobic properties reduce its bioavailability. Eight new alkyl‐TPP+ pinostrobin derivatives were synthesized and assessed for their cytotoxicity against the human HepG2 liver cancer cell line using the alamarBlue assay. All derivatives exhibited moderate cytotoxicity, with the IC50 values ranging from 38.55–101.95 μM, and were more potent than pinostrobin and pinostrobin hydrazone (IC50>100 μM). Four alkyl‐TPP+ pinostrobin hydrazone amide derivatives showed more potent cytotoxicity than four alkyl‐TPP+ pinostrobin ester derivatives. QSAR analysis showed key 3D structural descriptors of TPP+‐based compounds responsible for cytotoxicity against HepG2 cells for the first time. The resultant 3D‐QSAR models using PLS and PCR methods were evaluated and found reliable in predicting the cytotoxicity of other TPP+‐based compounds. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, quantitative structure-activity relationship (QSAR) analysis was conducted on 20 homologous compounds of benzimidazole derivatives. The structures of the benzimidazole derivatives were optimized using the semiempirical Parameterized Model 3 method of HyperChem for Windows 8.0 to obtain 14 descriptors. Then, multiple linear regression (MLR) analysis was performed using the backward method. The results of the MLR analysis obtained four new QSAR equation models. Based on statistical criteria, model 1 was determined as the best QSAR equation model in predicting the theoretical IC50 values of the new benzimidazole derivatives. As many as 20 new compounds of benzimidazole derivatives were modeled, of which 13 new compounds (23, 24, 25, 26, 27, 28, 29, 30, 31, 37, 38, 39, and 40 compounds) were recommended for synthesis in the laboratory because these compounds of benzimidazole derivatives havethey theoretically had higher antimalarial activity than chloroquine. [ABSTRACT FROM AUTHOR]

Efficient drug development holds prime importance in the present era. Computational techniques offer potential solutions for efficacious drug design. The present review attempts to summarize the essentiality of the Quantitative Structure–Activity Relationship (QSAR) of Schiff bases and thiosemicarbazones for developing potent therapeutics. It provides an overview of recent QSAR computational studies conducted to develop Schiff bases, their derivatives as medicinal agents, and their activity alteration upon substitution and structural changes. Various recent research papers, primarily from leading indexing sources and databases like SCOPUS, Web of Science, PubMed, Medline, etc., have focused on the studies reported during the last five years. Software like HYPERCHEM, MatLaB, DRAGON and RECKON are generally used for the QSAR analysis. Analysis of Schiff bases using QSAR showed that complexes with high molecular weight exhibit antibacterial activity. Computer-aided technology channelizes drug development of potential lead compounds and considerably contributes to the discovery and expansion of drugs. However, certain aspects viz., accuracy for the prediction of drug-target binding affinity, conformational changes in protein, prediction of physical properties of novel drugs and allosteric sites, differences between around thousands of molecular descriptors, limited biological response and alignment protocol of training-set and test-set ligands need further exploration. [ABSTRACT FROM AUTHOR]

Fraction unbound in plasma (fu,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 fu,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 fu,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 fu,p values. The molecular fragments may help for the assessment of the fu,p values of different small molecules/drugs in a speedy way in comparison to experiment-based in vivo and in vitro studies. [ABSTRACT FROM AUTHOR]

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 IC50 values. Remarkably, one of the identified leads possesses a novel chemotype compared to known ALK inhibitors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Outbreaks of Monkeypox (mpox) in over 100 non-endemic countries in 2022 represented a serious global health concern. Once a neglected disease, mpox has become a global public health issue. A42R profilin-like protein from mpox (PDB ID: 4QWO) represents a potential new lead for drug development and may interact with various synthetic and natural compounds. In this report, the interaction of A42R profilin-like protein with six phytochemicals found in the medicinal plant Ficus religiosa (abundant in India) was examined. Based on the predicted and compared protein–ligand binding energies, biological properties, IC50 values and toxicity, two compounds, kaempferol (C-1) and piperine (C-4), were selected. ADMET characteristics and quantitative structure–activity relationship (QSAR) of these two compounds were determined, and molecular dynamics (MD) simulations were performed. In silico examination of the kaempferol (C-1) and piperine (C-4) interactions with A42R profilin-like protein gave best-pose ligand-binding energies of –6.98 and –5.57 kcal/mol, respectively. The predicted IC50 of C-1 was 7.63 μM and 82 μM for C-4. Toxicity data indicated that kaempferol and piperine are non-mutagenic, and the QSAR data revealed that piperlongumine (5.92) and piperine (5.25) had higher log P values than the other compounds examined. MD simulations of A42R profilin-like protein in complex with C-1 and C-4 were performed to examine the stability of the ligand–protein interactions. As/C and C-4 showed the highest affinity and activities, they may be suitable lead candidates for developing mpox therapeutic drugs. This study should facilitate discovering and synthesizing innovative therapeutics to address other infectious diseases. [ABSTRACT FROM AUTHOR]

The pyrimidine heterocycle plays an important role in anticancer research. In particular, the pyrimidine derivative families of uracil show promise as structural scaffolds relevant to cervical cancer. This group of chemicals lacks data-driven machine learning quantitative structure-activity relationships (QSARs) that allow for generalization and predictive capabilities in the search for new active compounds. To achieve this, a dataset of pyrimidine and uracil compounds from ChEMBL were collected and curated. A workflow was developed for data-driven machine learning QSAR using an intuitive dataset design and forwards selection of molecular descriptors. The model was thoroughly externally validated against available data. Blind validation was also performed by synthesis and antiproliferative evaluation of new synthesized uracil-based and pyrimidine derivatives. The most active compound among new synthesized derivatives, 2,4,5-trisubstituted pyrimidine was predicted with the QSAR model with differences of 0.02 compared to experimentally tested activity. [ABSTRACT FROM AUTHOR]

Introduction: The aromatase enzyme plays an important role in the progress of hormonedependent breast cancer, especially in estrogen receptor-positive (ER+) breast cancers. In case of postmenopausal women, the aromatization of androstenedione to estrone in adipose tissue is the most important source of estrogen. Generally 60%-75% of pre- and post-menopausal women suffer from estrogendependent breast cancer, and thus suppressing estrogen has been recognized to be a successful therapy. Hence, to limit the stimulation of estrogen, aromatase inhibitors (AIs) are used in the second-line treatment of breast cancer. Methods: The present computational study employed an in silico approach in the identification of natural actives targeting the aromatase enzyme from a structurally diverse set of natural products. Molecular docking, QSAR studies and pharmacophore modeling were carried out using the VLife Molecular Design Suite (version 4.6). The stability of the compounds was confirmed by molecular dynamics. Results: From molecular docking and analysis of interactions with the amino acid residues of the binding cavity, it was found that the amino acid residues interacting with the non-steroidal inhibitors exhibited p-stacking interactions with PHE134, PHE 221, and TRP 224, while the steroidal drug exemestane lacked p-stacking interactions. QSAR studies were performed using the flavonoid compounds, in order to identify the structural functionalities needed to improve the anti-breast cancer activity. Molecular dynamics of the screened hits confirmed the stability of compounds with the target in the binding cavity. Moreover, pharmacophore modelling presented the pharmacophoric features of the selected scaffolds for aromatase inhibitory activity. Conclusion: The results presented 23 hit compounds that can be developed as anti-breast cancer modulating agents in the near future. Additionally, anthraquinone compounds with minor structural modification can also serve to be potential aromatase inhibitors. The in silico protocol utilised can be useful in the drug discovery process for development of new leads from structurally diverse set of natural products that are comparable to the drugs used clinically in breast cancer therapy. [ABSTRACT FROM AUTHOR]

Some aryl E-imines were synthesized by nano-fly-ash H3PO4-catalyzed condensation of various aryl anilines and benzaldehydes under ultrasound irradiated greener solvent medium. In this condensation, the obtained yield was more than 75%. These E-imines were characterized by their physico-chemical data, microanalysis, and spectroscopic data. The characteristic spectral frequencies were employed for spectral quantitative structure–activity relationship study. The spectral QSAR study was performed with single and multi-regression analysis using Hammett σ, σ, σI, σRFR, and Swain–Lupton's constants. From the outcome of the regression, the effect of substituents on the spectral frequencies was predicted. The molecular docking study of these imines was performed by the assessment of protein–ligand interaction study with a characteristic protein. The antimicrobial activities of these imines were studied using the Bauer–Kirby disk diffusion technique with various bacterial and fungal microbes. The in vitro antimalarial activity of these imines was measured using P. Falciparum Thai protein microbes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The enzyme ROCK1 plays a pivotal role in the disruption of the tight junction protein CLDN1, a downstream effector influencing various cellular functions such as cell migration, adhesion, and polarity. Elevated levels of ROCK1 pose challenges in HCV, where CLDN1 serves as a crucial entry factor for viral infections. This study integrates a drug screening protocol, employing a combination of quantitative structure–activity relationship machine learning (QSAR-ML) techniques; absorption, distribution, metabolism, and excretion (ADME) predictions; and molecular docking. This integrated approach allows for the effective screening of specific compounds, using their calculated features and properties as guidelines for selecting drug-like candidates targeting ROCK1 inhibition in HCV treatment. The QSAR-ML model, validated with scores of 0.54 (R2), 0.15 (RMSE), and 0.71 (CCC), demonstrates its predictive capabilities. The ADME-Docking study's final results highlight notable compounds from ZINC15, specifically ZINC000071318464, ZINC000073170040, ZINC000058568630, ZINC000058591055, and ZINC000058574949. These compounds exhibit the best ranking Vina scores for protein–ligand binding with the crystal structure of ROCK1 at the C2 pocket site. The generated features and calculated pIC50 bioactivity of these compounds provide valuable insights, facilitating the identification of structurally similar candidates in the ongoing exploration of drugs for ROCK1 inhibition. [ABSTRACT FROM AUTHOR]

Protein kinase CK2 is a validated target for cancer therapy. Many natural products have shown inhibitory activity against CK2 as potential anti-cancer drug candidates. A compatible quantitative structure-activity relationship (QSAR) model of natural products is necessary to identify the structural determinants related to their biological activities and provides valuable clues for the discovery of natural leads as anticancer drugs. In this study, genetic algorithm (GA) and multiple linear regression (MLR) methods, combined with preferred molecular descriptors, were employed to build QSAR models of CK2 natural product inhibitors. The best model, composed of eight molecular descriptors, yielded Q2Loo = 0.7914 and R2 = 0.8220 for the training set and Q2ext = 0.7921 and R2ext = 0.7998 for the test set, indicating the model's robust reliability and high predictability. As a proof of concept, a true external test set, distinct from the training and test sets, was synthesized and tested in vitro to verify the predictive ability of this model. The predicted pIC50 values of 13 compounds showed less than 30% relative error (including 10 compounds with relative errors less than 20%), further validating the predictive performance of this model. And compound M18, M24, and M26 were identified as potential CK2 inhibitors with the predicted pIC50 values of 11.29, 8.79, and 12.03 respectively. Furthermore, the underlying structural mechanisms through which key molecular descriptors influenced their inhibitory activities against CK2 were elucidated. All these results provide valuable information for the discovery of CK2 inhibitors. [ABSTRACT FROM AUTHOR]

The demand for novel flavors and fragrance (F&F) compounds has increased, highlighting the need for a systematic design approach. Currently, the F&F industry relies heavily on experimental approaches without considering the potential consequences of altering the features that contribute to the fragrance of the compound. In silico approaches have great potential to identify the necessary features for creating novel F&F compounds. In the present study, Quantitative Structure–Property Relationship (QSPR) models were developed using 1208 compounds and simple 2D descriptors, focusing on the RI (retention index) as the endpoint to predict the olfactory properties of molecules. Feature selection was initially carried out by multi-layered stepwise regression followed by feature thinning using the Genetic Algorithm (GA) and optimal feature combination selection using the BSS (best subset selection) method. Final models were developed using the Partial Least Squares (PLS) method. Additionally, internal and external validation of the models was performed using different validation metrics suggesting that the developed models are reliable, predictive, reproducible, and robust. To enhance the external prediction of the developed models, an Intelligent Consensus Prediction (ICP) method was employed and CM3 (consensus model 3) (best selection of predictions (compound-wise) from individual models) was found to provide the best predictivity. The modeling descriptors suggested that the hydrophobicity, high molecular weight, aromaticity, and presence of large-size fragments (high percentage of carbon) enhance the RI values. Conversely, polarity and hydrophilicity decrease the RI values. This study can be used to optimize the stationary phase according to the flavor and fragrance compounds to obtain the desired retention index (RI values). [ABSTRACT FROM AUTHOR]

Designing compounds with a range of desirable properties is a fundamental challenge in drug discovery. In pre-clinical early drug discovery, novel compounds are often designed based on an already existing promising starting compound through structural modifications for further property optimization. Recently, transformer-based deep learning models have been explored for the task of molecular optimization by training on pairs of similar molecules. This provides a starting point for generating similar molecules to a given input molecule, but has limited flexibility regarding user-defined property profiles. Here, we evaluate the effect of reinforcement learning on transformer-based molecular generative models. The generative model can be considered as a pre-trained model with knowledge of the chemical space close to an input compound, while reinforcement learning can be viewed as a tuning phase, steering the model towards chemical space with user-specific desirable properties. The evaluation of two distinct tasks—molecular optimization and scaffold discovery—suggest that reinforcement learning could guide the transformer-based generative model towards the generation of more compounds of interest. Additionally, the impact of pre-trained models, learning steps and learning rates are investigated. Scientific contribution Our study investigates the effect of reinforcement learning on a transformer-based generative model initially trained for generating molecules similar to starting molecules. The reinforcement learning framework is applied to facilitate multiparameter optimisation of starting molecules. This approach allows for more flexibility for optimizing user-specific property profiles and helps finding more ideas of interest. [ABSTRACT FROM AUTHOR]

Hepatitis B virus (HBV) infection is a major global public health problem and a serious threat to human health. The assembly of the HBV outer shell protein is an important step in the HBV life cycle. Influencing the assembly of core proteins to block viral DNA replication has become a popular target for anti‐HBV drug development. In this study, 47 heteroarylpyrimidine (HAP) compounds were investigated by QSAR, and Topomer CoMFA and HQSAR models with strong predictive ability were developed. Eight new compounds with high activity were successfully designed by searching R groups in Topomer Search. The results of molecular docking and ADMET performance prediction showed that all these compounds have good docking scores and potential medicinal values. To further understand the possible conformations and interactions of the compounds at the protein active site, we performed molecular dynamics simulations of the four compounds with high docking scores and confirmed that these complexes have stable binding conformations by free energy mapping. The free energy calculations verified the stable binding results. These results provide an important reference and theoretical basis for the design and development of effective heteroaryldihydropyrimidine analogs as potential HBV capsid inhibitors. [ABSTRACT FROM AUTHOR]