Quantitative Structure-activity Relationship Models Research Articles

Design of novel isoxazole derivatives as tubulin inhibitors using computer-aided techniques: QSAR modeling, in silico ADMETox, molecular docking, molecular dynamics, biological efficacy, and retrosynthesis

In the current work, computational methods were used to investigate new isoxazole derivatives that could be used as tubulin inhibitors. The study aims to develop a reliable quantitative structure-activity relationship (QSAR) model, following the criteria set by Golbraikh, Tropsha, and Roy. As a result, seven candidate compounds were developed, all having higher activity than the well-established anticancer agent Cisplatin (Cisp). According to the ADMETox in silico test, the candidates Pr4, Pr5, and P6 can be toxic. As a result, we have chosen to focus our study on compounds Pr1, Pr2, and Pr3. Molecular docking analysis revealed that drug candidate Pr2 exhibits the highest stability within the oxidized quinone reductase 2 (PDB ID: 4zvm), target receptor (ΔG(Pr2) = ΔG(Pr3) = −10.4 < ΔG(Pr1) = −10.0 < ΔG(Cisp) = −7.3 kcal/mol). This finding aligns with the activity predictions made by the QSAR model. Furthermore, molecular dynamics simulations of the Pr2–4zvm complex over 100 ns confirm the ligand’s robust stability within the receptor’s active site, supporting the results obtained from molecular docking and the QSAR model predictions. The CaverDock software was utilized to identify the tunnels likely to be followed by ligands moving from the active site to the receptor surface. This analysis also helped in determining the biological efficacy of the target compounds. The results indicated that the Pr2 compound is more effective than the others. Finally, the computer-assisted retrosynthesis process of two high confidence sequences was used to synthesize drug candidates. Communicated by Ramaswamy H. Sarma

Low energy consumption pathway to improve sulfamethoxazole degradation by carbon fiber@Fe3O4-CuO: Electrocatalysis activity, mechanism and toxicity

Catalysts play a pivotal role in advanced oxidation processes for the remediation of organic wastewater. In this study, a 3D carbon fiber@Fe3O4-CuO catalyst was fabricated, and its efficacy for persulfate activation to remove sulfamethoxazole (SMX) was investigated at extremely low current density. The results of characterization revealed that the catalyst was uniformly distributed on the carbon fiber, and the loaded catalyst was Fe3O4-CuO nanoparticles with a diameter range of 20–50 nm. The SMX removal rate was significantly enhanced at extremely low current density by the metallic oxide catalyst loaded on carbon fiber. Approximately 90 % of SMX was degraded within 90 min when the electric current density was set at 0.1 mA cm−2. This modification process not only improved the persulfate activation efficiency but also enhanced the generation of hydrogen peroxide. Both radical and non-radical pathways were involved in the degradation of SMX. The degradation pathway mainly included hydroxylation, carboxylation, aniline cleavage, and desulfonation reactions. The quantitative structure–activity relationship model indicated that the potential risk of intermediate products to fish, daphnia, and green algae significantly decreased during the electrocatalytic oxidation process. This study provides a novel strategy for persulfate activation, which can significantly enhance the degradation efficiency, toxicity abatement, and energy usage effectiveness of electrocatalytic technology.

Computational models for predicting liver toxicity in the deep learning era.

Drug-induced liver injury (DILI) is a severe adverse reaction caused by drugs and may result in acute liver failure and even death. Many efforts have centered on mitigating risks associated with potential DILI in humans. Among these, quantitative structure-activity relationship (QSAR) was proven to be a valuable tool for early-stage hepatotoxicity screening. Its advantages include no requirement for physical substances and rapid delivery of results. Deep learning (DL) made rapid advancements recently and has been used for developing QSAR models. This review discusses the use of DL in predicting DILI, focusing on the development of QSAR models employing extensive chemical structure datasets alongside their corresponding DILI outcomes. We undertake a comprehensive evaluation of various DL methods, comparing with those of traditional machine learning (ML) approaches, and explore the strengths and limitations of DL techniques regarding their interpretability, scalability, and generalization. Overall, our review underscores the potential of DL methodologies to enhance DILI prediction and provides insights into future avenues for developing predictive models to mitigate DILI risk in humans.

Theoretical study on degradation of polymethyl substituted benzenes by OH radicals in the atmosphere

As volatile organic compounds (VOCs), pentamethylbenzene (5-PeMB) and hexamethylbenzene (6-HeMB) are found widely in petroleum and coal tar. Through combustion and industrial generation entering into the atmosphere, they can produce photochemical smog and secondary organic aerosols (SOA) to endanger human health and ecoenvironment eventually. In order to reveal their environmental chemistry, the OH-initiated degradation mechanisms of 5-PeMB and 6-HeMB were studied based on density functional theory (DFT). Result showed that addition pathways were the most favorable with energy barriers of 20.7 and 25.3 kJ/mol, respectively, in the two reactions. The degradation rate constants at 298 K were calculated to be 2.69 × 10−10 and 1.28 × 10−10 cm3 molecule−1 ·s−1, coinciding with the available experimental values. In the presence of OH radicals, the atmospheric lifetimes were estimated to be 2.17 and 2.78 h, respectively, for 5-PeMB and 6-HeMB. According to the quantitative structure-activity relationship (QSAR) model, the toxicity during the degradation process is decreased to fish, daphnia, and green algae.

QSAR and docking based lead optimization of nitrogen heterocycles for enhanced prostaglandin EP2 receptor agonistic potency

In the existing effort, a dataset of 309 experimentally screened molecules for in vitro (Ki) agonist potential for Prostaglandin E2 (PGE2) receptor 2 subtype (EP2), which is a metabolite of arachidonic acid that binds with and regulates cellular responses to PGE2, was investigated in the QSAR (Quantitative structure–activity relationship) study. A six-parameter QSAR model was developed that meets the specified values ​​for internal and external validation as well as random parameters such as R2tr = 0.808, Q2LMO = 0.794, R2ex = 0.781. Insightful and quantitative opinion reveals several underappreciated and distinct structural features that are responsible for the agonist potency of these molecules on Prostaglandin EP2 receptor such as; the hydrogen atom is correct 2 bonds from the donor atom, the sp2 hybridized carbon atom is correct 2 bonds from the cyclic nitrogen atom, and so on. The developed QSAR model captures the narrative as well as the novel pharmacophoric features. The QSAR effect was further demonstrated using the reported crystalline buildings of CP533536 with the Prostaglandin EP2 receptor activity. The evaluation led to the identification of valuable new pharmacophoric properties that will be used to optimize lead compounds in the future.

A breast cancer-specific combinational QSAR model development using machine learning and deep learning approaches.

Breast cancer is the most prevalent and heterogeneous form of cancer affecting women worldwide. Various therapeutic strategies are in practice based on the extent of disease spread, such as surgery, chemotherapy, radiotherapy, and immunotherapy. Combinational therapy is another strategy that has proven to be effective in controlling cancer progression. Administration of Anchor drug, a well-established primary therapeutic agent with known efficacy for specific targets, with Library drug, a supplementary drug to enhance the efficacy of anchor drugs and broaden the therapeutic approach. Our work focused on harnessing regression-based Machine learning (ML) and deep learning (DL) algorithms to develop a structure-activity relationship between the molecular descriptors of drug pairs and their combined biological activity through a QSAR (Quantitative structure-activity relationship) model. 11 popularly known machine learning and deep learning algorithms were used to develop QSAR models. A total of 52 breast cancer cell lines, 25 anchor drugs, and 51 library drugs were considered in developing the QSAR model. It was observed that Deep Neural Networks (DNNs) achieved an impressive R2 (Coefficient of Determination) of 0.94, with an RMSE (Root Mean Square Error) value of 0.255, making it the most effective algorithm for developing a structure-activity relationship with strong generalization capabilities. In conclusion, applying combinational therapy alongside ML and DL techniques represents a promising approach to combating breast cancer.

3D-QSAR Studies on High-affinity Phosphodiestera.

Recent studies have found that Phosphodiesterase-4 (PDE4) is closely related to the pathogenesis of depression, cognitive impairment and neurological impairment. Our objective is to develop potent inhibitors of the high-affinity phosphodiesterase 4D isoform (PDE4D) that can serve as radioligands for Positron Emission Tomography (PET) imaging, thereby advancing research in the field of neurological diseases. We employed a multi-step approach combining three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, classification techniques, and CoMSIA analysis to investigate the conformational relationship of highaffinity PDE4D inhibitors as PET ligands. ADMET and Drug-likeness predictions were also conducted. By utilizing these methods, our aim was to identify more potent PDE4D inhibitors. The results showed that the CoMSIA model with the best principal component scores (n=7) had a cross-validated Q2 value of 0.602 and a non-cross-validated R2 value of 0.976. These results affirmed the excellent predictive capability of the established CoMSIA model. Analysis of the generated 3D-QSAR contour plots highlighted specific regions in the molecular structure of the compounds that can be further optimized and modified. Guided by the contour plots, we designed 100 novel PDE4D inhibitors, and molecular docking was performed for the top 4 compounds with high activity. The molecular docking scores were promising, and ADMET and drug similarity predictions yielded satisfactory results. Taking into consideration these factors, compound 51c was determined to be the optimal compound, laying a solid foundation for further research. For the continued development of PDE4D PET radioligand, these models and new compounds' developing methodology offer a theoretical foundation and crucial references.

An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approaches

Merging Density Functional Theory (DFT) with the Quantitative Structure-Activity Relationship (2D/3D-QSAR) modeling represents a promising avenue for exploring antibacterial activity and discovering potential drugs effective against both gram-positive and gram-negative microorganisms. In this study, we employed this integrated approach to investigate a newly synthesized and promising class of 1,3,4-oxadiazole derivatives renowned for their high performance as antibacterial agents. To ensure an accurate characterization and thorough description of the targeted biological activity, we systematically evaluated various DFT functionals to precisely predict the geometrical and electronic descriptors of the investigated compounds. These descriptors were crucial for developing and validating the proposed 2D/3D-QSAR models. Our results reveal that the introduction of a donor group enhances the antibacterial activity of the derivatives. The analysis of molecular descriptors underscores the positive impact of this modification on the compounds' efficacy against bacteria. Additionally, our experimental compounds exhibit favorable characteristics concerning oral bioavailability, a crucial aspect in drug development. The identification of robust correlations between antibacterial activity and specific descriptors was achieved by conducting an extensive analysis that encompassed multiple linear regression (MLR), Random Forest (RF), and Artificial Neural Networks (ANN). Subsequently, a partial least squares (PLS) model was employed to construct 3D-QSAR models based on the Comparative Molecular Field Analysis (CoMFA) and Comparative Similarity Indices Analysis (CoMSIA) descriptors. Validation of the output models was performed using leave-one-out and bootstrapping strategies. The resultant 2D/3D-QSAR models demonstrated a high correlation between experimental and predicted activity values. Leveraging these models, the developed MLR model, expressed as pMIC = -12.704 + 0.260 logP – 6.104 10-03 SAG – 51.385 qN33, serves as a valuable tool for predicting antibacterial activity. we indicate that the machine learning methods, Artificial Neural Networks (ANN) and Random Forest (RF), outperform traditional models in accurately predicting antibacterial activity. We designed ten novel molecules, subjecting them to molecular docking and molecular dynamics simulations to predict their optimal postures when docked in the target and gain insights into the formed interactions. Additionally, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis was applied to discern the potential behavior of these novel compounds in the human body. As a result, the newly suggested chemical, X4, X10, exhibited robust inhibitory potential against gram-negative microbes.

A further pocket or conformational plasticity by mapping COX-1 catalytic site through modified-mofezolac structure-inhibitory activity relationships and their antiplatelet behavior

Cyclooxygenase enzymes have distinct roles in cardiovascular, neurological, and neurodegenerative disease. They are differently expressed in different type of cancers. Specific and selective COXs inhibitors are needed to be used alone or in combo-therapies. Fully understand the differences at the catalytic site of the two cyclooxygenase (COX) isoforms is still opened to investigation. Thus, two series of novel compounds were designed and synthesized in fair to good yields using the highly selective COX-1 inhibitor mofezolac as the lead compound to explore a COX-1 zone formed by the polar residues Q192, S353, H90 and Y355, as well as hydrophobic amino acids I523, F518 and L352. According to the structure of the COX-1:mofezolac complex, hydrophobic amino acids appear to have free volume eventually accessible to the more sterically hindering groups than the methoxy linked to the phenyl groups of mofezolac, in particular the methoxyphenyl at C4-mofezolac isoxazole. Mofezolac bears two methoxyphenyl groups linked to C3 and C4 of the isoxazole core ring. Thus, in the novel compounds, one or both methoxy groups were replaced by the higher homologous ethoxy, normal and isopropyl, normal and tertiary butyl, and phenyl and benzyl. Furthermore, a major difference between the two sets of compounds is the presence of either a methyl or acetic moiety at the C5 of the isoxazole. Among the C5-methyl series, 12 (direct precursor of mofezolac) (COX-1 IC50 = 0.076 μM and COX-2 IC50 = 0.35 μM) and 15a (ethoxy replacing the two methoxy groups in 12; COX-1 IC50 = 0.23 μM and COX-2 IC50 > 50 μM) were still active and with a Selectivity Index (SI = COX-2 IC50/COX-1 IC50) = 5 and 217, respectively. The other symmetrically substituted alkoxyphenyl moietis were inactive at 50 μM final concentration. Among the asymmetrically substituted, only the 16a (methoxyphenyl on C3-isoxazole and ethoxyphenyl on C4-isoxazole) and 16b (methoxyphenyl on C3-isoxazole and n-propoxyphenyl on C4-isoxazole) were active with SI = 1087 and 38, respectively. Among the set of compounds with the acetic moiety, structurally more similar to mofezolac (SI = 6329), SI ranged between 1.4 and 943. It is noteworthy that 17b (n-propoxyphenyl on both C3- and C4-isoxazole) were found to be a COX-2 slightly selective inhibitor with SI = 0.072 (COX-1 IC50 > 50 μM and COX-2 IC50 = 3.6 μM). Platelet aggregation induced by arachidonic acid (AA) can be in vitro suppressed by the synthesized compounds, without affecting of the secondary hemostasia, confirming the biological effect provided by the selective inhibition of COX-1. A positive profile of hemocompatibility in relation to erythrocyte and platelet toxicity was observed. Additionally, these compounds exhibited a positive profile of hemocompatibility and reduced cytotoxicity. Quantitative structure activity relationship (QSAR) models and molecular modelling (Ligand and Structure based virtual screening procedures) provide key information on the physicochemical and pharmacokinetic properties of the COX-1 inhibitors as well as new insights into the mechanisms of inhibition that will be used to guide the development of more effective and selective compounds. X-ray analysis was used to confirm the chemical structure of 14 (MSA17).

First report on the QSAR modelling and multistep virtual screening of the inhibitors of nonstructural protein Nsp14 of SARS-CoV-2: Reducing unnecessary chemical synthesis and experimental tests

Corona Virus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a serious threat to human health and life safety. How to effectively prevent and treat COVID-19 is crucial. In this study, we used the inhibitors of nonstructural protein Nsp14 of SARS-CoV-2 to perform the quantitative structure activity relationship (QSAR) modelling for the first time. Based on different dataset division strategies, we selected partial least square (PLS) and multiple linear regression (MLR) methods to develop easily interpretable and reproducible QSAR models with 2D molecular descriptors. All models complied with the strict QSAR validation principles of OECD and internationally recognized validation metrics. The best model contained two molecular descriptors with the following statistical parameters: R2 = 0.7796, QLOO2= 0.7373, Rtest2 = 0.8539 and CCCtest = 0.9073. Obviously, the model exhibited good prediction performance and can be used for quickly predicting the inhibitory activity of unknown compounds against Nsp14. Mechanistic interpretation identified the detailed relationship between molecular structure information and inhibitory activity. The best QSAR model was used to predict the inhibitory activity of 263 true external compounds without experimental values against Nsp14, and the prediction reliability was analyzed and discussed. Molecular docking and ADMET analyses were conducted for compounds with higher similarity to the modelling compounds. Finally, two compounds were identified as potential candidate drugs of targeting Nsp14. The current work lays a solid theoretical foundation for the discovery of inhibitors targeting Nsp14, and has an important reference significance for the development of anti-COVID-19 drugs.

A machine learning-based QSAR model reveals important molecular features for understanding the potential inhibition mechanism of ionic liquids to acetylcholinesterase

The broad application of ionic liquids (ILs) has been hindered by uncertainties surrounding their ecotoxicity. In this work, a Quantitative Structure-Activity Relationship (QSAR) model was devised to predict the inhibition of ILs towards the activity of AChE, employing both Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) machine learning approaches. Fourteen kings of essential molecular feature descriptors were screened from an initial roster of 244 descriptors through the application of a feature importance index and they showed a significant impact on the activity of AChE activity. The two models based solely on the 14 most critical molecular descriptors could maintain model's robustness and reliability. The correlation analysis between these 14 descriptors and the inhibition of AChE activity revealed the potential impact of the molecular characteristics on ILs toxicity. The results underscored the main influence of cations in ILs on the inhibitory activity towards the AChE enzyme. Specifically, cations exhibiting hydrophobicity properties were found to exert more potent inhibitory effects on the AChE enzyme. In addition, some other properties of the cations, such as the degree of branching, atomic weight and partial charge also modulated their inhibition potential. This study enhances the comprehension of the structure-activity relationship between ILs and AChE inhibition, providing a reference for designing safer and greener ILs.

Aminopyrimidine Derivatives as Multiflavivirus Antiviral Compounds Identified from a Consensus Virtual Screening Approach.

Around three billion people are at risk of infection by the dengue virus (DENV) and potentially other flaviviruses. Worldwide outbreaks of DENV, Zika virus (ZIKV), and yellow fever virus (YFV), the lack of antiviral drugs, and limitations on vaccine usage emphasize the need for novel antiviral research. Here, we propose a consensus virtual screening approach to discover potential protease inhibitors (NS3pro) against different flavivirus. We employed an in silico combination of a hologram quantitative structure-activity relationship (HQSAR) model and molecular docking on characterized binding sites followed by molecular dynamics (MD) simulations, which filtered a data set of 7.6 million compounds to 2,775 hits. Lastly, docking and MD simulations selected six final potential NS3pro inhibitors with stable interactions along the simulations. Five compounds had their antiviral activity confirmed against ZIKV, YFV, DENV-2, and DENV-3 (ranging from 4.21 ± 0.14 to 37.51 ± 0.8 μM), displaying aggregator characteristics for enzymatic inhibition against ZIKV NS3pro (ranging from 28 ± 7 to 70 ± 7 μM). Taken together, the compounds identified in this approach may contribute to the design of promising candidates to treat different flavivirus infections.

Quantitative Predictive Studies of Multiple Biological Activities of TRPV1 Modulators.

TRPV1 channel agonists and antagonists, which have powerful analgesic effects without the addictive qualities associated with traditional analgesics, have become a focus area for the development of novel analgesics. In this study, quantitative structure-activity relationship (QSAR) models for three bioactive endpoints (Ki, IC50, and EC50) were successfully constructed using four machine learning algorithms: SVM, Bagging, GBDT, and XGBoost. These models were based on 2922 TRPV1 modulators and incorporated four types of molecular descriptors: Daylight, E-state, ECFP4, and MACCS. After the rigorous five-fold cross-validation and external test set validation, the optimal models for the three endpoints were obtained. For the Ki endpoint, the Bagging-ECFP4 model had a Q2 value of 0.778 and an R2 value of 0.780. For the IC50 endpoint, the XGBoost-ECFP4 model had a Q2 value of 0.806 and an R2 value of 0.784. For the EC50 endpoint, the SVM-Daylight model had a Q2 value of 0.784 and an R2 value of 0.809. These results demonstrate that the constructed models exhibit good predictive performance. In addition, based on the model feature importance analysis, the influence between substructure and biological activity was also explored, which can provide important theoretical guidance for the efficient virtual screening and structural optimization of novel TRPV1 analgesics. And subsequent studies on novel TRPV1 modulators will be based on the feature substructures of the three endpoints.

Modeling the Blood-Brain Barrier Permeability of Potential Heterocyclic Drugs via Biomimetic IAM Chromatography Technique Combined with QSAR Methodology.

Penetration through the blood-brain barrier (BBB) is desirable in the case of potential pharmaceuticals acting on the central nervous system (CNS), but is undesirable in the case of drug candidates acting on the peripheral nervous system because it may cause CNS side effects. Therefore, modeling of the permeability across the blood-brain barrier (i.e., the logarithm of the brain to blood concentration ratio, log BB) of potential pharmaceuticals should be performed as early as possible in the preclinical phase of drug development. Biomimetic chromatography with immobilized artificial membrane (IAM) and the quantitative structure-activity relationship (QSAR) methodology were successful in modeling the blood-brain barrier permeability of 126 drug candidates, whose experimentally-derived lipophilicity indices and computationally-derived molecular descriptors (such as molecular weight (MW), number of rotatable bonds (NRB), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), topological polar surface area (TPSA), and polarizability (α)) varied by class. The QSARs model established by multiple linear regression showed a positive effect of the lipophilicity (log kw, IAM) and molecular weight of the compound, and a negative effect of the number of hydrogen bond donors and acceptors, on the log BB values. The model has been cross-validated, and all statistics indicate that it is very good and has high predictive ability. The simplicity of the developed model, and its usefulness in screening studies of novel drug candidates that are able to cross the BBB by passive diffusion, are emphasized.

Control list of high-priority chemicals based on 5-HT-RI functionality and the human health interference effects selective CNN-GRU deep learning model

The antidepressant drug known as 5-HT reuptake inhibitor (5-HT-RI) was commonly detected in biological tissues and result in significant adverse health effects. Homology modeling was used to characterize the functionalities (efficacy and resistance), and the adverse outcome pathway was used to characterize its human health interferences (olfactory toxicity, neurotoxicity, and gut microbial interference). The convolutional neural network coupled with the gated recurrent unit (CNN-GRU) deep learning method was used to construct a comprehensive model of 5-HT-RI functionality and human health interference effects selectivity with small sample data. The architecture with 2 SE, 320 neuronal nodes and 6-folds cross-validation showed the best applicability. The results showed that the confidence interval of the constructed model reached 90 % indicating that the model had reliable prediction ability and generalization ability. Based on the CNN-GRU deep learning model, seven high-priority chemicals with a weak comprehensive effect, including D-VEN, (1R,4S)-SER, S-FLX, CTP, S-CTP, NEF, and VEN, were screened. Based on the molecular three-dimensional structure information, a comprehensive-effect three-dimensional quantitative structure-activity relationship (3D-QSAR) model was constructed to confirm the reliability of the constructed control list of 5-HT-RI high-priority chemicals. Analysis with the ranking of calculated values based on the molecular dynamics method and predicted values based on the CNN-GRU deep learning model, we found that the consistency of the three methods was above 85 %. Additionally, by analyzing the sensitivity, molecular electrostatic potential, polar surface area of the comprehensive-effect CNN-GRU deep learning model, and the electrostatic field of the 3D-QSAR models, we found that the significant effects of five key characteristics (DM, Qyy, Qxz, I, and BP), molecular electronegativity, and polarity significantly affected the high-priority degree of 5-HT-RI. In this study, we provided reasonable and reliable prediction tools and discussed theoretical methods for the risk assessment of functionality and human health interference of emerging pollutants such as 5-HT-RI.

QSAR studies of quinoline alkaloids camptothecin derivatives for prediction anticancer activity using linear and nonlinear methods

AbstractQuinoline alkaloid camptothecin (CPT) derivatives are compounds with a wide range of inhibitory activities and they are the basis of several groups of drugs. CPT is one of the prominent lead compounds in anticancer drug development. It inhibits DNA topoisomerase I (topo I) enzyme and has shown remarkable anticancer activity against lung, colon, rectum, ovarian, breast, and stomach cancers. Quantitative structure–activity relationship (QSAR) is basically a statistical approach correlating the response activity data with descriptors encoding chemical information. In the current research, a QSAR study was performed for modeling and predicting the anticancer activity (pIC50) of 76 CPT derivatives as an inhibitor of DNA topo I using the genetic algorithm multiple linear regression (GA‐MLR) and back propagation artificial neural network (BP‐ANN) methods. The results of this study indicate that the use of constitutional and geometrical descriptors provide good estimate for pIC50. The obtained results of statistical criteria, internal and external validation show the superiority of BP‐ANN model than GA‐MLR to predict the pIC50 values of the investigated compounds. QSAR model developed in this study can be used for design and development of novel potent CPT derivatives and for predicting their anticancer activity.

In silico study for discovering novel thiazole derivatives as anti-breast cancer agents (MCF-7)

The work validated the pharmacokinetic characteristics and docking approach, and discovered novel thiazole compounds with MCF-7 breast anti-cancer efficacy using quantitative structure-activity relationships (QSAR) models. Using an experimental dataset of 53 thiazole derivatives with IC50 values, QSAR models were developed using multivariate linear regression (QSARMLR) and artificial neural networks (QSARANN). Eight descriptions, with statistical values of R2train = 0.875 and Q2LOO = 0.834, comprised the successfully developed QSARMLR model. Based on the descriptors of the QSARMLR model, which has statistical values of R2 = 0.918, Q2test = 0.934, and Q2CV = 0.916, the QSARANN neural network model with the architectural network of I(8)-HL(9)-O(1) has also been constructed. Using the AD and outliers analysis, the models were utilized to forecast 120 new design derivatives based on the thiazole framework, and the IC50 values of 10 new thiazole derivatives were determined. Additionally, the derivatives were assessed for resistance to estrogen-positive breast cancer by docking them onto the Polo-like kinases (Plk1) receptor and screening them for pharmacokinetic features in accordance with Lipinski and Ghose guidelines. Consequently, it was discovered that the thiazole TAZ5 had promising derivative activity against the primary MCF-7 breast cancer cell line.

Exploring Citrus sinensis Phytochemicals as Potential Inhibitors for Breast Cancer Genes BRCA1 and BRCA2 Using Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Analysis.

Structure-activity relationship (SAR) is considered to be an effective in silico approach when discovering potential antagonists for breast cancer due to gene mutation. Major challenges are faced by conventional SAR in predicting novel antagonists due to the discovery of diverse antagonistic compounds. Methodologyand Results: In predicting breast cancer antagonists, a multistep screening of phytochemicals isolated from the seeds of the Citrus sinensis plant was applied using feasible complementary methodologies. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed through the Flare project, in which conformational analysis, pharmacophore generation, and compound alignment were done. Ten hit compounds were obtained through the development of the 3D-QSAR model. For exploring the mechanism of action of active compounds against cocrystal inhibitors, molecular docking analysis was done through Molegro software (MVD) to identify lead compounds. Three new proteins, namely, 1T15, 3EU7, and 1T29, displayed the best Moldock scores. The quality of the docking study was assessed by a molecular dynamics simulation. Based on binding affinities to the receptor in the docking studies, three lead compounds (stigmasterol P8, epoxybergamottin P28, and nobiletin P29) were obtained, and they passed through absorption, distribution, metabolism, and excretion (ADME) studies via the SwissADME online service, which proved that P28 and P29 were the most active allosteric inhibitors with the lowest toxicity level against breast cancer. Then, density functional theory (DFT) studies were performed to measure the active compound's reactivity, hardness, and softness with the help of Gaussian 09 software. This multistep screening of phytochemicals revealed high-reliability antagonists of breast cancer by 3D-QSAR using flare, docking analysis, and DFT studies. The present study helps in providing a proper guideline for the development of novel inhibitors of BRCA1 and BRCA2.

Establishing short-term occupational exposure limits (STELs) for sensory irritants using predictive and in silico respiratory rate depression (RD50) models

Sensory irritation is a health endpoint that serves as the critical effect basis for many occupational exposure limits (OELs). Schaper 1993 described a significant relationship with high correlation between the measured exposure concentration producing a 50% respiratory rate decrease (RD50) in a standard rodent assay and the American Conference of Governmental Industrial Hygienists (ACGIH®) Threshold Limit Values (TLVs®) as time-weighted averages (TWAs) for airborne chemical irritants. The results demonstrated the potential use of the RD50 values for deriving full-shift TWA OELs protective of irritant responses. However, there remains a need to develop a similar predictive model for deriving workplace short-term exposure limits (STELs) for sensory irritants. The aim of our study was to establish a model capable of correlating the relationship between RD50 values and published STELs to prospectively derive short-term exposure OELs for sensory irritants. A National Toxicology Program (NTP) database that included chemicals with both an RD50 and established STELs was used to fit several linear regression models. A strong correlation between RD50s and STELs was identified, with a predictive equation of ln (STEL) (ppm) = 0.86 * ln (RD50) (ppm) − 2.42 and an R2 value of 0.75. This model supports the use of RD50s to derive STELs for chemicals without existing exposure recommendations. Further, for data-poor sensory irritants, predicted RD50 values from in silico quantitative structure activity relationship (QSAR) models can be used to derive STELs. Hence, in silico methods and statistical modeling can present a path forward for establishing reliable OELs and improving worker safety and health.

QSAR models for the ozonation of diverse volatile organic compounds at different temperatures.

In order to assess the fate and persistence of volatile organic compounds (VOCs) in the atmosphere, it is necessary to determine their oxidation rate constants for their reaction with ozone (kO3). However, given that experimental values of kO3 are only available for a few hundred compounds and their determination is expensive and time-consuming, developing predictive models for kO3 is of great importance. Thus, this study aimed to develop reliable quantitative structure-activity relationship (QSAR) models for 302 values of 149 VOCs across a broad temperature range (178-409 K). The model was constructed based on the combination of a simplified molecular-input line-entry system (SMILES) and temperature as an experimental condition, namely quasi-SMILES. In this study, temperature was incorporated in the models as an independent feature. The hybrid optimal descriptor generated from the combination of quasi-SMILES and HFG (hydrogen-filled graph) was used to develop reliable, accurate, and predictive QSAR models employing the CORAL software. The balance between the correlation method and four different target functions (target function without considering IIC or CII, target function using each IIC or CII, and target function based on the combination of IIC and CII) was used to improve the predictability of the QSAR models. The performance of the developed models based on different target functions was compared. The correlation intensity index (CII) significantly enhanced the predictability of the model. The best model was selected based on the numerical value of Rm2 of the calibration set (split #1, Rtrain2 = 0.9834, Rcalibration2 = 0.9276, Rvalidation2 = 0.9136, and calibration = 0.8770). The promoters of increase/decrease for log kO3 were also computed based on the best model. The presence of a double bond (BOND10000000 and $10 000 000 000), absence of halogen (HALO00000000), and the nearest neighbor codes for carbon equal to 321 (NNC-C⋯321) are some significant promoters of endpoint increase.