This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and ...environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.
Targeting cancer cells through drug-based treatment or combination therapy protocols involving chemical compounds can be challenging due to multiple factors, including their resistance to bioactive ...compounds and the potential of drugs to damage healthy cells. This study aims to investigate the relationship between the structure of novel sulfur-containing shikonin oxime compounds and the corresponding cytotoxicity against four cancer types, namely colon, gastric, liver, and breast cancers, through computational chemistry tools. This investigation is suggested to help build insights into how the structure of the compounds influences their activity and understand the mechanisms behind it and subsequently might be used in multi-cancer drug design process to propose novel optimized compounds that potentially exhibit the desired activity. The findings showed that the cytotoxic activity against the four cancer types was accurately predictable (R2 > 0.7, NRMSE <20%) by a combination of search and machine learning algorithms, based on the information on the structure of the compounds, including their lipophilicity, surface area, and volume. Overall, this study is supposed to play a crucial role in effective multi-cancer drug design in cancer research areas.
•The importance of combining 2D and 3D QSAR methods in providing more relevant information about compounds was highlighted.•The combination of machine learning and meta-heuristic search algorithms has been proven to be effective in QSAR modeling.•The cytotoxicity of shikonin oxime derivatives against colon, gastric, liver, and breast cancers was accurately predicted.•Ensemble methods, multiple trajectory search, and simulated annealing allowed for development of powerful QSAR models.
This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based ...QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods.
Antibiotics and triazole fungicides coexist in varying concentrations in natural aquatic environments, resulting in complex mixtures. These mixtures can potentially affect aquatic ecosystems. ...Accurately distinguishing synergistic and antagonistic mixtures and predicting mixture toxicity are crucial for effective mixture risk assessment. We tested the toxicities of 75 binary mixtures of antibiotics and fungicides against Auxenochlorella pyrenoidosa. Both regression and classification models for these mixtures were developed using machine learning models: random forest (RF), k-nearest neighbors (KNN), and kernel k-nearest neighbors (KKNN). The KKNN model emerged as the best regression model with high values of determination coefficient (R2 = 0.977), explained variance in prediction leave-one-out (Q2LOO = 0.894), and explained variance in external prediction (Q2F1 = 0.929, Q2F2 = 0.929, and Q2F3 = 0.923). The RF model, the leading classifier, exhibited high accuracy (accuracy = 1 for the training set and 0.905 for the test set) in distinguishing the synergistic and antagonistic mixtures. These results provide crucial value for the risk assessment of mixtures.
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•75 mixture toxicities of antibiotics and fungicides were test on green algae•Eight machine learning models were used for regression and classification tasks•The KKNN model stands out with strong internal and external predictability•RF model classified synergistic and antagonistic mixtures with accuracy of 100%
The Multidimensional quantitative structure-activity relationship (multidimensional-QSAR) method is one of the most popular computational methods employed to predict interesting biochemical ...properties of existing or hypothetical molecules. With continuous progress, the QSAR method has made remarkable success in various fields, such as medicinal chemistry, material science and predictive toxicology. Areas covered: In this review, the authors cover the basic elements of multidimensional -QSAR including model construction, validation and application. It includes and emphasizes the very recent developments of multidimensional -QSAR such as: HQSAR, G-QSAR, MIA-QSAR, multi-target QSAR. The advantages and disadvantages of each method are also discussed and typical examples of their application are detailed. Expert opinion: Although there are defects in multidimensional-QSAR modeling, it is still of enormous help to chemists, biologists and other researchers in various fields. In the authors' opinion, the latest more precise and feasible QSAR models should be further developed by integrating new descriptors, algorithms and other relevant computational techniques. Apart from being applied in traditional fields (e.g. lead optimization and predictive risk assessment), QSAR should be used more widely as a routine method in other emerging research fields including the modeling of nanoparticles(NPs), mixture toxicity and peptides.
Corrosion inhibitors play a crucial role in mitigating the detrimental effects of chemical and electrochemical interactions over time. This phenomenon poses significant threats to structural ...integrity, strength, and has far-reaching economic implications across various industries such as construction, petrochemicals, mining, fertilizer, and energy units. The environmental repercussions, including toxic outflows and plant failures, underscore the need for comprehensive assessments. Despite the commendable contributions of Iraqi researchers in developing organic, inorganic, natural, and nano-corrosion inhibitors, there is a critical gap in considering potential side effects. This study explores a pioneering approach to evaluating the toxicity of corrosion inhibitors on both human health and the environment. Leveraging mathematical modeling and mechanisms, we present a mimic estimation of environmental factors influencing corrosion phenomena. The out-lab experimental calculations employ Quantitative Structure-Activity Relationship (QSAR) techniques, providing a structural-based predictive model for assessing the potential impact of chemicals before embarking on experimental complexities. Drawing from Iraqi journals, seven corrosion inhibitors with diverse chemical structures, experimental conditions, and publishing sources were selected. These compounds were subjected to scrutiny using online prediction websites that evaluate Embro-toxicity, Cardio-toxicity, and crop-toxicity. Each inhibitor underwent screening by specific toxicological web servers. The findings revealed that all studied compounds posed moderate to extremely unsafe risks to fetal health during pregnancy, potentially categorizing them as teratogens with elevated risks of preterm labor, miscarriage, or stillbirth. Additionally, none of the tested materials exhibited herbicidal activity.
Bisphenols, estrogenic endocrine-disrupting chemicals, disrupt at least one of three endocrine pathways (estrogen, androgen, and thyroid). 17β-Hydroxysteroid dehydrogenase 1 (17β-HSD1) is a ...steroidogenic enzyme that catalyzes the activation of estradiol from estrone in human placenta and rat ovary. However, whether bisphenols inhibit 17β-HSD1 and the mode of action remains unclear. This study we screened 17 bisphenols for inhibiting human 17β-HSD1 in placental microsomes and rat 17β-HSD1 in ovarian microsomes and determined 3D-quantitative structure-activity relationship (3D-QSAR) and mode of action. We observed some bisphenols with substituents were found to significantly inhibit both human and rat 17β-HSD1 with the most potent inhibition on human enzyme by bisphenol H (IC50 = 0.90 μM) when compared to bisphenol A (IC50 = 113.38 μM). Rat enzyme was less sensitive to the inhibition of bisphenols than human enzyme with bisphenol H (IC50 = 32.94 μM) for rat enzyme. We observed an inverse correlation between IC50 and hydrophobicity (expressed as Log P). Docking analysis showed that they bound steroid-binding site of 17β-HSD1. The 3D-QSAR models demonstrated that hydrophobic region, hydrophobic aromatic, ring aromatic, and hydrogen bond acceptor are key factors for the inhibition of steroid synthesis activity of 17β-HSD1.
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•BPA analogues have been widely used but disrupt at least one of three endocrine pathways.•Bisphenols inhibit both human and rat 17β-HSD1. Rat enzyme was less sensitive than human enzyme.•Docking analysis showed that they bound steroid-binding site of 17β-HSD1.•3D-QSAR models demonstrated HBA, HY, RA, HYA are key factors.•An inverse correlation between IC50 and hydrophobicity (expressed as Log P).
The Monte Carlo technique has been used to build up quantitative structure–activity relationships (QSARs) for prediction of dark cytotoxicity and photo-induced cytotoxicity of metal oxide ...nanoparticles to bacteria Escherichia coli (minus logarithm of lethal concentration for 50% bacteria pLC50, LC50 in mol/L). The representation of nanoparticles include (i) in the case of the dark cytotoxicity a simplified molecular input-line entry system (SMILES), and (ii) in the case of photo-induced cytotoxicity a SMILES plus symbol ‘^’. The predictability of the approach is checked up with six random distributions of available data into the visible training and calibration sets, and invisible validation set. The statistical characteristics of these models are correlation coefficient 0.90–0.94 (training set) and 0.73–0.98 (validation set).
•Predictive model based on available eclectic data is suggested.•Cytotoxicity of metal oxide nanoparticles is examined as an endpoint.•Six random distributions into the training and validation sets are analyzed.•The statistical quality of all six models is good.•Calculations were carried out with the CORAL software available on the Int.
Matrix metalloproteinase-2 (MMP-2) is a potential target in anticancer drug discovery due to its association with angiogenesis, metastasis and tumour progression. In this study, 67 glutamic acid ...derivatives, synthesized and evaluated as MMP-2 inhibitors, were taken into account for multi-QSAR modelling study (regression-based 2D-QSAR, classification-based LDA-QSAR, Bayesian classification QSAR, HQSAR, 3D-QSAR CoMFA and CoMSIA as well as Open3DQSAR). All these QSAR studies were statistically validated individually. Regarding the 3D-QSAR analysis, the Open3DQSAR results were better than CoMFA and CoMSIA, although all these 3D-QSAR models supported each other. The importance of biphenylsulphonyl moiety over phenylacetyl/naphthylacetyl moieties was established due to its association with favourable steric and hydrophobic characters. HQSAR, LDA-QSAR and Bayesian classification QSAR studies also suggested that the biphenylsulphonamido group was better than the phenylacetylcarboxamido function. Additionally, glutamines were proven to be far better inhibitors than isoglutamines. Observations obtained from the current study were revalidated and supported by the earlier reported molecular modelling studies. Depending on these observations, newer glutamic acid-based compounds may be designed further in future for potent MMP-2 inhibitory activity.
•A potencial SSRI (18a) was designed rationally by applying both an artificial neural Network-based QSAR model and molecular docking.•Organic synthesis was achieved in high yields without loss of ...stereoselectivity.•Paroxetine exhibited hemolytic effects at 2.3, 1.29 y 0.67 mM, while 18a didn't cause hemolysis at any of the concentrations tested.
Depression is one of the most common mental illnesses, affecting almost 300 million people. According to the WHO, depression is one of the world's leading causes of disability and morbidity. People with this illness require both psychological and pharmaceutical treatment because severe depressive episodes often result in suicide. Selective serotonin reuptake inhibitors (SSRI) are widely used antidepressants that target the human serotonin transporter (hSERT). The crystallization of hSERT and the experimental data available allows cost and time-efficient computational tools like virtual screening (VS) to be utilized in the development of therapeutic agents. Here, we synthesized, characterized, and evaluated the biological activity of a novel SSRI analog of paroxetine, rationally designed by applying an artificial neural network-based QSAR model and a molecular docking analysis on hSERT. The analog N-substituted 18a showed higher affinity for the transporter (-10.2 kcal/mol), lower Ki value (1.19 nM) and a safer toxicological profile than paroxetine and was synthesized with a 71% yield. The in vitro cytotoxicity of the analog was evaluated using human glioblastoma (U87 MG), human neuroblastoma (SH SY5Y) and murine fibroblast (L929) cell lines. Also, the hemolytic ability of the compound was assessed on human erythrocytes. Results showed that analog 18a did not exhibit cytotoxic activity on the cell lines used and has no hemolytic activity at any of the concentrations tested, whereas with paroxetine, hemolysis was observed at 2.3, 1.29 y 0.67 mM. Based on these results, it is possible to suggest that analog 18a could be a promising new SSRI candidate for the treatment of this illness.
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