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O6-methylguanine-DNA methyltransferase (MGMT) constitutes an important cellular mechanism for repairing potentially cytotoxic DNA damage induced by guanine O6-alkylating agents and can render cells highly resistant to certain cancer chemotherapeutic drugs. A wide variety of potential MGMT inactivators have been designed and synthesized for the purpose of overcoming MGMT-mediated tumor resistance. We determined the inactivation potency of these compounds against human recombinant MGMT using [3H]-methylated-DNA-based MGMT inactivation assays and calculated the IC50 values. Using the results of 370 compounds, we performed quantitative structure–activity relationship (QSAR) modeling to identify the correlation between the chemical structure and MGMT-inactivating ability. Modeling was based on subdividing the sorted pIC50 values or on chemical structures or was random. A total of nine molecular descriptors were presented in the model equation, in which the mechanistic interpretation indicated that the status of nitrogen atoms, aliphatic primary amino groups, the presence of O-S at topological distance 3, the presence of Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X, the ionization potential and hydrogen bond donors are the main factors responsible for inactivation ability. The final model was of high internal robustness, goodness of fit and prediction ability (R2pr = 0.7474, Q2Fn = 0.7375–0.7437, CCCpr = 0.8530). After the best splitting model was decided, we established the full model based on the entire set of compounds using the same descriptor combination. We also used a similarity-based read-across technique to further improve the external predictive ability of the model (R2pr = 0.7528, Q2Fn = 0.7387–0.7449, CCCpr = 0.8560). The prediction quality of 66 true external compounds was checked using the "Prediction Reliability Indicator" tool. In summary, we defined key structural features associated with MGMT inactivation, thus allowing for the design of MGMT inactivators that might improve clinical outcomes in cancer treatment. [ABSTRACT FROM AUTHOR]

[Display omitted] • The QSAR model for the inhibitors of SARS-CoV-2 Nsp14 was built for the first time. • Mechanistic analysis identified the main influencing factors for Nsp14 inhibition. • The best model was used for virtual screening of 262 untested compounds. • Docking and ADMET predictions identified two hit candidates as Nsp14 inhibitors. • The developed QSAR model can avoid unnecessary chemical synthesis and test. 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: R 2 = 0.7796, Q LOO 2 = 0.7373, R test 2 = 0.8539 and CCC test = 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. [ABSTRACT FROM AUTHOR]

N-(2-chloroethyl)- N′-cyclohexyl- N-nitrosoureas (CCNU) is an important alkylating agent used in the clinical treatment of cancer. The quantitative structure-activity relationship (QSAR) of CCNU derivatives was investigated using the density functional theory (DFT)-based descriptors and the n-octanol/water partition coefficient (milog P). Geometry optimization was performed using the DFT/B3LYP method in conjunction with the 6-311+G(d,p) basis set. Experimental data of anticancer activity, log(1/ C), were used for the QSAR analysis. By stepwise multiple regressions, four optimum descriptors, (milog P)2, E1, EZ/E and BO1Cl8 were found for constructing the QSAR models. Two satisfied models were obtained by multiple linear regressions with the values of R2 higher than 0.9. The (milog P)2 descriptor has the highest correlation to the anticancer activity, indicating that similar improvements to hydrophilicity and lipophilicity are necessary for enhancing anticancer activity. The energy barriers for the decomposition of CCNU derivatives via a retro-ene reaction ( E1) and for the transformation from Z-tautomers to E-tautomers ( EZ/E) are also considerable descriptors in the QSAR models. The anticancer activity is increased with the decrease of E1 and the increase of EZ/E. The BO1Cl8 descriptor, which requires the inclusion of the other three descriptors in the models, has positive correlation with the anticancer activity of CCNU derivatives. The results indicate that the introduction of the descriptors of activation energies ( E1 and EZ/E) is a significant contribution to the methodology of QSAR investigations, because the dynamic descriptors may be more correlated with the biological activity of drugs and toxicants than the static descriptors. Our models shed light on the structure-activity relationship of CCNU derivatives and may be useful in the development of more effective and less toxic nitrosoureas as anticancer agents. © 2011 Wiley Periodicals, Inc. Int J Quantum Chem, 2011 [ABSTRACT FROM AUTHOR]

The information of the acute oral toxicity for most polycyclic aromatic hydrocarbons (PAHs) in mammals are lacking due to limited experimental resources, leading to a need to develop reliable in silico methods to evaluate the toxicity endpoint. In this study, we developed the quantitative structure-activity relationship (QSAR) models by genetic algorithm (GA) and multiple linear regression (MLR) for the rat acute oral toxicity (LD 50) of PAHs following the strict validation principles of QSAR modeling recommended by OECD. The best QSAR model comprised eight simple 2D descriptors with definite physicochemical meaning, which showed that maximum atom-type electrotopological state, van der Waals surface area, mean atomic van der Waals volume, and total number of bonds are main influencing factors for the toxicity endpoint. A true external set (554 compounds) without rat acute oral toxicity values, and 22 limit test compounds, were firstly predicted along with reliability assessment. We also compared our proposed model with the OPERA predictions and recently published literature to prove the prediction reliability. Furthermore, the interspecies toxicity (iST) models of PAHs between rat and mouse were also established, validated and employed for filling data gap. Overall, our developed models should be applicable to new or untested or not yet synthesized PAHs falling within the applicability domain (AD) of the models for rapid acute oral toxicity prediction, thus being important for environmental or personal exposure risk assessment under regulatory frameworks. [Display omitted] • A highly robust QSAR model for the rat acute oral toxicity of PAHs was developed. • Lipophilicity contributes to the PAHs toxicity while polarity reduces the toxicity. • Two reliable interspecies toxicity (iST) models between rat and mouse were built. • QSAR and iST models were firstly successfully applied to true external sets. • The proposed models can be used for toxicity data filling and risk assessment. [ABSTRACT FROM AUTHOR]

Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure–activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints. [ABSTRACT FROM AUTHOR]

Nitroaromatic compounds (NACs) are an important type of environmental organic pollutants. However, it is lack of sufficient information relating to their potential adverse effects on human health and the environment due to the limited resources. Thus, using in silico technologies to assess their potential hazardous effects is urgent and promising. In this study, quantitative structure activity relationship (QSAR) and classification models were constructed using a set of NACs based on their mutagenicity against Salmonella typhimurium TA100 strain. For QSAR studies, DRAGON descriptors together with quantum chemistry descriptors were calculated for characterizing the detailed molecular information. Based on genetic algorithm (GA) and multiple linear regression (MLR) analyses, we screened descriptors and developed QSAR models. For classification studies, seven machine learning methods along with six molecular fingerprints were applied to develop qualitative classification models. The goodness of fitting, reliability, robustness and predictive performance of all developed models were measured by rigorous statistical validation criteria, then the best QSAR and classification models were chosen. Moreover, the QSAR models with quantum chemistry descriptors were compared to that without quantum chemistry descriptors and previously reported models. Notably, we also obtained some specific molecular properties or privileged substructures responsible for the high mutagenicity of NACs. Overall, the developed QSAR and classification models can be utilized as potential tools for rapidly predicting the mutagenicity of new or untested NACs for environmental hazard assessment and regulatory purposes, and may provide insights into the in vivo toxicity mechanisms of NACs and related compounds. Image 1 • An excellent QSAR model was developed for the mutagenicity of nitroaromatics. • Classification models were built to classify the high mutagenic nitroaromatics. • Specific molecular properties related to the high mutagenicity were obtained. • Privileged substructures provide better explanations for the high mutagenicity. • Our models can be used to rapidly predict the mutagenicity for hazard assessment. [ABSTRACT FROM AUTHOR]

O6-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O6-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer researchers because it can significantly improve the anticancer efficacy of such alkylating agents. In this study, we performed a quantitative structure activity relationship (QSAR) and classification study based on a total of 134 base analogs related to their ED50 values (50% inhibitory concentration) against MGMT. Molecular information of all compounds were described by quantum chemical descriptors and Dragon descriptors. Genetic algorithm (GA) and multiple linear regression (MLR) analysis were combined to develop QSAR models. Classification models were generated by seven machine-learning methods based on six types of molecular fingerprints. Performances of all developed models were assessed by internal and external validation techniques. The best QSAR model was obtained with Q2Loo = 0.83, R2 = 0.87, Q2ext = 0.67, and R2ext = 0.69 based on 84 compounds. The results from QSAR studies indicated topological charge indices, polarizability, ionization potential (IP), and number of primary aromatic amines are main contributors for MGMT inhibition of base analogs. For classification studies, the accuracies of 10-fold cross-validation ranged from 0.750 to 0.885 for top ten models. The range of accuracy for the external test set ranged from 0.800 to 0.880 except for PubChem-Tree model, suggesting a satisfactory predictive ability. Three models (Ext-SVM, Ext-Tree and Graph-RF) showed high and reliable predictive accuracy for both training and external test sets. In addition, several representative substructures for characterizing MGMT inhibitors were identified by information gain and substructure frequency analysis method. Our studies might be useful for further study to design and rapidly identify potential MGMT inhibitors. [ABSTRACT FROM AUTHOR]

To better understand the mechanism of in vivo toxicity of N-nitroso compounds (NNCs), the toxicity data of 80 NNCs related to their rat acute oral toxicity data (50% lethal dose concentration, LD50) were used to establish quantitative structure-activity relationship (QSAR) and classification models. Quantum chemistry methods calculated descriptors and Dragon descriptors were combined to describe the molecular information of all compounds. Genetic algorithm (GA) and multiple linear regression (MLR) analyses were combined to develop QSAR models. Fingerprints and machine learning methods were used to establish classification models. The quality and predictive performance of all established models were evaluated by internal and external validation techniques. The best GA-MLR-based QSAR model containing eight molecular descriptors was obtained with Q2loo = 0.7533, R2 = 0.8071, Q2ext = 0.7041 and R2ext = 0.7195. The results derived from QSAR studies showed that the acute oral toxicity of NNCs mainly depends on three factors, namely, the polarizability, the ionization potential (IP) and the presence/absence and frequency of C–O bond. For classification studies, the best model was obtained using the MACCS keys fingerprint combined with artificial neural network (ANN) algorithm. The classification models suggested that several representative substructures, including nitrile, hetero N nonbasic, alkylchloride and amine-containing fragments are main contributors for the high toxicity of NNCs. Overall, the developed QSAR and classification models of the rat acute oral toxicity of NNCs showed satisfying predictive abilities. The results provide an insight into the understanding of the toxicity mechanism of NNCs in vivo, which might be used for a preliminary assessment of NNCs toxicity to mammals. [ABSTRACT FROM AUTHOR]

This study utilized available oral acute toxicity data in Rat and Mouse for polychlorinated persistent organic pollutants (PC-POPs) to construct data fusion-driven machine learning (ML) global models. Based on atom-centered fragments (ACFs), the collected high-throughput data overcame the applicability limitations, enabling accurate toxicity prediction for a wide range of PC-POPs series compounds using only single models. The data variances in the Rat training and test sets were 1.52 and 1.34, respectively, while for the Mouse , the values were 1.48 and 1.36, respectively. Genetic algorithm (GA) was used to build multiple linear regression (MLR) models and pre-screen descriptors, addressing the "black-box" problem prevalent in ML and enhancing model interpretability. The best ML models for Rat and Mouse achieved approximately 90 % prediction reliability for over 100,000 true untested compounds. Ultimately, a warning list of highly toxic compounds for eight categories of polychlorinated atom-centered fragments (PCACFs) was generated based on the prediction results. The analysis of descriptors revealed that dioxin analogs generally exhibited higher toxicity, because the heteroatoms and ring systems increased structural complexity and formed larger conjugated systems, contributing to greater oral acute toxicity. The present study provides valuable insights for guiding the subsequent in vivo tests, environmental risk assessment and the improvement of global governance system of pollutants. [Display omitted] ● Atom-Centered Fragments (ACFs)-based screening of PC-POPs were firstly proposed. ● The first high-throughput oral acute toxicity prediction of over 100,000 PC-POPs. ● Interpretable ML and multi-algorithm fusion for computational toxicology modeling. ● Warning list of highly toxic PC-POPs was provided based on the predicted results. ● Detailed descriptor-based toxicity mechanism explanations were provided. [ABSTRACT FROM AUTHOR]

Per- and polyfluoroalkyl substances (PFASs) are widely used in modern industry, causing many adverse effects on both the environment and human health. In this study, for the first time, we followed OECD guidelines to systematically investigate the quantitative structure-activity relationship (QSAR) of the oral acute toxicity of PFASs to Rat and Mouse using simple 2D descriptors. The Read-Across similarity descriptors and 2D descriptors were also combined to develop the quantitative read-across structure-activity relationship (q-RASAR) models. Interspecies toxicity (iST) correlation was also explored between the two rodent species. All developed QSAR, q-RASAR and iST models met the state-of-the-art validation criteria and were applied for toxicity predictions of hundreds of untested PFASs in true external sets. Subsequently, we performed the priority ranking of the untested PFASs based on the model predictions, with the mechanistic interpretation of the top 20 most toxic PFASs predicted by both QSAR and q-RASAR models. The two univariate iST models were also used for filling the interspecies toxicity data gap. Overall, the developed QSAR, q-RASAR and iST models can be used as effective tools for predicting the oral acute toxicity of untested PFASs to Rat and Mouse , thus being important for risk assessment of PFASs in ecological environment. [Display omitted] • QSAR and q-RASAR models for PFASs toxicity to Rat and Mouse were built first time. • The correlation between descriptors and toxicity was interpreted in detail. • Interspecies toxicity modelling was firstly explored for PFASs between Rat and Mouse. • All models were applied to predict true PFASs for the first time. • Untested PFASs with top 20 predicted toxicity by both models were ranked and analyzed. [ABSTRACT FROM AUTHOR]

The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC 50) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R 2 > 0.6 and Q LOO 2 > 0.6) and external predictivity (R test 2 > 0.7, Q Fn 2 >0.7, and CCC test > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles. [Display omitted] • Developmental toxicity prediction models of (bio)pesticides/animal drugs were built. • The mechanistic link between descriptors and toxicity was analyzed in detail. • q-RASAR technology further enhanced the predictive performance of QSAR model. • The dual models were applied to predict untested (bio)pesticides/animal drugs. • A top 10 priority list of unknown (bio)pesticides/animal drugs were provided. [ABSTRACT FROM AUTHOR]

• QSAR models of the acute toxicity of FNFPAHs to two aquatic organisms were built. • Detailed relationship between descriptors and aquatic toxicity was analysed. • The proposed models were firstly applied to predict untested pesticides from PPDB. • Consensus models were developed to further improve the prediction accuracy. • The proposed models can be used for environmental risk assessment of FNFPAHs. Fused and non-fused polycyclic aromatic hydrocarbons (FNFPAHs) are a type of organic compounds widely occurring in the environment that pose a potential hazard to ecosystem and public health, and thus receive extensive attention from various regulatory agencies. Here, quantitative structure-activity relationship (QSAR) models were constructed to model the ecotoxicity of FNFPAHs against two aquatic species, Daphnia magna and Oncorhynchus mykiss. According to the stringent OECD guidelines, we used genetic algorithm (GA) plus multiple linear regression (MLR) approach to establish QSAR models of the two aquatic toxicity endpoints: D. magna (48 h LC 50) and O. mykiss (96 h LC 50). The models were established using simple 2D descriptors with explicit physicochemical significance and evaluated using various internal/external validation metrics. The results clearly show that both models are statistically robust (Q L O O 2 = 0.7834 for D. magna and Q L O O 2 = 0.8162 for O. mykiss), have good internal fitness (R 2 = 0.8159 for D. magna and R 2 = 0.8626 for O. mykiss and external predictive ability (D. magna : R t e s t 2 = 0.8259, Q F n 2 = 0.7640∼0.8140, C C C t e s t = 0.8972; O. mykiss : R t e s t 2 = 0.8077, Q F n 2 = 0.7615∼0.7722, C C C t e s t = 0.8910). To prove the predictive performance of the developed models, an additional comparison with the standard ECOSAR tool obviously shows that our models have lower RMSE values. Subsequently, we utilized the best models to predict the true external set compounds collected from the PPDB database to further fill the toxicity data gap. In addition, consensus models (CMs) that integrate all validated individual models (IMs) were more externally predictive than IMs, of which CM2 has the best prediction performance towards the two aquatic species. Overall, the models presented here could be used to evaluate unknown FNFPAHs inside the domain of applicability (AD), thus being very important for environmental risk assessment under current regulatory frameworks. [Display omitted] [ABSTRACT FROM AUTHOR]