QSAR model for pesticide toxicity in bioluminescent Vibrio qinghaiensis sp.-Q67.

Hydrophobicity, as measured by Log P, is an important molecular property in assessing toxicity and carcinogenicity of disinfection by-products (DBPs). With increasing public health concerns of DPBs, there are considerable benefits in developing Quantitative Structure Activity Relationship (QSAR) models, capable of accurately predicting Log P, which could be used in health risk assessment of DBPs. In this research, Log P values of 46 halogenated alkanes, as one class of DBP compounds, were used to develop QSAR models for this class. Three molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) were used in Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated according to the principles set up by the Organization for Economic Co-operation and Development (OECD). Model Applicability Domain (AD) of the developed QSAR models was defined and mechanisms were interpreted. Considering large number of halogenated alkane compounds, the established QSAR models performed very well in terms of goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models have correlation coefficient R2 from 81 % to 98 % with the observed Log P. The leverage approach by Williams plot was applied to detect and remove outliers. As a result, the correlation coefficient, R2, of the QSAR models increased by approximately 2 % to 13 %, before and after removing the outliers, respectively. The developed QSAR model was statistically validated for its predictive power of Log P by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods.

The present study deals with the development of an artificial neural network based quantitative structure activity relationship (QSAR) model for virtual screening of active compounds which contain androstenedione carbonskeleton or their similar skeleton at the core. An empirical data modeling (with fitted data mapping) has been performed on the basis of bioassay record for human breast cancer cell line MCF7. The whole experimental data set was considered as test set. Standard feed-forward back-propagation neural network technique was applied to build the model. Leave-One- Out (LOO) cross-validation was performed to evaluate the performance of the model. The mapped model became the basis for selection best mapped compounds followed by development of Pharmacophore specific secondary QSAR model. In the present study, two best mapped molecules '4beta-hydroxy Withanolide-E' and '7, 8-Dehydrocalotropin' were used for development of the secondary QSAR model. These secondary-QSAR models were resulted with R2 LOOCV value 0.9845 and 0.9666 respectively. Docking studies, in silico phamacokinetic and toxicity analysis was also done for selected compounds. The screened compounds CID_73621, CID_16757497, CID_301751, CID_390666 and CID_46830222 were found with promising binding affinity value with aromatase with reference to the co-crystallized control compound androstenedione. Due to excellent extent of variance coverage in ANN based QSAR map model, it can be used as a robust non-linear QSAR model for androstenedione carbon-skeleton containing molecules and the protocol can be used to derive secondary QSAR models for other compounds set.

We have developed a quantitative structure-activity relationship (QSAR) model for predicting the larvicidal activity of 60 plant-derived molecules against Aedes aegypti L. (Diptera: Culicidae), a vector of several diseases such as dengue, yellow fever, chikungunya and Zika. The balanced subsets method (BSM) based on k-means cluster analysis (k-MCA) was employed to split the data set. The replacement method (RM) variable subset selection technique coupled with multivariable linear regression (MLR) proved to be successful for exploring 18 326 molecular descriptors and fingerprints calculated with PaDEL, Mold2 and EPI Suite open-source softwares. A robust QSAR model (Rtrain2=0.84, Strain = 0.20 and Rtest2=0.92, Stest = 0.23) involving five non-conformational descriptors was established. The model was validated and tested through the use of an external test set of compounds, the leave-one-out (LOO) and leave-more-out (LMO) cross-validation methods, Y-randomization and applicability domain (AD) analysis. The QSAR model surpasses previously published models based on geometrical descriptors, thereby representing a suitable tool for predicting larvicidal activity against the vector A. aegypti using a conformation-independent approach. © 2018 Society of Chemical Industry.

In high-dimensional quantitative structure–activity relationship (QSAR) modelling, penalization methods have been a popular choice to simultaneously address molecular descriptor selection and QSAR model estimation. In this study, a penalized linear regression model with L1/2-norm is proposed. Furthermore, the local linear approximation algorithm is utilized to avoid the non-convexity of the proposed method. The potential applicability of the proposed method is tested on several benchmark data sets. Compared with other commonly used penalized methods, the proposed method can not only obtain the best predictive ability, but also provide an easily interpretable QSAR model. In addition, it is noteworthy that the results obtained in terms of applicability domain and Y-randomization test provide an efficient and a robust QSAR model. It is evident from the results that the proposed method may possibly be a promising penalized method in the field of computational chemistry research, especially when the number of molecular descriptors exceeds the number of compounds.

Quantitative structure-activity relationship modeling of the toxicity of organothiophosphate pesticides to Daphnia magna and Cyprinus carpio

Assessing the ecotoxicity of pharmaceuticals is of urgent need due to the recognition of their possible adverse effects on nontarget organisms in the aquatic environment. The reality of ecotoxicity data scarcity promotes the development and application of quantitative structure activity relationship (QSAR) models. In the present study, we aimed to clarify whether a QSAR model of ecotoxicity specifically for pharmaceuticals is needed considering that pharmaceuticals are a class of chemicals with complex structures, multiple functional groups, and reactive properties. To this end, we conducted a performance comparison of two previously developed and validated QSAR models specifically for pharmaceuticals with the commonly used narcosis toxicity prediction model, i.e., Ecological Structure Activity Relationship (ECOSAR), using a subset of pharmaceuticals produced in China that had not been included in the training datasets of QSAR models under consideration. A variety of statistical measures demonstrated that the pharmaceutical specific model outperformed ECOSAR, indicating the necessity of developing a specific QSAR model of ecotoxicity for the active pharmaceutical contaminants. ECOSAR, which was generally used to predict the baseline or the minimum toxicity of a compound, generally underestimated the ecotoxicity of the analyzed pharmaceuticals. This could possibly be because some pharmaceuticals can react through specific modes of action. Nonetheless, it should be noted that 95% prediction intervals spread over approximately four orders of magnitude for both tested QSAR models specifically for pharmaceuticals.

Ecotoxicological QSAR modeling of organic compounds against fish: Application of fragment based descriptors in feature analysis

Red tides that occur off coasts have become a worldwide phenomenon over the past decades. In order to mitigate the damage of the red tides on the aquatic ecosystems, it is crucial to develop a method for predicting algicidal activities that requires less labor and time, and most importantly, this method can quickly screen potential algicides to control red tides. In this study, we have investigated the algicidal activity of 19 natural flavonoids against a typical red tide alga, Phaeocystis globosa. Our results indicate that after 5 days of flavonoid exposure, the half inhibition concentrations (IC50) ranged from 0.068 to 3.065 mg L-1, which showed the strong algicidal activities of the flavonoids. Furthermore, quantitative structure activity relationship (QSAR) model has been carried out between negative scale logarithm (pIC50) of the flavonoids and the corresponding molecular descriptors. The developed model was validated, both internally and externally, which displayed statistical robustness (R2 = 0.867, p = 0.0002, Q2LOO = 0.825, RMSEC = 0.182, Q2extF3 = 0.896, RMSEP = 0.161, CCC = 0.935). This indicates that the developed model was obtained successfully with satisfactory predictability and robustness for the future rapid screening of natural flavonoids with high inhibition activity on the red tide alga growth. Moreover, the main descriptors in the QSAR model were the molar refractivity, partition coefficient, lowest unoccupied molecular orbital, and highest occupied molecular orbital, illustrating that the molecular electro-chemical characteristics are significant in the algicidal actions of the flavonoids. Graphical abstract Red tides frequently occur worldwide and have become a global environment problem. Flavonoids showed great potential in allelopathic control of the excessive growth of red tide algae. In this study, the algicidal activity of 19 natural flavonoids was investigated on a typical red tide organism Phaeocystis globosa. Futhermore, we applied the quantitative structure-activity relationship (QSAR) model to the experimental data. The model between molecular descriptors of flavonoids and their antialgal activity displays statistical robustness, and 4 of 45 selected molecular descriptors were obtained by regression of training set. The numbers in the figure represent the half inhibition concentration (IC50) of flavonoids. Our results show that the algicidal activity of flavonoids is closely related to molar refraction, partition coefficient, lowest unoccupied molecular orbital, and highest occupied molecular orbital. The QSAR model can efficaciously predict the algicidal activity and provide insights into the inhibitory mechanisms of flavonoids.

Uncertainty quantification: Can we trust artificial intelligence in drug discovery?

A comprehensive safety evaluation of chemicals should require toxicity assessment in both the aquatic and terrestrial test species. Due to the application practices and nature of chemical pesticides, the avian toxicity testing is considered as an essential requirement in the risk assessment process. In this study, tree-based multispecies QSAR (quantitative-structure activity relationship) models were constructed for predicting the avian toxicity of pesticides using a set of nine descriptors derived directly from the chemical structures and following the OECD guidelines. Accordingly, the Bobwhite quail toxicity data was used to construct the QSAR models (SDT, DTF, DTB) and were externally validated using the toxicity data in four other test species (Mallard duck, Ring-necked pheasant, Japanese quail, House sparrow). Prior to the model development, the diversity in the chemical structures and end-point were verified. The external predictive power of the QSAR models was tested through rigorous validation deriving a wide series of statistical checks. Intercorrelation analysis and PCA methods provided information on the association of the molecular descriptors related to MW and topology. The S36 and MW were the most influential descriptors identified by DTF and DTB models. The DTF and DTB performed better than the SDT model and yielded a correlation (R(2)) of 0.945 and 0.966 between the measured and predicted toxicity values in test data array. Both these models also performed well in four other test species (R(2) > 0.918). ChemoTyper was used to identify the substructure alerts responsible for the avian toxicity. The results suggest for the appropriateness of the developed QSAR models to reliably predict the toxicity of pesticides in multiple avian test species and can be useful tools in screening the new chemical pesticides for regulatory purposes.

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.

Flavones, recognized as "privileged scaffolds" in drug discovery, hold significant promise as anticancer agents. This study develops a quantitative structure-activity relationship (QSAR) model to accelerate the optimization of lead compounds. Using pharmacophore modeling against different cancer targets, 89 flavone analogs with varied substitution patterns were designed and synthesized. Biological evaluation revealed promising candidates with enhanced cytotoxicity against breast cancer (MCF-7) and liver cancer (HepG2) cell lines, along with low toxicity toward normal Vero cells. A machine learning (ML)-driven QSAR approach was employed, comparing random forest (RF), extreme gradient boosting, and artificial neural network (ANN) models. The RF model exhibits superior performance, achieving R2 of 0.820 for (MCF-7 and 0.835 for HepG2, with cross-validation (R2cv) of 0.744 and 0.770, respectively. Validation using 27 test compounds yielded root mean square error test values of 0.573 (MCF-7) and 0.563 (HepG2). SHapley Additive exPlanations analysis highlighted key molecular descriptors influencing anticancer activity. This work presents a robust ML-driven QSAR model that supports the rational design of flavone derivatives and advances the development of selective, potent anticancer agents.

QSAR (Quantitative Structure Activity Relationship) modelling was performed on a dataset of 90 sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors. The quantitative and explicative evaluations revealed some of the subtle and distinguished structural features that are responsible for the inhibitory potency of these compounds against SGLT2, such as less possible number of ring carbons at 8 Å from the lipophilic atoms in the molecule (fringClipo8A) and more possible value for the sum of the partial charges of the lipophilic atoms present within seven bonds from the donor atoms (lipo_don_7Bc). Multivariate GA–MLR (genetic algorithm–multi linear regression) and thorough validation methodology out-turned a statistically robust QSAR model with a very high predictability shown from various statistical parameters. A QSAR model with r 2 = 0.83, F = 51.54, Q 2 LOO = 0.79, Q 2 LMO = 0.79, CCC cv = 0.88, Q 2Fn = 0.76–0.81, r 2 ext = 0.77, CCC ext = 0.85, and with RMSEtr < RMSEcv was proposed. This QSAR model will assist synthetic chemists in the development of the SGLT2 inhibitors as the antidiabetic leads.

The pharmacokinetic properties of absorption, distribution, metabolism and excretion (ADME) play a crucial role in drug discovery and development, since many drug candidates fail due to an inappropriate pharmacokinetic profile. Cytochrome P450 (CYP) enzymes are predominantly involved in Phase 1 metabolism of xenobiotics. Thus, it is important to better understand and prognosticate substrate binding and inhibition of CYP450.The goal of this study was to obtain QSAR (Quantitative Structure-Activity Relationship) models to identify substrates and inhibitors of CYP3A4. The data sets were collected and curated from online available databases and literature. Several QSAR models were obtained and validated according to the recommendations of the Organization for Economic Co-operation Development (OECD). The combination of different descriptors and machine learning methods led to robust and predictive QSAR models with high coverage. The interpretation of developed models was performed using the predicted probability maps (PPM). These maps help to encode major structural fragments to classify compounds as inhibitors or not inhibitors of CYP3A4. In conclusion, the obtained models can reliably identify substrates and non-substrates, and inhibitors and non-inhibitors of CYP3A4, which is very important &nbsp;in the early stages of the development of new drugs.

It is not always possible to build predictive Quantitative Structure-Activity Relationships (QSAR) models for a given chemical dataset. In this work, we propose several statistical criteria, which can with high confidence answer a question, whether it is possible to build a predictive model for a dataset prior to actual modeling, i.e. to establish, whether the dataset is modelable. Calculation of these criteria is fast, and using them in QSAR studies could dramatically reduce modelers’ time and efforts, as well as computational resources necessary to build QSAR models for at least some datasets, especially for those which are not modelable. The calculation of modelability criteria is based on the k-nearest neighbors approach. For all datasets, as modelability criteria we have proposed dataset diversity (MODI_DIV) and new activity cliff indices (MODI_ACI). For datasets with binary end points, as modelability criteria we have proposed the correct classification rate (MODI_CCR) CCR = 0.5(sensitivity + specificity) for leave-one-out (LOO) cross-validation in the entire descriptor space, and correct classification rate for similarity search (MODI_ssCCR) in the entire descriptor space with leave 20 %-out (five-fold) cross-validation. For binary datasets, all these modelability criteria were tested on 42 datasets with previously generated QSAR models. Two latter criteria (MODI_CCR and MODI_ssCCR) were found to have high correlation with the predictivity of QSAR models (QSAR_CCR) and were additionally tested on 60 ToxCast end points with QSAR modeling results published recently (Thomas RS, Black MB, Li L, Healy E, Chu T-M, Bao W, Andersen MD, Wolfinger RD. Toxicol Sci: Off JSoc Toxicol 128(2):398–417, 2012). These modelability criteria can be used to classify many datasets as modelable or non-modelable. These criteria can be generalized to datasets with compounds belonging to more than two categories or classes. Additionally, criteria which take into account errors of prediction MODI_CAT i and MODI_CLASS i were proposed for datasets with compounds belonging to more than two (i > 2) categories or classes and continuous end points, divided into i > 2 bins. For continuous end points, LOO cross-validation q 2 for similarity search with different numbers of nearest neighbors in the entire descriptor space (MODI_q 2), and similarity search coefficient of determination (MODI_ssR 2) in the entire descriptor space were proposed as modelability criteria. Our preliminary studies demonstrated high correlation between the external predictivity of QSAR models (QSAR_R 2) and each of the MODI_q 2 and MODI_ssR 2. On the other hand, for datasets with any binary or continuous response variable, MODI_DIVs and MODI_ACIs were found to be less useful to establish dataset modelability.