Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient

Synthesis and receptor dependent 4D-QSAR studies of 4,5-dihydro-1,3,4-oxadiazole derivatives targeting cannabinoid receptor

Cannabinoid receptor has been shown to be overexpressed in various types of cancers, especially non-small cell lung cancer. As a result, it could be used as novel target for anticancer treatments. Because receptor-dependent 4D-QSAR generates conformational ensemble profiles of compounds by molecular dynamics simulations at the binding site of the enzyme, this work describes the synthesis, biological activity evaluation and 4D-QSAR studies of 4,5-dihydro-1,3,4-oxadiazole derivatives targeting cannabinoid receptor. Compared with WIN55,212-2, compound 5 f showed the best antiproliferative activity. The receptor-dependent 4D-QSAR model was generated by multiple linear regression method using QSARINS. Leave-n-out cross-validation and chemical applicability domain were performed to analyse the independent test set and to verify the robustness of the model. The best 4D-QSAR model showed the following statistics: r² = 0.8487, Q²_LOO = 0.7667, Q²_LNO = 0.7524, and r²_Pred = 0.8358.

Molecular dynamics-guided receptor-dependent 4D-QSAR studies of HDACs inhibitors

Histone deacetylases (HDACs) were highlighted as a novel category of anticancer targets. Several HDACs inhibitors were approved for therapeutic use in cancer treatment. Comparatively, receptor-dependent 4D-QSAR, LQTA-QSAR, is a new approach which generates conformational ensemble profiles of compounds by molecular dynamics simulations at binding site of enzyme. This work describes a receptor-dependent 4D-QSAR studies on hydroxamate-based HDACs inhibitors. The 4D-QSAR model was generated by multiple linear regression method of QSARINS. Leave-N-out cross-validation (LNO) and Y-randomization were performed to analysis of the independent test set and to verify the robustness of the model. Best 4D-QSAR model showed the following statistics: R² = 0.8117, Q²_LOO = 0.6881, Q²_LNO = 0.6830, R²_Pred = 0.884. The results may be used for further virtual screening and design for novel HDACs inhibitors. The receptor dependent 4D-QSAR model was developed for the hydroxamate derivatives as HDAC inhibitors by making use of molecular dynamics simulation to obtain conformational ensemble profile for each compound. The multiple linear regression method was used to generate 4D-QSAR model with the suitable predictive ability and the excellent statistical parameters.

Evaluation of the OECD QSAR toolbox automatic workflow for the prediction of the acute toxicity of organic chemicals to fathead minnow

Regulatory frameworks require information on acute fish toxicity to ensure environmental protection. The experimental assessment of this property relies on a substantial number of fish to be tested and it is in conflict with the current drive to replace in vivo testing. For this reason, alternatives to in vivo testing have been proposed during the past years. Among these alternatives, there are Quantitative Structure-Activity Relationships (QSAR) that require the sole knowledge of chemical structure to yield predictions of toxicities. In this context, the OECD QSAR Toolbox is one of the leading QSAR tools for regulatory purposes that enables the prediction of fish toxicities. The aim of this work is to provide evidence about the predictive reliability of the automated workflow for predicting acute toxicity in fish which is embedded within this toolbox. The results herein presented show that the logic underpinning this automated workflow can predict with a reliability that, in the majority of cases, is comparable to inter-laboratory variability and, in a significant number of cases, is also comparable with intra-laboratory variability. Moreover, considerations on the toxic mode of action provided by the OECD tool proved to be helpful in refining predictions and reducing the number of prediction outliers.

Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability

Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.

Concentration-dependent adsorption of organic contaminants by graphene nanosheets: quantum-mechanical models

Adsorption is the key process in the expression of environmentally relevant physicochemical and toxicological properties of carbon nanomaterials. However, the adsorption of organic contaminants on to nanomaterials is a highly complex phenomenon, owing to the heterogeneity of adsorption sites, for example, on graphene surface as well as due to multiple factors operative during the adsorption, particularly, at the quantum-mechanical level. For predicting the concentration-dependent adsorption coefficients of organic contaminants by carbon nanomaterials, one option has been to rely on the existing linear-solvation energy relationship (LSER) models. The present work on the adsorption of aromatic and aliphatic organic contaminants by graphene nanosheets reveals that the existing LSER models are prone to failure when tested for internal and external validation using an external prediction set of compounds unknown to the model. As an alternative to the LSERs, the present work reports pure quantum-mechanical models developed using computational only quantum-mechanical descriptors. The reliability of the quantum-mechanical models was tested using state-of-the-art validation procedures employing an external prediction set of compounds. The proposed quantum-mechanical models reveal mean polarizability, zero-point vibrational energy, and its electron-correlation contribution to be the key descriptors in the prediction of adsorption coefficients of organic contaminants by graphene nanosheets.

The unreliability of the reliability criteria in the estimation of QSAR for skin sensitivity: A pun or a reliable law?

Some new products, which include common personal-care products, drugs, household items, can be hazardous in aspect personal care products/cosmetics and their ingredients (i.e. the above can effect human skin). International organizations (e.g. the Organisation for Economic Co-operation and Development-OECD) recommend evaluating individual ingredients when assessing the safety of personal care or cosmetic products. Thus, checking up that "popular at the market" substances are non-toxic, do not penetrate into or through normal or compromised human skin, and therefore, pose no risk to human health is an essential element of modern toxicology. The development of reliable models of toxicological endpoints is a tool to carry out the above checking up via quantitative structure-activity relationships (QSARs). The reliability of the QSAR is the current task of mathematical statistics. Recently, the index of ideality of correlation (IIC) and correlation intensity index (CII) were suggested as criteria of predictive potential (i.e. reliability) of QSAR-models. Here, the abilities of these criteria were studied for the case of building up models for skin sensitivity (LLNA, local lymph node assay). Computational experiments have confirmed that the IIC demonstrates an obvious ability to improve the predictive potential of models of skin sensitization. The applying of the CII for the case of skin sensitization also improves the quality of the model. However, the best models for skin sensitization were observed if the above-mentioned criteria are applied jointly (n = 268; R² = 0.60; RMSE = 0.63).

Cytotoxicity of quantum dots: Use of quasiSMILES in development of reliable models with index of ideality of correlation and the consensus modelling

The assessment of cytotoxicity of quantum dots is very essential for environmental and health risk analysis. In the present work we have modelled HeLa cell cytotoxicity of sixty one CdSe quantum dots with ZnS shell as a function of its experimental conditions and molecular construction using quasiSMILES representations. The index of ideality of correlation helps in the building of ten statistically significant models having good fitting ability with value of R² ranging from 0.8414 to 0.9609 for the training set. The split 5 model is rated as the best model with values of R², Q²_F1, Q²_F2 and Q²_F3 as 0.8964, 0.8267, 0.8264 and 0.8777 respectively for the calibration set. The extraction of features causing increase and decrease of cytotoxicity of quantum dots indicates importance of neutral surface charge, surface modified with protein, 72 h exposure time, combination of MTT assay with surface protein in decreasing the cytotoxicity. Amphiphilic polymer, polyol ligand with neutral charge, 0.5 - 0.6 nm quantum dot diameter with lipid ligand and unmodified positively charged surface are grouped in toxicity enhancer features. Further, consensus modelling using split 5 and 8 patterns enhances the prediction quality by increasing the R²_val to 0.9361 and 0.9656 respectively.

QSAR Assessing the Efficiency of Antioxidants in the Termination of Radical-Chain Oxidation Processes of Organic Compounds

Using the GUSAR 2013 program, the quantitative structure–antioxidant activity relationship has been studied for 74 phenols, aminophenols, aromatic amines and uracils having lgk₇ = 0.01–6.65 (where k₇ is the rate constant for the reaction of antioxidants with peroxyl radicals generated upon oxidation). Based on the atomic descriptors (Quantitative Neighborhood of Atoms (QNA) and Multilevel Neighborhoods of Atoms (MNA)) and molecular (topological length, topological volume and lipophilicity) descriptors, we have developed 9 statistically significant QSAR consensus models that demonstrate high accuracy in predicting the lgk₇ values for the compounds of training sets and appropriately predict lgk₇ for the test samples. Moderate predictive power of these models is demonstrated using metrics of two categories: (1) based on the determination coefficients R² (R²_TSi, R²₀, Q²(_F1), Q²(_F2), RmTSi2¯) and based on the concordance correlation coefficient (CCC)); or (2) based on the prediction lgk₇ errors (root mean square error (RMSEP), mean absolute error (MAE) and standard deviation (S.D.)) The RBF-SCR method has been used for selecting the descriptors. Our theoretical prognosis of the lgk₇ for 8-PPDA, a known antioxidant, based on the consensus models well agrees with the experimental value measure in the present work. Thus, the algorithms for calculating the descriptors implemented in the GUSAR 2013 program allow simulating kinetic parameters of the reactions underling the liquid-phase oxidation of hydrocarbons.

Chemoinformatics for the Safety of Energetic and Reactive Materials at Ineris

The characterization of physical hazards of substances is a key information to manage the risks associated to their use, storage and transport. With decades of work in this area, Ineris develops and implements cutting-edge experimental facilities allowing such characterizations at different scales and under various conditions to study all of the dreaded accident scenarios. This review presents the efforts engaged by Ineris more recently in the field of chemoinformatics to develop and use new predictive methods for the anticipation and management of industrials risks associated to energetic and reactive materials as a complement to experiments. An overview of the methods used for the development of Quantitative Structure-Property Relationships for physical hazards are presented and discussed regarding the specificities associated to this class of properties. A review of models developed at Ineris is also provided from the first tentative models on the explosivity of nitro compounds to the successful application to the flammability of organic mixtures. Then, a discussion is proposed on the use of QSPR models. Good practices for robust use for QSPR models are recalled with specific comments related to physical hazards, notably for regulatory purpose. Dissemination and training efforts engaged by Ineris are also presented. The potential offered by these predictive methods in terms of in silico design and for the development of new intrinsically safer technologies in safety-by-design strategies is finally discussed. At last, challenges and perspectives to extend the application of chemoinformatics in the field of safety and in particular for the physical hazards of energetic and reactive substances are proposed.

A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants

The Monte Carlo algorithm was applied to formulate a robust quantitative structure-property relationship (QSPR) model to compute the reactions rate constants of hydrated electron values for a data set of 309 water contaminants containing 125 aliphatic and 184 phenyl-based chemicals. The QSPR models were computed with the hybrid optimal descriptors which were procured by combining the SMILES and hydrogen-suppressed molecular graph for both classes of compounds. Approximately 75% of the total experimental data set was randomly divided into training and invisible training sets, while approximately 25% was divided into calibration and validation sets. The authenticity and robustness of the developed QSPR models were also judged by the Index of Ideality of Correlation. In QSPR modelling of aliphatic compounds, the numerical values of r T r a i n i n g 2 , r V a l i d a t i o n 2 , Q T r a i n i n g 2 and Q V a l i d a t i o n 2 were in the range of 0.852-0.905, 0.815-0.894, 0.839-0.897 and 0.737-0.867, respectively. Whereas, in the QSPR modelling of phenyl-based compounds, the numerical values of r T r a i n i n g 2 , r V a l i d a t i o n 2 , Q T r a i n i n g 2 and Q V a l i d a t i o n 2 were in the range of 0.867-0.896, 0.852-0.865, 0.816-0.850 and 0.760-0.762, respectively. The structural attributes, which are promoters of l o g K e a q - increase/decrease are also extracted from the SMILES notation for mechanistic interpretation. These QSPR models can also be applied to compute the reaction rate constants of organic contaminants.

In vivo toxicity of nitroaromatic compounds to rats: QSTR modelling and interspecies toxicity relationship with mouse

Nitroaromatic compounds (NACs) in the environment can cause serious public health and environmental problems due to their potential toxicity. This study established quantitative structure-toxicity relationship (QSTR) models for the acute oral toxicity of NACs towards rats following the stringent OECD principles for QSTR modelling. All models were assessed by various internationally accepted validation metrics and the OECD criteria. The best QSTR model contains seven simple and interpretable 2D descriptors with defined physicochemical meaning. Mechanistic interpretation indicated that van der Waals surface area, presence of C-F at topological distance 6, heteroatom content and frequency of C-N at topological distance 9 are main factors responsible for the toxicity of NACs. This proposed model was successfully applied to a true external set (295 compounds), and prediction reliability was analysed and discussed. Moreover, the rat-mouse and mouse-rat interspecies quantitative toxicity-toxicity relationship (iQTTR) models were also constructed, validated and employed in toxicity prediction for true external sets consisting of 67 and 265 compounds, respectively. These models showed good external predictivity that can be used to rapidly predict the rat oral acute toxicity of new or untested NACs falling within the applicability domain of the models, thus being beneficial in environmental risk assessment and regulatory purposes.

Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative structure activity relationship study

In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R² ​= ​0.80-0.82, Q² _loo ​= ​0.74-0.77, Q ² _LMO  ​= ​0.66-0.67). The developed QSAR models identified number of sp² hybridized Oxygen atoms within seven bonds from aromatic Carbon atoms, the presence of Carbon and Nitrogen atoms at a topological distance of 3 and other interrelations of atom pairs as important pharmacophoric features. Hence, the present QSAR models have a good balance of Qualitative (Descriptive QSARs) and Quantitative (Predictive QSARs) approaches, therefore useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.

Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods

Environmental fate, behavior and effects of hazardous organic compounds have recently received great attention in diverse environmental phases, including water, atmosphere, soil and sediment. Considering polydimethylsiloxane (PDMS) fibers were validated for the wide application in the determination of partition behavior in passive sampling, in this work, several in silico models were established to predict PDMS-water (K_PDMS-w), PDMS-air (K_PDMS-a) and PDMS-seawater partition coefficients (K_PDMS-sw) of diverse chemicals. This is an attempt to combine conventional linear method and popular nonlinear algorithm for the estimation of partition coefficients between PDMS and different environmental media. All of the developed models showed satisfactory goodness-of-fit with high adjusted correlation coefficient (R²_adj) and were validated to be robust, stable and predictable by various internal and external validation techniques, deriving a wide series of statistical checks. Moreover, it was found that hydrophobicity, polarizability, charge distribution and molecular size of compounds contributed significantly to the model development by interpreting the selected descriptors. Based on the broad applicability domains (ADs), the current study provides suitable tools to fill the experimental data gap for other compounds and to help researchers better understand the mechanistic basis of adsorption behavior of PDMS.

Prediction of the Partition Coefficient between Adipose Tissue and Blood for Environmental Chemicals: From Single QSAR Models to an Integrated Approach

The adipose tissue:blood partition coefficient is a key-endpoint to predict the pharmacokinetics of chemicals in humans and animals, since other organ:blood affinities can be estimated as a function of this parameter. We performed a search in the literature to select all the available rat in vivo data. This approach resulted into two improvements to existing models: a homogeneous definition of the endpoint and an expanded data collection. The resulting dataset was used to develop QSAR models as a function of linear and non-linear algorithms. Several applicability domain definitions were assessed and the definition corresponding to a good balance between performance and coverage was retained. We assessed the pertinence of combining single models into integrated approaches to increase the accuracy in predictions. The best integrated model outperformed the single models and it was characterized by an external mean absolute error (MAE) equal to 0.26, while preserving an adequate coverage (84 %). This performance is comparable to experimental variability and it highlights the pertinence of the integrated model.

In silico design and analysis of NS4B inhibitors against hepatitis C virus

The hepatitis C virus is a communicable disease that gradually harms the liver leading to cirrhosis and hepatocellular carcinoma. Important therapeutic interventions have been reached since the discovery of the disease. However, its resurgence urges the need for new approaches against this malady. The NS4B receptor is one of the important proteins for Hepatitis C Virus RNA replication that acts by mediating different viral properties. In this work, we opt to explore the relationships between the molecular structures of biologically tested NS4B inhibitors and their corresponding inhibitory activities to assist the design of novel and potent NS4B inhibitors. For that, a set of 115 indol-2-ylpyridine-3-sulfonamides (IPSA) compounds with inhibitory activity against NS4B is used. A hybrid genetic algorithm combined with multiple linear regressions (GA-MLR) was implemented to construct a predictive model. This model was further used and applied to a set of compounds that were generated based on a pharmacophore modeling study combined with virtual screening to identify structurally similar lead compounds. Multiple filtrations were implemented for selecting potent hits. The selected hits exhibited advantageous molecular features, allowing for favorable inhibitory activity against HCV. The results showed that 7 out of 1285 screened compounds, were selected as potent candidate hits where Zinc14822482 exhibits the best predicted potency and pharmacophore features. The predictive pharmacokinetic analysis further justified the compounds as potential hit molecules, prompting their recommendation for a confirmatory biological evaluation. We believe that our strategy could help in the design and screening of potential inhibitors in drug discovery.Communicated by Ramaswamy H. Sarma.

Correlation intensity index: Building up models for mutagenicity of silver nanoparticles

Nanomaterials become significant component of economics. Consequently, nanomaterials become object of environmental sciences. There is a traditional list of endpoints which are indicators of the ecological risk. Mutagenicity is one of important component in this list. The quasi-SMILES approach, that in contrast to majority of work dedicated to modelling behaviour of nanomaterials gives possibility to consider experimental conditions as well as other circumstances which can impact the behaviour of nanomaterials is suggested. This is carried out via so-called quasi-SMILES. The quasi-SMILES is a line on of codes that contains all the above available eclectic data. Modelling process aimed to build up a model involves Correlation Intensity Index (CII) that is a new criterion of predictive potential of models. The scheme of calculation of CII is described in this work in the first time. The applying of CII together with Index of Ideality Correlation (IIC) in modelling of mutagenicity of silver nanoparticles by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral) indicates that application of the CII improves the predictive potential of these models for three random splits into the training set (75%) and validation set (25%).

Modelling quantitative structure activity-activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation

Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors. We investigated a large acute toxicity data set of five aquatic organisms, fish, Daphnia magna, and algae from the VEGA-Hub, as well as Tetrahymena pyriformis and Vibrio fischeri. The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.

Zebrafish AC50 modelling: (Q)SAR models to predict developmental toxicity in zebrafish embryo

Developmental toxicity refers to the occurrence of adverse effects on a developing organism as a consequence of exposure to hazardous chemicals. The assessment of developmental toxicity has become relevant to the safety assessment process of chemicals. The zebrafish embryo developmental toxicology assay is an emerging test used to screen the teratogenic potential of chemicals and it is proposed as a promising test to replace teratogenic assays with animals. Supported by the increased availability of data from this test, the developmental toxicity assay with zebrafish has become an interesting endpoint for the in silico modelling. The purpose of this study was to build up quantitative structure-activity relationship (QSAR) models. In this work, new in silico models for the evaluation of developmental toxicity were built using a well-defined set of data from the ToxCast^TM Phase I chemical library on the zebrafish embryo. Categorical and continuous QSAR models were built by gradient boosting machine learning and the Monte Carlo technique respectively, in accordance with Organization for Economic Co-operation and Development principles and their statistical quality was satisfactory. The classification model reached balanced accuracy 0.89 and Matthews correlation coefficient 0.77 on the test set. The regression model reached correlation coefficient R² 0.70 in external validation and leave-one-out cross-validated Q² 0.73 in internal validation.

In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation

Azo dyes are a group of chemical moieties joined by azo (-N=N-) group with potential usefulness in different industrial applications. But these dyes are not devoid of hazardous consequence because of poor affinity for the fibre and discharge into the water stream. The chemical aspects of 72 azo dyes towards cellulose fibre in terms of their affinity by QSPR have been explored in the present work. We have employed two approaches, namely balance of correlation without IIC (TF₁) and balance of correlation with IIC (TF₂), to generate 16 QSAR models from 8 splits. The determination coefficient of calibration and validation set was found higher when the QSPR models were developed using the index of ideality correlation (IIC) parameter (TF₂). The model developed with TF₂ for split 3 was considered as a prominent model because the determination coefficient of the validation set was maximum (r ² = 0.9468). The applicability domain (AD) was also analysed based on 'statistical defect', d(A) for a SMILES attribute. The mechanistic interpretation was done by identifying the SMILES attributes responsible for the promoter of endpoint increase and promoter of endpoint decrease. These SMILES attributes were applied to design 15 new dyes with higher affinity for cellulose fibre.

Novel sulfone derivatives containing a 1,3,4-oxadiazole moiety: design and synthesis based on the 3D-QSAR model as potential antibacterial agent

BACKGROUND

The rice bacterial leaf blight (BLB) is one of the most serious bacterial diseases caused by Xanthomonas oryzae pv. oryzae (Xoo), which can cause yield loss of rice up to 50%. The three-dimensional quantitative structure-activity relationship (3D-QSAR) is an important auxiliary method to find potential high-efficient pesticides active structures.

RESULTS

A series of novel 1,3,4-oxadiazole compounds were designed and synthesized based on the 3D-QSAR model, and their antibacterial activities in vitro against Xoo were evaluated. The results indicated that all the target compounds showed excellent in vitro antibacterial activities. For example, the compounds 6, 12, 13, 20, 21, and 23 exhibited excellent antibacterial activities against Xoo, with half-maximal effective concentration (EC₅₀ ) values of 0.24, 0.31, 0.36, 0.29, 0.19, and 0.31 mg/L, respectively, which were superior to the antibacterial agents thiodiazole copper (127.44 mg/L) and bismerthiazol (91.08 mg/L). Meanwhile, compound 21 showed good antibacterial activity in vivo against BLB, with curative and protective activities of 46.7% and 56.4%, respectively, which were superior to thiodiazole copper (28.5% and 32.5%) and bismerthiazol (37.6% and 38.4%). Compound 21 can significantly reduce the extracellular polysaccharides production of Xoo, increase the permeability of the cell membranes, and also can cause cell surface wrinkles, deformation and dryness.

CONCLUSION

The 3D-QSAR model can be used to find sulfone compounds containing a 1,3,4-oxadiazole moiety with higher antibacterial activity, and compound 21 can be used as a potential antibacterial agent in the future. © 2020 Society of Chemical Industry.

Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling

A quantitative structure-activity relationship (QSAR) model was built from a dataset of 54 peptide-type compounds as SARS-CoV inhibitors. The analysis was executed to identify prominent and hidden structural features that govern anti-SARS-CoV activity. The QSAR model was derived from the genetic algorithm-multi-linear regression (GA-MLR) methodology. This resulted in the generation of a statistically robust and highly predictive model. In addition, it satisfied the OECD principles for QSAR validation. The model was validated thoroughly and fulfilled the threshold values of a battery of statistical parameters (e.g. r ² = 0.87, Q ² _loo = 0.82). The derived model is successful in identifying many atom-pairs as important structural features that govern the anti-SARS-CoV activity of peptide-type compounds. The newly developed model has a good balance of descriptive and statistical approaches. Consequently, the present work is useful for future modifications of peptide-type compounds for SARS-CoV and SARS-CoV-2 activity.

Synthesis, In Silico and In Vitro Evaluation for Acetylcholinesterase and BACE-1 Inhibitory Activity of Some N-Substituted-4-Phenothiazine-Chalcones

Acetylcholinesterase (AChE) and beta-secretase (BACE-1) are two attractive targets in the discovery of novel substances that could control multiple aspects of Alzheimer’s disease (AD). Chalcones are the flavonoid derivatives with diverse bioactivities, including AChE and BACE-1 inhibition. In this study, a series of N-substituted-4-phenothiazine-chalcones was synthesized and tested for AChE and BACE-1 inhibitory activities. In silico models, including two-dimensional quantitative structure–activity relationship (2D-QSAR) for AChE and BACE-1 inhibitors, and molecular docking investigation, were developed to elucidate the experimental process. The results indicated that 13 chalcone derivatives were synthesized with relatively high yields (39–81%). The bioactivities of these substances were examined with pIC₅₀ 3.73–5.96 (AChE) and 5.20–6.81 (BACE-1). Eleven of synthesized chalcones had completely new structures. Two substances AC4 and AC12 exhibited the highest biological activities on both AChE and BACE-1. These substances could be employed for further researches. In addition to this, the present study results suggested that, by using a combination of two types of predictive models, 2D-QSAR and molecular docking, it was possible to estimate the biological activities of the prepared compounds with relatively high accuracy.

A Refractive Index Study of a Diverse Set of Polymeric Materials by QSPR with Quantum-Chemical and Additive Descriptors

Predicting the activities and properties of materials via in silico methods has been shown to be a cost- and time-effective way of aiding chemists in synthesizing materials with desired properties. Refractive index (n) is one of the most important defining characteristics of an optical material. Presented in this work is a quantitative structure–property relationship (QSPR) model that was developed to predict the refractive index for a diverse set of polymers. A number of models were created, where a four-variable model showed the best predictive performance with R² = 0.904 and Q²_LOO = 0.897. The robustness and predictability of the best model was validated using the leave-one-out technique, external set and y-scrambling methods. The predictive ability of the model was confirmed with the external set, showing the R²_ext = 0.880. For the refractive index, the ionization potential, polarizability, 2D and 3D geometrical descriptors were the most influential properties. The developed model was transparent and mechanistically explainable and can be used in the prediction of the refractive index for new and untested polymers.

In silico exploration of the fingerprints triggering modulation of glutaminyl cyclase inhibition for the treatment of Alzheimer's disease using SMILES based attributes in Monte Carlo optimization

Alzheimer's disease is the most common neurodegenerative disorder and being a social burden Alzheimer's has become an economic liability on developing countries. With limited understanding regarding the cause of disease, it is commonly identified by extracellular deposit of amyloid β (Aβ) peptides as senile plaques. Pyroglutamated Aβ is identified from the brain of AD patients and constituted the majority of total Aβ present. The formation of Pyroglutamated Aβ could be hindered by the use of Glutaminyl cyclase inhibitors and could efficiently improve the symptoms of Alzheimer's. The literature revealed the competence of quantitative structure activity/property relationship studies in drug discovery. The present work explores the efficiency of Monte Carlo based QSAR modelling studies on a dataset of 125 Glutaminyl cyclase inhibitors with pKi taken as the endpoint for QSAR analysis. The dataset is divided into training, subtraining, calibration and validation sets resulting in the generation of five random splits. The validation is performed in accordance with the Organization of Economic Corporation and Development principles. The values of R², Q², index of ideality of correlation, concordance correlation coefficient, av. rm2 and delta rm2 of calibration set of the best split are found to be 0.9012, 0.8775, 0.9479, 0.9435, 0.8347 and 0.0847, respectively. The structural features responsible for increasing the inhibitory activity are identified. These structural features are added to a base compound from the dataset to design six novel molecules. These new molecules possess improved inhibitory activity as compare to the base compound. The results are further supported by docking studies.Communicated by Vsevolod Makeev.

Design of Curcumin and Flavonoid Derivatives with Acetylcholinesterase and Beta-Secretase Inhibitory Activities Using in Silico Approaches

Acetylcholinesterase (AChE) and beta-secretase (BACE-1) are the two crucial enzymes involved in the pathology of Alzheimer's disease. The former is responsible for many defects in cholinergic signaling pathway and the latter is the primary enzyme in the biosynthesis of beta-amyloid as the main component of the amyloid plaques. These both abnormalities are found in the brains of Alzheimer's patients. In this study, in silico models were developed, including 3D-pharmacophore, 2D-QSAR (two-dimensional quantitative structure-activity relationship), and molecular docking, to screen virtually a database of compounds for AChE and BACE-1 inhibitory activities. A combinatorial library containing more than 3 million structures of curcumin and flavonoid derivatives was generated and screened for drug-likeness and enzymatic inhibitory bioactivities against AChE and BACE-1 through the validated in silico models. A total of 47 substances (two curcumins and 45 flavonoids), with remarkable predicted pIC₅₀ values against AChE and BACE-1 ranging from 4.24-5.11 (AChE) and 4.52-10.27 (BACE-1), were designed. The in vitro assays on AChE and BACE-1 were performed and confirmed the in silico results. The study indicated that, by using in silico methods, a series of curcumin and flavonoid structures were generated with promising predicted bioactivities. This would be a helpful foundation for the experimental investigations in the future. Designed compounds which were the most feasible for chemical synthesis could be potential candidates for further research and lead optimization.

Versatile modelling of polyoxymethylene-water partition coefficients for hydrophobic organic contaminants using linear and nonlinear approaches

Environmental fate or transport of hydrophobic organic contaminants (HOCs) depends on the partitioning properties of compounds within various environmental phases. Due to the wide application of polyoxymethylene (POM) in the passive sampling technique, several in silico models were developed to predict POM-water partition coefficients (K_POM-w) in accordance with the guidelines of the Organization for Economic Cooperation and Development (OECD). It is an attempt to combine conventional linear method (multiple linear regression, MLR) and popular nonlinear algorithm (artificial neural network, ANN) for estimating partition coefficients of HOCs. All models were performed on a dataset of 210 chemicals from 13 different classes. The polyparameter linear free energy relationship (pp-LFER) model included 5 molecular descriptors, namely, E, S, A, B and V, and predicted log K_POM-w with R²_adj of 0.825. The values of statistical parameters including R²_adj, Q²_ext, RMSE_tra and RMSE_ext for quantitative structure-property relationship (QSPR)-MLR and QSPR-ANN models with four descriptors (ALOGP, MeanDD, E1m and Mor24s) were: (0.928, 0.877, 0.498 and 0.649) and (0.943, 0.905, 0.443 and 0.571), with high similarity for both models, which confirmed the robustness, significance, and remarkable prediction accuracy of the QSPR models. Moreover, the mechanism interpretation revealed that the molecular volume and hydrophobicity had a major impact on distribution procedure of HOCs. The models developed herein, with the broad applicability domain (AD), provide suitable tools to fill the experimental data gap for untested chemicals and help researchers better understand the mechanistic basis of adsorption behavior of POM.

Prediction of Soil Adsorption Coefficient in Pesticides Using Physicochemical Properties and Molecular Descriptors by Machine Learning Models

The soil adsorption coefficient (K_OC ) plays an important role in environmental risk assessment of pesticide registration. Based on this risk assessment, applied and registered pesticides can be allowed in the European Union. Almost 1 yr is required to study and obtain the K_OC value of a pesticide. Furthermore, acquiring the K_OC requires a large cost. It is necessary to efficiently estimate the K_OC value in the early stages of pesticide development. In the present study, the experimental values of physicochemical properties and molecular descriptors of chemical structures were collected to develop a quantitative structure-property relationship (QSPR) model, and the prediction performance of the model was evaluated. More specifically, we compared the accuracies of models based on a gradient boosting decision tree, multiple linear regression, and support vector machine. The experimental results suggest that it is possible to develop a QSPR model with high accuracy using both the molecular descriptors calculated from the structural formula and experimental values of physicochemical properties from open literature and databases. Comparing to the previously established models, we achieved high prediction accuracy, fitness, and robustness by only using freeware. Therefore, our developed QSPR models can be useful preliminary risk assessment in the early developmental stages of pesticides. Environ Toxicol Chem 2020;39:1451-1459. © 2020 SETAC.

Principles of QSAR Modeling

At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.

QSAR and Molecular Docking Studies on Non-Imidazole-Based Histamine H3 Receptor Antagonists

Background: In the recent years, histamine H3 receptor (H3R) has been receiving increasing attention in pharmacotherapy of neurological disorders. The aim of the current study was to investigate structural requirements for the prediction of H3 antagonistic activity using quantitative structure-activity relationship (QSAR) and molecular docking techniques. Methods: To this end, genetic algorithm coupled partial least square and stepwise multiple linear regression methods were employed for developing a QSAR model. The obtained QSAR model was stringently assessed using different validation criteria. Results: The generated model indicated that connectivity information and mean absolute charge are two important descriptors for the prediction of H3 antagonistic activity of the studied compounds. To gain insight into the mechanism of interaction between studied molecules and H3R, molecular docking was performed. The most important residues involved in the ligand-receptor interactions were identified. Conclusion: The result of current study can be used for designing of new H3 antagonist and proposing structural modifications to improve H3 inhibitory potency.

Planning new Trypanosoma cruzi CYP51 inhibitors using QSAR studies

Chagas disease kills over 10,000 people per year, and approximately 8 million people are infected by Trypanosoma cruzi. The reference drug for treatment of the disease, benznidazole, is the same since the 70s. In recent years, many CYP51 inhibitors were tested against this parasite's target. One of them, posaconazole, was even tested in clinical trials that unfortunately were not successful. Nevertheless, there are still many evidences that CYP51 is a great potential target to treat T. cruzi infection. The research for new effective molecules that can cure the chronic phase of the disease is essential. 2D and 3D-quantitative structure activity relationship (QSAR) studies were conducted in this work to create three QSAR models using the chemical structures of 197 published compounds that already went through either in vivo or in vitro tests. After the analysis of the models, new analogues not yet synthesized were suggested here and had their biological activity and synthetic availability assessed.

Computational strategies towards developing novel antimelanogenic agents

AIMS

Extrinsic ageing or photoageing relates to the onset of age-linked phenotypes such as skin hyperpigmentation due to UV exposure. UV induced upregulated production of tyrosinase enzyme, which catalyses the vital biochemical reactions of melanin synthesis is responsible for the inception of skin hyperpigmentation. We aimed to generate a validated QSAR model with a dataset consisting of 69 thio-semicarbazone derivatives to elucidate the physicochemical properties of compounds essential for tyrosinase inhibition and to identify novel lead molecules with enhanced tyrosinase inhibitory activity and bioavailability.

MAIN METHODS

Lead optimization and insilico approaches were employed in this research work. QSAR model was generated and validated by exploiting Multiple Linear Regression method. Prioritization of lead-like compounds was accomplished by performing multi parameter optimization depleting molecular docking, bioavailability assessments and toxicity prediction for 69 compounds Derivatives of best lead compound were retrieved from chemical spaces.

KEY FINDINGS

Molecular descriptors explicated the significance of chemical properties essential for chelation of copper ions present in the active site of tyrosinase protein target. Further, derivatives which comprise of electron donating groups in their chemical structure were predicted and analysed for tyrosinase inhibitory activity by employing insilico methodologies including chemical space exploration.

SIGNIFICANCE

Our research work resulted in the generation of a validated QSAR model with higher degree of external predictive ability and significance to tyrosinase inhibitory activity. We propose 11 novel derivative compounds with enhanced tyrosinase inhibitory activity and bioavailability.

2D QSAR studies on a series of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one as CETP inhibitors

Cardiovascular disease (CVD) is one of the major causes of human death. Preliminary evidence indicates that the inhibition treatment of Cholesteryl Ester Transfer Protein (CETP) causes the most pronounced increase in HDL cholesterol reported so far. Merck has disclosed certain (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one derivatives, which show potent CETP inhibitory activity. Therefore, it would be desirable to develop computational models to facilitate the screening of these inhibitors. In the present work, quantitative structure-activity relationship (QSAR) models have been developed to predict the therapeutic potency of 108 derivatives of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one: Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Feedforward Neural Network using Particle Swarm Optimization (FNN-PSO). Six descriptors were selected using genetic algorithms, whereas, internal and external validation of the models was performed according to all available validation strategies. It was shown that CETP inhibitory activity is mainly governed by electronegativity, the structure of the molecule, and the electronic properties. The best results were obtained with the SVR model. The results obtained may assist in the design of new CETP inhibitors.

Design of New Therapeutic Agents Targeting FLT3 Receptor Tyrosine Kinase Using Molecular Docking and 3D-QSAR Approach

Background:

FMS-like tyrosine kinase-3 (FLT3) belongs to the class III Receptor Tyrosine Kinase (RTK) family. FLT3 is involved in normal hematopoiesis and is generally expressed in early hematopoietic progenitor cells. Mutations either with an internal tandem duplication of FMS-like tyrosine kinase-3 (FLT3-ITD) or point mutation at the activation loop leads to the Acute Myeloid Leukemia (AML), a highly heterogeneous disease. Thus, FLT3 is an important therapeutic target for AML.

Method:

In the present work, docking and 3D-QSAR techniques were performed on a series of diaminopyrimidine derivatives as FLT3 kinase antagonists.

Results:

Docking study recognized important active site residues such as Leu616, Gly617, Val624, Ala642, Phe830, Tyr693, Cys694, Cys695, Tyr696 and Gly697 that participate in the inhibition of FLT3 kinase. Receptor-based CoMFA, RF-CoMFA and CoMSIA models were developed. RFCoMFA model revealed relatively better statistical results compared to other models. Furthermore, the selected RF-CoMFA model was evaluated using various validation techniques. Contour maps of the RF-CoMFA illustrated that steric and electronegative substitutions were favored at R1 position whereas steric and electropositive substitutions were favored at R2 position to enhance the potency.

Conclusion:

Based on the designed strategy, we derived from the contour map analysis, 14 novel FLT3 inhibitors were designed and their activities were predicted. These designed inhibitors exhibited more potent activity than the most active compounds of the dataset.

Quantitative structure-chromatographic retention relationship of synthesized peptides (HGRFG, NPNPT) and their derivatives

Carapax Trionycis extract peptides (HGRFG, NPNPT) are able to protect against CCl₄-induced liver fibrosis. Therefore, this study applies to deal with chromatographic lipophilicity determination of synthesized peptides (HGRFG, NPNPT) and their derivatives using reversed-phase high performance liquid chromatography (RP-HPLC) combined with methanol-water mobile phase and two reversed-phase chromatographic columns (COSMOISL 5C18-MS-II and SHIMADZU-C18). The chromatographic lipophilicity of the analyzed compounds was expressed as logk_w constant and correlated with lipophilicity descriptors. Quantitative structure-retention relationships (QSRR) analysis was performed to imitate chromatographic lipophilicity behavior using molecular descriptors. Modeling was performed using linear regression (LR) and multiple linear regression (MLR) methods with the help of principal component analysis (PCA) and hierarchical cluster analysis (HCA). The most influential molecular descriptors were lipophilicity descriptors, which are important for molecules ability to pass through biological membranes. All established QSRR models were statistically validated by standards, cross- and external validation parameters. According to these statistical validation parameters, MLR models (R² > 0.856) were better for chromatographic lipophilicity prediction of peptide compounds. It can be concluded that chromatographic systems with COSMOISL 5C18-MS-II column were better for modeling of logk_w than systems with SHIMADZU-C18 column. Modeling was performed in order to obtain lipophilicity profiles of investigated compounds as future drug candidates.

Norm index-based QSPR model for describing the n-octanol/water partition coefficients of organics

The n-octanol/water partition coefficient (logK_ow) is widely used in the environmental, agricultural and pharmaceutical fields for the risk evaluation and application of organic chemicals. In this work, grounded on atomic distribution matrices, a norm index-based QSPR model was built for organic chemicals with 18 kinds of diverse structures. The statistical results (R² = 0.9037, RMSE = 0.4515) showed that the QSPR model for describing the logK_ow of organics was fitted well. Various validation results showed that the model had good robustness, good predictability and wide applicability. These satisfactory results indicated that the model was applicable for the logK_ow description of organic chemicals and that norm descriptors were reliable and general for the description of organic structures. The model was relatively better at describing logK_ow for aromatics, alcohols, nitriles, esters, amides, halogenated compounds, acids and amine compounds. The intensity of spatial branching and the space charge distribution intensity descriptors could have a greater impact on the logK_ow value of a compound.

Application of chemometric methods and QSAR models to support pesticide risk assessment starting from ecotoxicological datasets

The EFSA 'Guidance on tiered risk assessment for edge-of-field surface waters' underscores the importance of in silico models to support the pesticide risk assessment. The aim of this work was to use in silico models starting from an available, structured and harmonized pesticide dataset that was developed for different purposes, in order to stimulate the use of QSAR models for risk assessment. The present work focuses on the development of a set of in silico models, developed to predict the aquatic toxicity of heterogeneous pesticides with incomplete/unknown toxic behavior in the water compartment. The generated models have good fitting performances (R²: 0.75-0.99), they are internally robust (Q²loo: 0.66-0.98) and can handle up to 30% of perturbation of the training set (Q² lmo: 0.64-0.98). The absence of chance correlation was guaranteed by low values of R² calculated on scrambled responses (R² Y_scr: 0.11-0.38). Different statistical parameters were used to quantify the external predictivity of the models (CCC_ext: 0.73-0.91, Q² ext-Fn: 0.53-0.96). The results indicate that all the best models are predictive when applied to chemicals not involved in the models development. In addition, all models have similar accuracy both in fitting and in prediction and this represents a good degree of generalization. These models may be useful to support the risk assessment procedure when experimental data for key species are missing or to create prioritization lists for the general a priori assessment of the potential toxicity of existing and new pesticides which fall in the applicability domain.

Quantitative structure-property relationship of the photoelectrochemical oxidation of phenolic pollutants at modified nanoporous titanium oxide using supervised machine learning

Here we report on an advanced photoelectrochemical (PEC) oxidation of 22 phenolic pollutants based on modified nanoporous TiO2, which was directly grown on a titanium substrate electrochemically. Their degradation rate constants were experimentally determined and their physicochemical properties were computaionally calculated. The quantitative structure-property relationship (QSPR) was elucidated by employing multiple linear regression (MLR) method. A supervised machine learning approach was employed to build QSPR models. The high predictive abilities of the QSPR model were validated via leave-one-out (LOO) method and a strict regimen of statistical validation tests. The significant descriptors identified in the QSPR Model for the phenolic compounds were also assessed using a typical dye pollutant Rhodamine B, further confirming the high effectiveness and predictability of the optimized model. Our study has shown that the integrated effect of the structural, hydrophobic and topological properties along with electronic property should be considered in order to design an efficient PEC catalytic approach for environmental applications.

Modeling pesticides toxicity to Sheepshead minnow using QSAR

Nowadays, the environmental risk caused by the widespread use of pesticides and their ubiquitous residuals has received more and more attention in academia and regulatory agencies. Due to the large number of pesticides used in agriculture and their adverse effects on all living organisms and the numerous end-points, it is necessary to employ the in silico tools to quickly highlight hazardous pesticides. In this study, we have evaluated the toxicity of pesticides against Sheepshead minnow with the Quantitative Structure-Activity Relationship (QSAR) approach. The models for the specific-type (insecticides, herbicides and fungicides) as well as the general-type (combing all the specific-type pesticides and some microbicides, nematicides, etc.) pesticides were developed using the Genetic Algorithm and the Multiple Linear Regression method, subsequently validated with various metrics. The validation results suggested that the obtained models were highly robust, externally predictive and characterized by a broad applicability domain. Considering the modeling descriptors, the toxicity of pesticides would increase with the lipophilicity and decrease with the polarity and hydrophilicity. Most electrotopological state descriptors contribute negatively to the toxicity, while the influence of topological structure descriptors mainly depends on the physiochemical information they encode. The models proposed in this paper would be useful in filling the data gaps, prioritizing and then focusing experiments on more hazardous pesticides.