Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection

From molecular descriptors to the developmental toxicity prediction of pesticides/veterinary drugs/bio-pesticides against zebrafish embryo: Dual computational toxicological approaches for prioritization

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 (AC50) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R2 > 0.6 and QLOO2 > 0.6) and external predictivity (Rtest2 > 0.7, QFn2 >0.7, and CCCtest > 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.

Synthesis, biological evaluation, theoretical calculations, QSAR and molecular docking studies of novel arylaminonaphthols as potent antioxidants and BChE inhibitors

A completely green protocol was developed for the synthesis of a series of arylaminonaphthol derivatives in the presence of N-ethylethanolamine (NEEA) as a catalyst under ultrasonic irradiation and solventless conditions. The major assets of this methodology were the use of non-toxic organic medium, available catalyst, mild reaction condition, and good to excellent yield of desired products. All of the synthesized products were screened for their in vitro antioxidant activity using DPPH, ABTS, and Ferric-phenanthroline assays and it was found that most of them are potent antioxidant agents. Also, their butyrylcholinesterase inhibitory activity has been investigated in vitro. All tested compounds exhibited potential inhibitory activity toward BuChE when compared to standard reference drug galantamine, however, compounds 4r, 4u, 4 g and 4x gave higher butyrylcholinesterase inhibitory with IC50 values of 14.78 ± 0.65 µM, 16.18 ± 0.50 µM, 20.00 ± 0.50 µM, and 20.28 ± 0.08 µM respectively. On the other hand, we employed density functional theory (DFT), calculations to analyze molecular geometry and global reactivity descriptors, and MESP analysis to predict electrophilic and nucleophilic attacks. A quantitative structure-activity relationship (QSAR) investigation was conducted on the antioxidant and butyrylcholinesterase properties of 25 arylaminonaphthol derivatives, resulting in robust and satisfactory models. To evaluate their anti-Alzheimer's activity, compounds 4 g, 4q, 4r, 4u, and 4x underwent docking simulations at the active site of the acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), revealing why these compounds displayed superior activity, consistent with the biological findings.

New Species and Cytotoxicity Mechanism of Halohydroxybenzonitrile Disinfection Byproducts in Drinking Water

Recently, seven dihalohydroxybenzonitriles (diHHBNs) have been determined as concerning nitrogenous aromatic disinfection byproducts (DBPs) in drinking water. Herein, eight new monohalohydroxybenzonitriles (monoHHBNs), including 3-chloro-2-hydroxybenzonitrile, 5-chloro-2-hydroxybenzonitrile, 3-chloro-4-hydroxybenzonitrile, 3-bromo-2-hydroxybenzonitrile, 5-bromo-2-hydroxybenzonitrile, 3-bromo-4-hydroxybenzonitrile, 5-iodo-2-hydroxybenzonitrile, and 3-iodo-4-hydroxybenzonitrile, were detected and identified in drinking water for the first time. Thereafter, the relative concentration-cytotoxicity contribution of each HHBN was calculated based on the acquired occurrence level and cytotoxicity data in this study, the genome-scale cytotoxicity mechanism was explored, and a quantitative structure-activity relationship (QSAR) model was developed. Results indicated that new monoHHBNs were present in drinking water at concentrations of 0.04-1.83 ng/L and exhibited higher cytotoxicity than some other monohalogenated aromatic DBPs. Notably, monoHHBNs showed concentration-cytotoxicity contribution comparable to diHHBNs, which have been previously identified as potential toxicity drivers in drinking water. Transcriptomic analysis revealed immunotoxicity and genotoxicity as dominant cytotoxicity mechanisms for HHBNs in Chinese hamster ovary (CHO-K1) cells, with potential carcinogenic effects. The QSAR model suggested oxidative stress and cellular uptake efficiency as important factors for their cytotoxicity, highlighting the importance of potential iodinated HHBNs in drinking water, such as 3,5-diiodo-2-hydroxybenzonitrile, for future studies. These findings are meaningful for better understanding the health risk and toxicological significance of HHBNs in drinking water.

Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics

In the field of microplastics (MPs) toxicity prediction, machine learning (ML) computer simulation techniques are showing great potential. In this study, six ML algorithms were utilized to predict the toxicity of MPs on BEAS-2B cells based on quantitative structure-activity relationship (QSAR) models. Comparing the models of different algorithms, the extreme gradient boosting model showed the best fit and prediction performance (R2tra = 0.9876, R2test = 0.9286). Additionally, Williams plot analysis showed that the six models developed were able to predict stably within their applicability domain, with few outliers. Finally, the three feature importance methods-Embedded Feature Importance (EFI), Recursive Feature Elimination (RFE), and SHapley Additive exPlanations (SHAP)-consistently identified particle size as the most critical feature affecting toxicity prediction. The proposed QSAR model can be utilized for preliminary environmental exposure assessments of MPs and to better understand the associated health risks.

The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods

All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure-toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs.

Toxicity of individual and mixture of organic compounds to P. Phosphoreum and S. Capricornutum using interpretable simple structural parameters

Human and environmental ecosystem beings are exposed to multicomponent compound mixtures but the toxicity nature of compound mixtures is not alike to the individual chemicals. This work introduces four models for the prediction of the negative logarithm of median effective concentration (pEC50) of individual chemicals to marine bacteria Photobacterium Phosphoreum (P. Phosphoreum) and algal test species Selenastrum Capricornutum (S. Capricornutum) as well as their mixtures to P. Phosphoreum, and S. Capricornutum. These models provide the simplest approaches for the forecast of pEC50 of some classes of organic compounds from their interpretable structural parameters. Due to the lack of adequate toxicity data for chemical mixtures, the largest available experimental data of individual chemicals (55 data) and their mixtures (99 data) are used to derive the new correlations. The models of individual chemicals are based on two simple structural parameters but chemical mixture models require further interaction terms. The new model's results are compared with the outputs of the best accessible quantitative structure-activity relationships (QSARs) models. Various statistical parameters are done on the new and comparative complex QSAR models, which confirm the higher reliability and simplicity of the new correlations.

Cytotoxicity of emerging halophenylacetamide disinfection byproducts in drinking water: Mechanism and prediction

Halophenylacetamides (HPAcAms) have been identified as a new group of nitrogenous aromatic disinfection byproducts (DBPs) in drinking water, but the toxicity mechanisms associated with HPAcAms remain almost completely unknown. In this work, the cytotoxicity of HPAcAms in human hepatoma (HepG2) cells was evaluated, intracellular oxidative stress/damage levels were analyzed, their binding interactions with antioxidative enzyme were explored, and a quantitative structure-activity relationship (QSAR) model was established. Results indicated that the EC50 values of HPAcAms ranged from 2353 μM to 9780 μM, and the isomeric structure as well as the type and number of halogen substitutions could obviously induce the change in the cytotoxicity of HPAcAms. Upon exposure to 2-(3,4-dichlorophenyl)acetamide (3,4-DCPAcAm), various important biomarkers linked to oxidative stress and damage, such as reactive oxygen species, 8‑hydroxy-2-deoxyguanosine, and cell apoptosis, exhibited a significant increase in a dose-dependent manner. Moreover, 3,4-DCPAcAm could directly bind with Cu/Zn-superoxide dismutase and induce the alterations in the structure and activity, and the formation of complexes was predominantly influenced by the van der Waals force and hydrogen bonding. The QSAR model supported that the nucleophilic reactivity as well as the molecular compactness might be highly important in their cytotoxicity mechanisms in HepG2 cells, and 2-(2,4-dibromophenyl)acetamide and 2-(3,4-dibromophenyl)acetamide deserved particular attention in future studies due to the relatively higher predicted cytotoxicity. This study provided the first comprehensive investigation on the cytotoxicity mechanisms of HPAcAm DBPs.

Prediction of acute toxicity for Chlorella vulgaris caused by tire wear particle-derived compounds using quantitative structure-activity relationship models

Tire wear particles (TWPs) enter aquatic ecosystems through various pathways, such as rainwater and urban runoff. Additives in TWPs can harm aquatic organisms in these ecosystems. Therefore, it is essential to investigate their toxicity to aquatic organisms. In our study, we initially recorded the median effective concentrations of 21 TWP-derived compounds on Chlorella vulgaris growth, ranging from 0.04 to 8.60 mg/L. Subsequently, through an extensive review of the literature, we incorporated 112 compounds with specific toxicity endpoints to construct the QSAR model using genetic algorithm and multiple linear regression techniques, followed by the construction of the consensus model and the quantitative read-across structure-activity relationship (q-RASAR) model. Meanwhile, we employed rigorous internal and external validation measures to assess the performance of the model. The results indicated that the developed q-RASAR model exhibited strong adaptation, robustness, and reliable prediction, with q-RASAR indicators of Q2LOO = 0.7673, R2tr = 0.8079, R2test = 0.8610, Q2Fn = 0.8285-0.8614, and CCCtest = 0.9222. Based on an external dataset containing 128 emerging TWP-derived compounds, the model's applicability domain coverage was 90.6 %. The q-RASAR model predicted that the structure of diphenylamine was associated with higher toxicity, possibly liked to the SpMax2_Bhm and LogBCF descriptors. The established model reliably provides prediction and fills a critical data gap. These findings highlight the potential risks posed by emerging TWP-derived compounds to aquatic organisms.

The first report on the assessment of maximum acceptable daily intake (MADI) of pesticides for humans using intelligent consensus predictions

Direct or indirect consumption of pesticides and their related products by humans and other living organisms without safe dosing may pose a health risk. The risk may arise after a short/long time which depends on the nature and amount of chemicals consumed. Therefore, the maximum acceptable daily intake of chemicals must be calculated to prevent these risks. In the present work, regression-based quantitative structure-activity relationship (QSAR) models were developed using 39 pesticides with maximum acceptable daily intake (MADI) for humans as the endpoint. From the statistical results (R2 = 0.674-0.712, QLOO2 = 0.553-0.580, Q(F1)2 = 0.544-0.611, and Q(F2)2 = 0.531-0.599), it can be inferred that the developed models were robust, reliable, reproducible, accurate, and predictive. Intelligent Consensus Prediction (ICP) was employed to improve the external predictivity (Q(F1)2 =0.579-0.657 and Q(F2)2 = 0.563-0.647) of the models. Some of the chemical markers responsible for toxicity enhancement are the presence of unsaturated bonds, lipophilicity, presence of C< (double bond-single bond-single bonded carbon), and the presence of sulphur and phosphate bonds at the topological distances 1 and 6, while the presence of hydrophilic groups and short chain fragments reduces the toxicity. The Pesticide Properties Database (PPDB) (1694 pesticides) was also screened with the developed models. Hence, this research work will be helpful for the toxicity assessment of pesticides before their synthesis, the development of eco-friendly and safer pesticides, and data-gap filling reducing the time, cost, and animal experimentation. Thus, this study might hold promise for future potential MADI assessment of pesticides and provide a meaningful contribution to the field of risk assessment.

Estrogen Receptor Alpha Binders for Hormone-Dependent Forms of Breast Cancer: e-QSAR and Molecular Docking Supported by X-ray Resolved Structures

Cancer, a life-disturbing and lethal disease with a high global impact, causes significant economic, social, and health challenges. Breast cancer refers to the abnormal growth of cells originating from breast tissues. Hormone-dependent forms of breast cancer, such as those influenced by estrogen, prompt the exploration of estrogen receptors as targets for potential therapeutic interventions. In this study, we conducted e-QSAR molecular docking and molecular dynamics analyses on a diverse set of inhibitors targeting estrogen receptor alpha (ER-α). The e-QSAR model is based on a genetic algorithm combined with multilinear regression analysis. The newly developed model possesses a balance between predictive accuracy and mechanistic insights adhering to the OECD guidelines. The e-QSAR model pointed out that sp2-hybridized carbon and nitrogen atoms are important atoms governing binding profiles. In addition, a specific combination of H-bond donors and acceptors with carbon, nitrogen, and ring sulfur atoms also plays a crucial role. The results are supported by molecular docking, MD simulations, and X-ray-resolved structures. The novel results could be useful for future drug development for ER-α.

Prioritization of the ecotoxicological hazard of PAHs towards aquatic species spanning three trophic levels using 2D-QSTR, read-across and machine learning-driven modelling approaches

Polycyclic aromatic hydrocarbons (PAHs) represent a common group of environmental pollutants that endanger various aquatic organisms via various pathways. To better prioritize the ecotoxicological hazard of PAHs to aquatic environment, we used 2D descriptors-based quantitative structure-toxicity relationship (QSTR) to assess the toxicity of PAHs toward six aquatic model organisms spanning three trophic levels. According to strict OECD guideline, six easily interpretable, transferable and reproducible 2D-QSTR models were constructed with high robustness and reliability. A mechanistic interpretation unveiled the key structural factors primarily responsible for controlling the aquatic ecotoxicity of PAHs. Furthermore, quantitative read-across and different machine learning approaches were employed to validate and optimize the modelling approach. Importantly, the optimum QSTR models were further applied for predicting the ecotoxicity of hundreds of untested/unknown PAHs gathered from Pesticide Properties Database (PPDB). Especially, we provided a priority list in terms of the toxicity of unknown PAHs to six aquatic species, along with the corresponding mechanistic interpretation. In summary, the models can serve as valuable tools for aquatic risk assessment and prioritization of untested or completely new PAHs chemicals, providing essential guidance for formulating regulatory policies.

Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across

We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.

Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA

Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.

QSAR modeling approaches to identify a novel ACE2 inhibitor that selectively bind with the C and N terminals of the ectodomain

Due to the high rates of drug development failure and the massive expenses associated with drug discovery, repurposing existing drugs has become more popular. As a result, we have used QSAR modelling on a large and varied dataset of 657 compounds in an effort to discover both explicit and subtle structural features requisite for ACE2 inhibitory activity, with the goal of identifying novel hit molecules. The QSAR modelling yielded a statistically robust QSAR model with high predictivity (R2tr=0.84, R2ex=0.79), previously undisclosed features, and novel mechanistic interpretations. The developed QSAR model predicted the ACE2 inhibitory activity (PIC50) of 1615 ZINC FDA compounds. This led to the detection of a PIC50 of 8.604 M for the hit molecule (ZINC000027990463). The hit molecule's docking score is -9.67 kcal/mol (RMSD 1.4). The hit molecule revealed 25 interactions with the residue ASP40, which defines the N and C termini of the ectodomain of ACE2. The HIT molecule conducted more than thirty contacts with water molecules and exhibited polar interaction with the ARG522 residue coupled with the second chloride ion, which is 10.4 nm away from the zinc ion. Both molecular docking and QSAR produced comparable findings. Moreover, MD simulation and MMGBSA studies verified docking analysis. The MD simulation showed that the hit molecule-ACE2 receptor complex is stable for 400 ns, suggesting that repurposed hit molecule 3 is a viable ACE2 inhibitor.

Mechanistic QSAR modeling derived virtual screening, drug repurposing, ADMET and in-vitro evaluation to identify anticancer lead as lysine-specific demethylase 5a inhibitor

A lysine-specific demethylase is an enzyme that selectively eliminates methyl groups from lysine residues. KDM5A, also known as JARID1A or RBP2, belongs to the KDM5 Jumonji histone demethylase subfamily. To identify novel molecules that interact with the LSD5A receptor, we created a quantitative structure-activity relationship (QSAR) model. A group of 435 compounds was used in a study of the quantitative relationship between structure and activity to guess the IC50 values for blocking LASD5A. We used a genetic algorithm-multilinear regression-based quantitative structure-activity connection model to forecast the bioactivity (PIC50) of 1615 food and drug administration pharmaceuticals from the zinc database with the goal of repurposing clinically used medications. We used molecular docking, molecular dynamic simulation modelling, and molecular mechanics generalised surface area analysis to investigate the molecule's binding mechanism. A genetic algorithm and multi-linear regression method were used to make six variable-based quantitative structure-activity relationship models that worked well (R2 = 0.8521, Q2LOO = 0.8438, and Q2LMO = 0.8414). ZINC000000538621 was found to be a new hit against LSD5A after a quantitative structure-activity relationship-based virtual screening of 1615 zinc food and drug administration compounds. The docking analysis revealed that the hit molecule 11 in the KDM5A binding pocket adopted a conformation similar to the pdb-6bh1 ligand (docking score: -8.61 kcal/mol). The results from molecular docking and the quantitative structure-activity relationship were complementary and consistent. The most active lead molecule 11, which has shown encouraging results, has good absorption, distribution, metabolism, and excretion (ADME) properties, and its toxicity has been shown to be minimal. In addition, the MTT assay of ZINC000000538621 with MCF-7 cell lines backs up the in silico studies. We used molecular mechanics generalise borne surface area analysis and a 200-ns molecular dynamics simulation to find structural motifs for KDM5A enzyme interactions. Thus, our strategy will likely expand food and drug administration molecule repurposing research to find better anticancer drugs and therapies.Communicated by Ramaswamy H. Sarma.

Integrative Approach for Designing Novel Triazole Derivatives as α-Glucosidase Inhibitors: QSAR, Molecular Docking, ADMET, and Molecular Dynamics Investigations

In response to the increasing prevalence of diabetes mellitus and the limitations associated with the current treatments, there is a growing need to develop novel medications for this disease. This study is focused on creating new compounds that exhibit a strong inhibition of alpha-glucosidase, which is a pivotal enzyme in diabetes control. A set of 33 triazole derivatives underwent an extensive QSAR analysis, aiming to identify the key factors influencing their inhibitory activity against α-glucosidase. Using the multiple linear regression (MLR) model, seven promising compounds were designed as potential drugs. Molecular docking and dynamics simulations were employed to shed light on the mode of interaction between the ligands and the target, and the stability of the obtained complexes. Furthermore, the pharmacokinetic properties of the designed compounds were assessed to predict their behavior in the human body. The binding free energy was also calculated using MMGBSA method and revealed favorable thermodynamic properties. The results highlighted three novel compounds with high biological activity, strong binding affinity to the target enzyme, and suitability for oral administration. These results offer interesting prospects for the development of effective and well-tolerated medications against diabetes mellitus.

The pursuit of accurate predictive models of the bioactivity of small molecules

Property prediction is a key interest in chemistry. For several decades there has been a continued and incremental development of mathematical models to predict properties. As more data is generated and accumulated, there seems to be more areas of opportunity to develop models with increased accuracy. The same is true if one considers the large developments in machine and deep learning models. However, along with the same areas of opportunity and development, issues and challenges remain and, with more data, new challenges emerge such as the quality and quantity and reliability of the data, and model reproducibility. Herein, we discuss the status of the accuracy of predictive models and present the authors' perspective of the direction of the field, emphasizing on good practices. We focus on predictive models of bioactive properties of small molecules relevant for drug discovery, agrochemical, food chemistry, natural product research, and related fields.

In silico soil degradation and ecotoxicity analysis of veterinary pharmaceuticals on terrestrial species: first report

With the aim of persistence property analysis and ecotoxicological impact of veterinary pharmaceuticals on different terrestrial species, different classes of veterinary pharmaceuticals (n = 37) with soil degradation property (DT50) were gathered and subjected to QSAR and q-RASAR model development. The models were developed from 2D descriptors under organization for economic cooperation and development guidelines with the application of multiple linear regressions along with genetic algorithm. All developed QSAR and q-RASAR were statistically significant (Internal = R2adj: 0.721-0.861, Q2LOO: 0.609-0.757, and external = Q2Fn = 0.597-0.933, MAEext = 0.174-0.260). Further, the leverage approach of applicability domain assured the model's reliability. The veterinary pharmaceuticals with no experimental values were classified based on their persistence level. Further, the terrestrial toxicity analysis of persistent veterinary pharmaceuticals was done using toxicity prediction by computer assisted technology and in-house built quantitative structure toxicity relationship models to prioritize the toxic and persistent veterinary pharmaceuticals. This study will be helpful in estimation of persistence and toxicity of existing and upcoming veterinary pharmaceuticals.

Correlation intensity index-index of ideality of correlation: A hyphenated target function for furtherance of MAO-B inhibitory activity assessment

Monoamine oxidases are the enzymes involved in the management of brain homeostasis through oxidative deamination of monoamines such as neurotransmitters, tyramine etc. The excessive production of monoamine oxidase-B specifically results in numerous neurodegenerative disorders like Alzheimer's and Parkinson's diseases. Inhibitors of monoamine oxidase-B are applied in the management of these disorders. Here in this article we have developed robust hybrid descriptor based QSAR models related to 123 monoamine oxidase-B inhibitors through CORAL software by means of Monte Carlo optimization method. Three target functions were applied to prepare QSAR models and three splits were made for each target function. The most reliable, robust and better predictive QSAR models were developed with TF3 (correlation intensity index -index of ideality of correlation). Correlation intensity index showed positive effect on QSAR models. The structural features obtained from the QSAR modeling were incorporated in newly designed molecules and exhibited positive effect on their endpoint. Significant binding interactions were represented by these molecules in docking studies. Molecule B5 displayed prominent pIC50 (8.3) and binding affinity (-11.5 kcal mol-1) towards monoamine oxidase-B.

The use of simple structural parameters of organic compounds to assess their PUF-air partition coefficients

A novel approach is introduced for the reliable prediction of PUF-air partition coefficients of organic compounds, which can determine the environmental fate of organic compounds during interactions with air, soil, and water. The biggest accessible measured data of PUF-air partition coefficients for 170 chemicals are used to develop and test the novel model. In comparison to available quantitative structure-property relationship (QSPR) methods for the prediction of PUF-air partition coefficients that need complex descriptors, the here used descriptors are simpler. The assessed various statistical factors of the simple method containing 147 (training) and 23 (test) organic compounds can verify the external and internal cross-validations. Various statistical parameters confirm the high reliability of the novel model as compared with the outputs of complex multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM) methods. The values of R-squared (R2), and root mean square error (RMSE) of the new model are for training/test sets are 0.924/0.894 and 0.374/0.318, respectively. Meanwhile, R2 and RMSE values for three comparative models training/test sets are (i) MLR: 0.848/0.670 (R2) and 0.531/0.573 (RMSE); (ii) ANN: 0.902/0.664 (R2) and 0.425/0.560 (RMSE); (iii) SVM: 0.935/0.794 (R2) and 0.351/0.419 (RMSE). Thus, the new model the simplest approach with higher reliability in comparison to the best available methods.

Target analysis, occurrence and cytotoxicity of halogenated polyhydroxyphenols as emerging disinfection byproducts in drinking water

Halogenated aromatic disinfection byproducts (DBPs) in drinking water, such as halogenated phenols, have received widespread attention due to their high toxicity and ubiquitous occurrence in recent years. This study identified a group of emerging halogenated aromatic DBPs, known as halogenated polyhydroxyphenols (HPPs), and investigated their occurrence and cytotoxicity. We developed a highly sensitive solid-phase extraction ultra-performance liquid chromatography-tandem mass spectrometry (SPE-UPLC-MS/MS) method under multiple reaction monitoring (MRM) mode, with recoveries ranging from 86 to 115% and method detection limits (MDLs) ranging from 0.10 to 1.87 ng/L for the analysis of 15 HPPs. Eleven of these HPP DBPs were detected in collected drinking water samples using this method with detection frequencies ranging from 14 to 100% and a maximum concentration of 24 ng/L. The IC50 of the 15 HPPs in Chinese hamster ovary (CHO-K1) cells were ranged from 15.13 µM to 6.08×103 µM. The tested HPPs with -CHO substitution exhibited higher cytotoxicity compared to those with -COOH substitution. The TIC-Tox values of HPPs were calculated to be higher than those of HPs, indicating a potential necessity to pay attention to HPP DBPs. A quantitative structure-activity relationship (QSAR) model was developed for the cytotoxicity of HPPs, which was shown to be significantly associated with acid dissociation constant (pKa) and total valence connectivity (TVCon). To the best of our knowledge, this study reported the analysis, occurrence, and cytotoxicity of HPP DBPs in drinking water for the first time.

Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ

Polo-like kinase 1 (PLK1) and p38γ mitogen-activated protein kinase (p38γ) play important roles in cancer pathogenesis by controlling cell cycle progression and are therefore attractive cancer targets. The design of multitarget inhibitors may offer synergistic inhibition of distinct targets and reduce the risk of drug-drug interactions to improve the balance between therapeutic efficacy and safety. We combined deep-learning-based quantitative structure-activity relationship (QSAR) modeling and hybrid-based consensus scoring to screen for inhibitors with potential activity against the targeted proteins. Using this combination strategy, we identified a potent PLK1 inhibitor (compound 4) that inhibited PLK1 activity and liver cancer cell growth in the nanomolar range. Next, we deployed both our QSAR models for PLK1 and p38γ on the Enamine compound library to identify dual-targeting inhibitors against PLK1 and p38γ. Likewise, the identified hits were subsequently subjected to hybrid-based consensus scoring. Using this method, we identified a promising compound (compound 14) that could inhibit both PLK1 and p38γ activities. At nanomolar concentrations, compound 14 inhibited the growth of human hepatocellular carcinoma and hepatoblastoma cells in vitro. This study demonstrates the combined screening strategy to identify novel potential inhibitors for existing targets.

Identification of 1,3,4-oxadiazoles as tubulin-targeted anticancer agents: a combined field-based 3D-QSAR, pharmacophore model-based virtual screening, molecular docking, molecular dynamics simulation, and density functional theory calculation approach

Cancer is one of the most prominent causes of death worldwide and tubulin is a crucial protein of cytoskeleton that maintains essential cellular functions including cell division as well as cell signalling, that makes an attractive drug target for cancer drug development. 1,3,4-oxadiazoles disrupt microtubule causing G2-M phase cell cycle arrest and provide anti-proliferative effect. In this study, field-based 3D-QSAR models were developed using 62 bioactive anti-tubulin 1,3,4-oxadiazoles. The best model characterized by PLS factor 7 was rigorously validated using various statistical parameters. Generated 3D-QSAR model having high degree of confidence showed favourable and unfavourable contours around 1,3,4-oxadiazole core that assisted in defining proper spatial positioning of desired functional groups for better bioactivity. A five featured pharmacophore model (AAHHR_1) was developed using same ligand library and validated through enrichment analysis (BEDROC160.9 value = 0.59, Average EF 1% = 27.05, and AUC = 0.74). Total 30,212 derivatives of 1,3,4-oxadiazole obtained from PubChem database was prefiltered through validated pharmacophore model and docked in XP mode on binding cavity of tubulin protein (PDB code: 1SA0) which led into the identification of 11 HITs having docking scores between -7.530 and -9.719 kcal/mol while the reference compound Colchicine exerted docking score of -7.046 kcal/mol. Following the analysis of MM-GBSA and ADME studies, HIT1 and HIT4 emerged as the two promising hits. To verify their thermodynamic stability at the target site, molecular dynamic simulations were carried out. Both HITs were further subjected to DFT analysis to determine their HOMO-LUMO energy gap for ensuring their biological feasibility. Finally, molecular docking based structural exploration for 1,3,4-oxadiazoles to set up a lead of Formula I for further advancements of tubulin polymerization inhibitors as anti-cancer agents.Communicated by Ramaswamy H. Sarma.

QSPR models to predict the physical hazards of mixtures: a state of art

Physical hazards of chemical mixtures, associated for example with their fire or explosion risks, are generally characterized using experimental tools. These tests can be expensive, complex, long to perform and even dangerous for operators. Therefore, for several years and especially with the implementation of the REACH regulation, predictive methods like quantitative structure-property relationships have been encouraged as alternatives tests to determine (eco)toxicological but also physical hazards of chemical substances. Initially, these approaches were intended for pure products, by considering a molecular similarity principle. However, additional to those for pure products, QSPR models for mixtures recently appeared and represent an increasing field of research. This study proposes a state of the art of existing QSPR models specifically dedicated to the prediction of the physical hazards of mixtures. Identified models have been analysed on the key elements of model development (experimental data and fields of application, descriptors used, development and validation methods). It draws up an overview of the potential and limitations of current models as well as areas of progress towards enlarged deployment as a complement to experimental characterizations, for example in the search for safer substances (according to safety-by-design concepts).

Ecotoxicological QSAR study of fused/non-fused polycyclic aromatic hydrocarbons (FNFPAHs): Assessment and priority ranking of the acute toxicity to Pimephales promelas by QSAR and consensus modeling methods

Fused/non-fused polycyclic aromatic hydrocarbons (FNFPAHs) have a variety of toxic effects on ecosystems and human body, but the acquisition of their toxicity data is greatly limited by the limited resources available. Here, we followed the EU REACH regulation and used Pimephales promelas as a model organism to investigate the quantitative structure-activity relationship (QSAR) between the FNFPAHs and their toxicity for the aquatic environment for the first time. We developed a single QSAR model (SM1) containing five simple and interpretable 2D molecular descriptors, which met the validation of OECD QSAR-related principles, and analyzed their mechanistic relationships with toxicity in detail. The model had good degree of fitting and robustness, and had better external prediction performance (MAE_test = 0.4219) than ECOSAR model (MAE_test = 0.5614). To further enhance its prediction accuracy, the three qualified single models (SMs) were used for constructing consensus models (CMs), the best one CM2 (MAE_test = 0.3954) had a significantly higher prediction accuracy for test compounds than SM1, and also outperformed the T.E.S.T. consensus model (MAE_test = 0.4233). Subsequently, the toxicity of 252 true external FNFPAHs from Pesticide Properties Database (PPDB) was predicted by SM1, the prediction results showed that 94.84 % compounds were reliably predicted within the model's application domain (AD). We also applied the best CM2 to predict the untested 252 FNFPAHs. Furthermore, we provided a mechanistic analysis and explanation for pesticides ranked as top 10 most toxic FNFPAHs. In summary, all developed QSAR and consensus models can be used as efficient tools for predicting the acute toxicity of unknown FNFPAHs to Pimephales promelas, thus being important for the risk assessment and regulation of FNFPAHs contamination in aquatic environment.

Predictive in silico models for aquatic toxicity of cosmetic and personal care additive mixtures

As emerging environmental contaminants, cosmetic and personal care additives (CPCAs) may have less oversight than other consumer products. Their continuous release and pseudopersistence could cause long-term harm to the aquatic environment. Since CPCAs generally exist in the form of mixtures in the environment, prediction and analysis of their mixture toxicity are crucial for ecological risk assessment. In this study, the acute toxicity of five typical CPCA mixtures to Daphnia magna was tested. The combined toxicity of binary mixtures was examined with the traditional concentration addition (CA) and independent action (IA) model. Overall, the synergistic effect of the five CPCAs may be caused mainly by methylparaben. In addition, reliable approaches for quantitative structure-activity relationship (QSAR) model development were explored. Specifically, 18 QSAR models were developed by three dataset partitioning techniques (Kennard-Stone's algorithm division, Euclidean distance based division, and sorted activity based division), two descriptor filtering methods (genetic algorithm and stepwise multiple linear regression) and three regression methods (multiple linear regression, partial least squares and support vector machine). Sixteen equations were applied for the calculation of the mixture descriptors to screen the functional expression of the mixture descriptors with the largest contribution to the mixture toxicity. A new comprehensive parameter that integrates internal and external validation was proposed for QSAR models evaluation. The mixture toxicity is mainly related the 3D distribution of atomic masses and the spatial distribution of the molecule electronic properties. Rigorously validated and externally predictive QSAR models were developed for predicting the toxicity of binary CPCAs mixtures with any ratio, in the applicability domain. The best possible work frame for construction and validation of QSAR models to provide reliable predictions on the mixture toxicity was proposed.

Application of unsupervised and supervised learning to a material attribute database of tablets produced at two different granulation scales

The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and data were collected according to the design of experiments at different scales. In total, 38 different tablets were prepared, and the tensile strength (TS) and dissolution rate after 10 min (DS10) were measured. In addition, 15 material attributes (MAs) related to particle size distribution, bulk density, elasticity, plasticity, surface properties, and moisture content of granules were evaluated. By using unsupervised learning including principal component analysis and hierarchical cluster analysis, the regions of tablets produced at each scale were visualized. Subsequently, supervised learning with feature selection including partial least squares regression with variable importance in projection and elastic net were applied. The constructed models could predict the TS and DS10 from the MAs and the compression force with high accuracy (R²= 0.777 and 0.748, respectively), independent of scale. In addition, important factors were successfully identified. ML can be used for better understanding of similarity/dissimilarity between scales, for constructing predictive models of critical quality attributes, and for determining critical factors.

Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies

Protein kinase p38γ is an attractive target against cancer because it plays a pivotal role in cancer cell proliferation by phosphorylating the retinoblastoma tumour suppressor protein. Therefore, inhibition of p38γ with active small molecules represents an attractive alternative for developing anti-cancer drugs. In this work, we present a rigorous and systematic virtual screening framework to identify potential p38γ inhibitors against cancer. We combined the use of machine learning-based quantitative structure activity relationship modelling with conventional computer-aided drug discovery techniques, namely molecular docking and ligand-based methods, to identify potential p38γ inhibitors. The hit compounds were filtered using negative design techniques and then assessed for their binding stability with p38γ through molecular dynamics simulations. To this end, we identified a promising compound that inhibits p38γ activity at nanomolar concentrations and hepatocellular carcinoma cell growth in vitro in the low micromolar range. This hit compound could serve as a potential scaffold for further development of a potent p38γ inhibitor against cancer.

A QSAR study to predict the survival motor neuron promoter activity of candidate diaminoquinazoline derivatives for the potential treatment of spinal muscular atrophy

Spinal Muscular Atrophy is a genetic neuromuscular disease that leads to muscle weakness and atrophy and it is characterized by the loss of α-motor neurons in the spinal cord's anterior horn cells. The disease appears due to low levels of the survival motor neuron protein. There are continuing clinical trials for the treatment of Spinal Muscular Atrophy. Quinazoline-based compounds are promising since they were tested on fibroblasts derived from the patients and found to increase the survival motor neuron protein levels. In this study, using multiple linear regression, we generated robust and valid quantitative structure- activity relationship models to predict the survival motor neuron-2 promoter activity of the new candidate compounds using the experimental survival motor neuron-2 promoter activity values of 2,4-diaminoquinazoline derivatives taken from the literature. The novel compounds designed by combining the pyrido[1,2-α]pyrimidin-4-one moeity of the known drug Risdiplam with that of 2,4 - diaminoquinazoline scaffold were predicted to exhibit strong promoter activities.

Development of reliable quantitative structure-toxicity relationship models for toxicity prediction of benzene derivatives using semiempirical descriptors

The Health and environmental hazards of benzene and nitrobenzene (NB) derivatives have remained a topic of interest of researchers. In silico methods for prediction of toxicity of chemicals have proved their worth in accurate forecast of environmental as well as health toxicity and are strongly recommended by regulatory authorities. Two quantitative structure-toxicity relationship (QSTR) models explaining Scenedesmus obliquus toxicity trends among 39 benzene derivatives and Tetrahymena pyriformis toxicity of 103 NB and 392 benzene derivatives are developed using semiempirical quantum chemical parameters. The best constructed QSTR models have good fitting ability (R² = 0.8053, 0.7591, and 0.8283) and robustness (Q²_LOO = 0.7507, 0.7227, and 0.8194; Q²_LMO = 0.7338, 0.7153, and 0.8172). The external predictivity of all the models are quite good (R²_EXT = 0.8256, 0.9349, and 0.8698). Electronegativity, Cosmo volume, total energy, and molecular weight are responsible for the increase and decrease of toxicity of benzene derivatives against S. obliquus while electronegativity, electrophilicity index, the heat of formation, total energy, hydrophobicity, and cosmo volume are responsible for modulation of toxicity of NB and benzene derivatives toward T. pyriformis. These models fulfill the requirements of all the five OECD principles.

A comprehensive evaluation of liposome/water partition coefficient prediction models based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method: Challenges from different descriptor dimension reduction methods and machine learning algorithms

The liposome/water partition coefficient (K_lip/w) is a key parameter to evaluate the bioaccumulation potential of pollutants. Considering that it is difficult to determine the K_lip/w values of all pollutants through experiments, researchers gradually developed models to predict it. However, there is currently no research on how to comprehensively evaluate prediction models and recommend a compelling optimal modeling method. To remedy the defect of single parameters in a traditional model comparison, the TOPSIS evaluation method, based on entropy weight, was first proposed. We use this method to comprehensively evaluate models from multiple angles in this study. Thirty QSPR models, including 3 descriptor dimension reduction methods and 10 algorithms (belonging to 4 tribes), were used to predict K_lip/w and verify the effectiveness of the comprehensive assessment method. The results showed that RF (descriptor dimension reduction method), symbolism (tribes) and RF (algorithm) exhibited significant advantages in establishing the K_lip/w value prediction model. At present, the application of TOPSIS in environmental model evaluations is almost absent. We hope that the proposed TOPSIS evaluation method can be applied to more chemical datasets and provide a more systematic and comprehensive basis for the application of the QSPR model in environmental studies and other fields.

Ecotoxicological QSAR modelling of the acute toxicity of fused and non-fused polycyclic aromatic hydrocarbons (FNFPAHs) against two aquatic organisms: Consensus modelling and comparison with ECOSAR

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 LC₅₀) and O. mykiss (96 h LC₅₀). 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_LOO² = 0.7834 for D. magna and Q_LOO² = 0.8162 for O. mykiss), have good internal fitness (R² = 0.8159 for D. magna and R² = 0.8626 for O. mykiss and external predictive ability (D. magna: R_test² = 0.8259, Q_Fn² = 0.7640∼0.8140, CCC_test = 0.8972; O. mykiss:R_test² = 0.8077, Q_Fn² = 0.7615∼0.7722, CCC_test = 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.

Prediction of organic contaminant rejection by nanofiltration and reverse osmosis membranes using interpretable machine learning models

Efficiency improvement in contaminant removal by nanofiltration (NF) and reverse osmosis (RO) membranes is a multidimensional process involving membrane material selection and experimental condition optimization. It is unrealistic to explore the contributions of diverse influencing factors to the removal rate by trial-and-error experimentation. However, the advanced machine learning (ML) method is a powerful tool to simulate this complex decision-making process. Here, 4 traditional learning algorithms (MLR, SVM, ANN, kNN) and 4 ensemble learning algorithms (RF, GBDT, XGBoost, LightGBM) were applied to predict the removal efficiency of contaminants. Results reported here demonstrate that ensemble models showed significantly better predictive performance than traditional models. More importantly, this study achieved a compelling tradeoff between accuracy and interpretability for ensemble models with an effective model interpretation approach, which revealed the mutual interaction mechanism between the membrane material, contaminants and experimental conditions in membrane separation. Additionally, feature selection was for the first time achieved based on the aforementioned model interpretation method to determine the most important variable influencing the contaminant removal rate. Ultimately, the four ensemble models retrained by the selected variables achieved distinguished prediction performance (R²_adj = 92.4 %-99.5 %). MWCO (membrane molecular weight cut-off), McGowan volume of solute (V) and molecular weight (MW) of the compound were demonstrated to be the most important influencing factors in contaminant removal by the NF and RO processes. Overall, the proposed methods in this study can facilitate versatile complex decision-making processes in the environmental field, particularly in contaminant removal by advanced physicochemical separation processes.

SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors

A growing body of preclinical evidence recognized selective sirtuin 2 (SIRT2) inhibitors as novel therapeutics for treatment of age-related diseases. However, none of the SIRT2 inhibitors have reached clinical trials yet. Transformative potential of machine learning (ML) in early stages of drug discovery has been witnessed by widespread adoption of these techniques in recent years. Despite great potential, there is a lack of robust and large-scale ML models for discovery of novel SIRT2 inhibitors. In order to support virtual screening (VS), lead optimization, or facilitate the selection of SIRT2 inhibitors for experimental evaluation, a machine-learning-based tool titled SIRT2i_Predictor was developed. The tool was built on a panel of high-quality ML regression and classification-based models for prediction of inhibitor potency and SIRT1-3 isoform selectivity. State-of-the-art ML algorithms were used to train the models on a large and diverse dataset containing 1797 compounds. Benchmarking against structure-based VS protocol indicated comparable coverage of chemical space with great gain in speed. The tool was applied to screen the in-house database of compounds, corroborating the utility in the prioritization of compounds for costly in vitro screening campaigns. The easy-to-use web-based interface makes SIRT2i_Predictor a convenient tool for the wider community. The SIRT2i_Predictor's source code is made available online.

Contribution of Reliable Chromatographic Data in QSAR for Modelling Bisphenol Transport across the Human Placenta Barrier

Regulatory measures and public concerns regarding bisphenol A (BPA) have led to its replacement by structural analogues, such as BPAF, BPAP, BPB, BPF, BPP, BPS, and BPZ. However, these alternatives are under surveillance for potential endocrine disruption, particularly during the critical period of fetal development. Despite their structural analogies, these BPs differ greatly in their placental transport efficiency. For predicting the fetal exposure of this important class of emerging contaminants, quantitative structure-activity relationship (QSAR) studies were developed to model and predict the placental clearance indices (CI). The most usual input parameters were molecular descriptors obtained by modelling, but for bisphenols (BPs) with structural similarities or heteroatoms such as sulfur, these descriptors do not contrast greatly. This study evaluated and compared the capacity of QSAR models based either on molecular or chromatographic descriptors or a combination of both to predict the placental passage of BPs. These chromatographic descriptors include both the retention mechanism and the peak shape on columns that reflect specific molecular interactions between solute and stationary and mobile phases and are characteristic of the molecular structure of BPs. The chromatographic peak shape such as the asymmetry and tailing factors had more influence on predicting the placental passage than the usual retention parameters. Furthermore, the QSAR model, having the best prediction capacity, was obtained with the chromatographic descriptors alone and met the criteria of internal and cross validation. These QSAR models are crucial for predicting the fetal exposure of this important class of emerging contaminants.

Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach

With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q²_LOO = 0.620-0.663) and externally (Q²F_n = 0.693-0.868, CCC_ext = 0.843-0.877). The leverage approach of applicability domain (AD) and prediction reliability indicator assured the reliability of the developed models. The mechanistic interpretation highlighted that the presence of SO₂, F and aromatic rings influenced the toxicity of pesticides towards PS species while the presence of alkyl, alkyl halide, aromatic rings and carbonyl was responsible for the toxicity of pesticides towards SS species. Additionally, we have reported the application of developed models to pesticides without experimental value and the cumulative toxicity of pesticides on the aquatic environment by using principal component analysis (PCA). The reliable prediction and prioritization of toxic compounds from the developed models will be useful in the aquatic toxicity assessment of pesticides.

First report on soil ecotoxicity prediction against Folsomia candida using intelligent consensus predictions and chemical read-across

Soil invertebrates serve as an outstanding biological indicator of the terrestrial ecosystem and overall soil quality, considering their high sensitivity when compared to other indicators of soil quality. In this study, the available soil ecotoxicity data (pEC50) against the soil invertebrate Folsomia candida (C. name: Springtail) (n = 45) were collated from the database of ECOTOX (cfpub.epa.gov/ecotox) and subjected to QSAR analysis using 2D descriptors. Four partial least squares (PLS) models were built based on the features selected through genertic algorithm followed by the best subset selection. These four models were then used as inputs for Intelligent Consensus Predictor version 1.2 (PLS version) to get the final consensus predictions, using the best selection of predictions (compound-wise) from four "qualified" individual models. Both internal and external validations metrics of the consensus predictions are well- balanced and within the acceptable range as per the OECD criteria. The consensus model was found to be better than the previous developed models for this endpoint. Predictions were also made using the Chemical Read-across approach, which showed even better external validation metric values than the consensus predictions. From the selected features in the QSAR models, it has been found out that molecular weight and presence of a di-thiophosphate group, electron donor groups, and polyhalogen substitutions have a significant impact on the soil ecotoxicity. The soil ecotoxicological risk assessment of organic chemicals can therefore be prioritized by these features. The models developed from diverse structural organic compounds can be applied to any new query compound for data gap filling.

Pharmacophore Synergism in Diverse Scaffold Clinches in Aurora Kinase B

Aurora kinase B (AKB) is a crucial signaling kinase with an important role in cell division. Therefore, inhibition of AKB is an attractive approach to the treatment of cancer. In the present work, extensive quantitative structure-activity relationships (QSAR) analysis has been performed using a set of 561 structurally diverse aurora kinase B inhibitors. The Organization for Economic Cooperation and Development (OECD) guidelines were used to develop a QSAR model that has high statistical performance (R²_tr = 0.815, Q²_LMO = 0.808, R²_ex = 0.814, CCC_ex = 0.899). The seven-variable-based newly developed QSAR model has an excellent balance of external predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The QSAR analysis successfully identifies not only the visible pharmacophoric features but also the hidden features. The analysis indicates that the lipophilic and polar groups-especially the H-bond capable groups-must be present at a specific distance from each other. Moreover, the ring nitrogen and ring carbon atoms play important roles in determining the inhibitory activity for AKB. The analysis effectively captures reported as well as unreported pharmacophoric features. The results of the present analysis are also supported by the reported crystal structures of inhibitors bound to AKB.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (K_d). Almost all quantitative structure-property relationship (QSPR) models that describe K_d apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (K_PE-w), PE-seawater (K_PE-sw), PVC-water (K_PVC-w) and PP-seawater (K_PP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting K_d more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with K_PE-w, K_PE-sw, K_PVC-w and K_PP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (R_adj²: 0.907-0.999), robustness (Q_BOOT²: 0.900-0.937) and predictability (R_ext²: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Predicting the Temperature Dependence of the Octanol–Air Partition Ratio: A New Model for Estimating $$\Delta {U^{ \circ}_{\text{OA}}}$$

The octanol–air partition ratio (KOA) describes the partitioning of a chemical between air and octanol and is often used to approximate other partitioning phenomena in environmental chemistry (e.g., blood–air, atmospheric particulate matter–air, polyurethane foam-air). Such partitioning processes often occur at environmental temperatures other than 25 °C. Enthalpies $$\Delta {H^{ \circ}_{\text{OA}}}$$ Δ H OA ∘ or internal energies $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ of phase transfer are used to express the temperature dependence of the KOA. Existing poly-parameter linear free energy relationships (ppLFERs) for predicting $$\Delta {H^{ \circ}_{\text{OA}}}$$ Δ H OA ∘ were developed using a relatively small dataset. In this work we utilize a recently developed comprehensive KOA database to create and curate a $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ dataset containing 195 chemicals and use this dataset in the development of new predictive equations. Using the QSAR development platform QSARINS we evaluate the use of Abraham descriptors, other molecular descriptors, and the log10KOA at 25 °C as variables in different multilinear regression equations for $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ . The $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ of neutral organic chemicals can be reliably predicted using only the log10KOA (RMSEEXT = 6.86 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2  = 0.94), only the solute’s hydrogen acidity A and the logarithm of the hexadecane–air partition ratio L (RMSEEXT = 7.23 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2  = 0.93), or A and log10KOA (RMSEEXT = 6.76 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2  = 0.95).

Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches

Globally, pesticides are toxic substances with wide applications. However, the widespread use of pesticides has received increasing attention from regulatory agencies due to their various acute and chronic effects on multiple organisms. In this study, Quantitative Structure-Toxicity Relationship (QSTR) models were established using Multiple Linear Regression (MLR) and five Machine Learning (ML) algorithms to predict pesticide toxicity in Americamysis bahia. The most influential descriptors included in the MLR model are RBF, JGI2, nCbH, nRCOOR, nRSR, nPO4 and 'Cl-090', with positive contributions to the dependent variable (negative decimal logarithm of median lethal concentration at 96-h). The Random Forest (RF) regression model was superior amongst the five ML models. We observed higher values of R² (0.812) and lower values of RMSE (0.595) and MAE (0.462) in the cross-validation training set and external validation set. Similarly, this study had a high level of fitness and was internally robust and externally predictive compared to models presented in similar studies. The results suggest that the developed QSTR models are suitable for reliably predicting the aquatic toxicity of structurally diverse pesticides and can be used for screening, prioritising new pesticides, filling data gaps and overcoming the limitations of in vivo and in vitro tests.

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

Design of Novel IRAK4 Inhibitors Using Molecular Docking, Dynamics Simulation and 3D-QSAR Studies

Treatment of several autoimmune diseases and types of cancer has been an intense area of research over the past two decades. Many signaling pathways that regulate innate and/or adaptive immunity, as well as those that induce overexpression or mutation of protein kinases, have been targeted for drug discovery. One of the serine/threonine kinases, Interleukin-1 Receptor Associated Kinase 4 (IRAK4) regulates signaling through various Toll-like receptors (TLRs) and interleukin-1 receptor (IL1R). It controls diverse cellular processes including inflammation, apoptosis, and cellular differentiation. MyD88 gain-of-function mutations or overexpression of IRAK4 has been implicated in various types of malignancies such as Waldenström macroglobulinemia, B cell lymphoma, colorectal cancer, pancreatic ductal adenocarcinoma, breast cancer, etc. Moreover, over activation of IRAK4 is also associated with several autoimmune diseases. The significant role of IRAK4 makes it an interesting target for the discovery and development of potent small molecule inhibitors. A few potent IRAK4 inhibitors such as PF-06650833, RA9 and BAY1834845 have recently entered phase I/II clinical trial studies. Nevertheless, there is still a need of selective inhibitors for the treatment of cancer and various autoimmune diseases. A great need for the same intrigued us to perform molecular modeling studies on 4,6-diaminonicotinamide derivatives as IRAK4 inhibitors. We performed molecular docking and dynamics simulation of 50 ns for one of the most active compounds of the dataset. We also carried out MM-PBSA binding free energy calculation to identify the active site residues, interactions of which are contributing to the total binding energy. The final 50 ns conformation of the most active compound was selected to perform dataset alignment in a 3D-QSAR study. Generated RF-CoMFA (q² = 0.751, ONC = 4, r² = 0.911) model revealed reasonable statistical results. Overall results of molecular dynamics simulation, MM-PBSA binding free energy calculation and RF-CoMFA model revealed important active site residues of IRAK4 and necessary structural properties of ligand to design more potent IRAK4 inhibitors. We designed few IRAK4 inhibitors based on these results, which possessed higher activity (predicted pIC₅₀) than the most active compounds of the dataset selected for this study. Moreover, ADMET properties of these inhibitors revealed promising results and need to be validated using experimental studies.

Design and Synthesis of Aminopyrimidinyl Pyrazole Analogs as PLK1 Inhibitors Using Hybrid 3D-QSAR and Molecular Docking

Cancer continues to be one of the world’s most severe public health issues. Polo-like kinase 1 (PLK1) is one of the most studied members of the polo-like kinase subfamily of serine/threonine protein kinases. PLK1 is a key mitotic regulator responsible for cell cycle processes, such as mitosis initiation, bipolar mitotic spindle formation, centrosome maturation, the metaphase to anaphase transition, and mitotic exit, whose overexpression is often associated with oncogenesis. Moreover, it is also involved in DNA damage response, autophagy, cytokine signaling, and apoptosis. Due to its fundamental role in cell cycle regulation, PLK1 has been linked to various types of cancer onset and progression, such as lung, colon, prostate, ovary, breast cancer, melanoma, and AML. Hence, PLK1 is recognized as a critical therapeutic target in the treatment of various proliferative diseases. PLK1 inhibitors developed in recent years have been researched and studied through clinical trials; however, most of them have failed because of their toxicity and poor therapeutic response. To design more potent and selective PLK1 inhibitors, we performed a receptor-based hybrid 3D-QSAR study of two datasets, possessing similar common scaffolds. The developed hybrid CoMFA (q² = 0.628, r² = 0.905) and CoMSIA (q² = 0.580, r² = 0.895) models showed admissible statistical results. Comprehensive, molecular docking of one of the most active compounds from the dataset and hybrid 3D-QSAR studies revealed important active site residues of PLK1 and requisite structural characteristics of ligand to design potent PLK1 inhibitors. Based on this information, we have proposed approximately 38 PLK1 inhibitors. The newly designed PLK1 inhibitors showed higher activity (predicted pIC₅₀) than the most active compounds of all the derivatives selected for this study. We selected and synthesized two compounds, which were ultimately found to possess good IC₅₀ values. Our design strategy provides insight into development of potent and selective PLK1 inhibitors.

Assessment of perfluorohexane sulfonic acid (PFHxS)-related compounds degradation potential: Computational and experimental approaches

Perfluorohexane sulfonic acid (PFHxS) and PFHxS-related compounds are listed in Annex A of the Stockholm Convention without specific exemptions. Substances that potentially degrade to PFHxS are considered as their related compounds. Unfortunately, the degradation behavior of PFHxS precursors, an important basis for the corresponding chemical regulation, remains unclear. Herein, based on the hypothesis that bond dissociation enthalpy (BDE) is the determining factor for the degradation of PFHxS precursors, the BDE of PFHxS-related precursors to produceC6F13SO2-groups was calculated. In addition, quantitative structure-activity relationship models based on partial least squares, partial least squares discrimination analysis, and support vector machine algorithms were developed to predict the BDE of 48 PFHxS precursors and distinguish the precursors with different degradation potential. Subsequent photodegradation experiments demonstrated that the order of degradation rates was consistent with that predicted by theoretical models. Importantly, perfluorohexanoic acid (PFHxA) and perfluorobutanoic acid, and not PFHxS, were detected as the degradation products of potential PFHxS precursors. Sulfonamides, phenyl unit, and other radicals in the non-nucleus part of PFHxS precursors were identified as the critical molecular segments that affect their degradation potential. Ultimately, by comparing BDE values, it was theoretically speculated that PFHxS related compounds exhibit a greater potential to generate PFHxA than PFHxS. Results in this study indicated for the first time that not all the compounds containing C6F13SO2- groups were guaranteed to degrade into PFHxS under natural conditions.

QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads

ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm−multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69−0.87, F = 403.46−292.11, etc., internal validation parameters; Q2LOO = 0.69−0.86, Q2LMO = 0.69−0.86, CCCcv = 0.82−0.93, etc., or external validation parameters Q2F1 = 0.64−0.82, Q2F2 = 0.63−0.82, Q2F3 = 0.65−0.81, R2ext = 0.65−0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.

QSAR Evaluations to Unravel the Structural Features in Lysine-Specific Histone Demethylase 1A Inhibitors for Novel Anticancer Lead Development Supported by Molecular Docking, MD Simulation and MMGBSA

Using 84 structurally diverse and experimentally validated LSD1/KDM1A inhibitors, quantitative structure–activity relationship (QSAR) models were built by OECD requirements. In the QSAR analysis, certainly significant and understated pharmacophoric features were identified as critical for LSD1 inhibition, such as a ring Carbon atom with exactly six bonds from a Nitrogen atom, partial charges of lipophilic atoms within eight bonds from a ring Sulphur atom, a non-ring Oxygen atom exactly nine bonds from the amide Nitrogen, etc. The genetic algorithm–multi-linear regression (GA-MLR) and double cross-validation criteria were used to create robust QSAR models with high predictability. In this study, two QSAR models were developed, with fitting parameters like R² = 0.83–0.81, F = 61.22–67.96, internal validation parameters such as Q²_LOO = 0.79–0.77, Q²_LMO = 0.78–0.76, CCC_cv = 0.89–0.88, and external validation parameters such as, R2ext = 0.82 and CCCex = 0.90. In terms of mechanistic interpretation and statistical analysis, both QSAR models are well-balanced. Furthermore, utilizing the pharmacophoric features revealed by QSAR modelling, molecular docking experiments corroborated with the most active compound’s binding to the LSD1 receptor. The docking results are then refined using Molecular dynamic simulation and MMGBSA analysis. As a consequence, the findings of the study can be used to produce LSD1/KDM1A inhibitors as anticancer leads.

Perceiving the Concealed and Unreported Pharmacophoric Features of the 5-Hydroxytryptamine Receptor Using Balanced QSAR Analysis

The 5-hydroxytryptamine receptor 6 (5-HT6) has gained attention as a target for developing therapeutics for Alzheimer’s disease, schizophrenia, cognitive dysfunctions, anxiety, and depression, to list a few. In the present analysis, a larger and diverse dataset of 1278 molecules covering a broad chemical and activity space was used to identify visual and concealed structural features associated with binding affinity for 5-HT6. For this, quantitative structure–activity relationships (QSAR) and molecular docking analyses were executed. This led to the development of a statistically robust QSAR model with a balance of excellent predictivity (R²_tr = 0.78, R²_ex = 0.77), the identification of unreported aspects of known features, and also novel mechanistic interpretations. Molecular docking and QSAR provided similar as well as complementary results. The present analysis indicates that the partial charges on ring carbons present within four bonds from a sulfur atom, the occurrence of sp3-hybridized carbon atoms bonded with donor atoms, and a conditional occurrence of lipophilic atoms/groups from nitrogen atoms, which are prominent but unreported pharmacophores that should be considered while optimizing a molecule for 5-HT6. Thus, the present analysis led to identification of some novel unreported structural features that govern the binding affinity of a molecule. The results could be beneficial in optimizing the molecules for 5-HT6.