Quantitative structure–activity relationship prediction of blood-to-brain partitioning behavior using support vector machine

Modeling the reactivities of hydroxyl radical and ozone towards atmospheric organic chemicals using quantitative structure-reactivity relationship approaches

The persistence and the removal of organic chemicals from the atmosphere are largely determined by their reactions with the OH radical and O3. Experimental determinations of the kinetic rate constants of OH and O3 with a large number of chemicals are tedious and resource intensive and development of computational approaches has widely been advocated. Recently, ensemble machine learning (EML) methods have emerged as unbiased tools to establish relationship between independent and dependent variables having a nonlinear dependence. In this study, EML-based, temperature-dependent quantitative structure-reactivity relationship (QSRR) models have been developed for predicting the kinetic rate constants for OH (kOH) and O3 (kO3) reactions with diverse chemicals. Structural diversity of chemicals was evaluated using a Tanimoto similarity index. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation performed employing statistical checks. In test data, the EML QSRR models yielded correlation (R (2)) of ≥0.91 between the measured and the predicted reactivities. The applicability domains of the constructed models were determined using methods based on descriptors range, Euclidean distance, leverage, and standardization approaches. The prediction accuracies for the higher reactivity compounds were relatively better than those of the low reactivity compounds. Proposed EML QSRR models performed well and outperformed the previous reports. The proposed QSRR models can make predictions of rate constants at different temperatures. The proposed models can be useful tools in predicting the reactivities of chemicals towards OH radical and O3 in the atmosphere.

Exploring simple, transparent, interpretable and predictive QSAR models for classification and quantitative prediction of rat toxicity of ionic liquids using OECD recommended guidelines

The present study explores the chemical attributes of diverse ionic liquids responsible for their cytotoxicity in a rat leukemia cell line (IPC-81) by developing predictive classification as well as regression-based mathematical models. Simple and interpretable descriptors derived from a two-dimensional representation of the chemical structures along with quantum topological molecular similarity indices have been used for model development, employing unambiguous modeling strategies that strictly obey the guidelines of the Organization for Economic Co-operation and Development (OECD) for quantitative structure-activity relationship (QSAR) analysis. The structure-toxicity relationships that emerged from both classification and regression-based models were in accordance with the findings of some previous studies. The models suggested that the cytotoxicity of ionic liquids is dependent on the cationic surfactant action, long alkyl side chains, cationic lipophilicity as well as aromaticity, the presence of a dialkylamino substituent at the 4-position of the pyridinium nucleus and a bulky anionic moiety. The models have been transparently presented in the form of equations, thus allowing their easy transferability in accordance with the OECD guidelines. The models have also been subjected to rigorous validation tests proving their predictive potential and can hence be used for designing novel and "greener" ionic liquids. The major strength of the present study lies in the use of a diverse and large dataset, use of simple reproducible descriptors and compliance with the OECD norms.

A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction

Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration.

Qualitative and quantitative structure-activity relationship modelling for predicting blood-brain barrier permeability of structurally diverse chemicals

In this study, structure-activity relationship (SAR) models have been established for qualitative and quantitative prediction of the blood-brain barrier (BBB) permeability of chemicals. The structural diversity of the chemicals and nonlinear structure in the data were tested. The predictive and generalization ability of the developed SAR models were tested through internal and external validation procedures. In complete data, the QSAR models rendered ternary classification accuracy of >98.15%, while the quantitative SAR models yielded correlation (r(2)) of >0.926 between the measured and the predicted BBB permeability values with the mean squared error (MSE) <0.045. The proposed models were also applied to an external new in vitro data and yielded classification accuracy of >82.7% and r(2) > 0.905 (MSE < 0.019). The sensitivity analysis revealed that topological polar surface area (TPSA) has the highest effect in qualitative and quantitative models for predicting the BBB permeability of chemicals. Moreover, these models showed predictive performance superior to those reported earlier in the literature. This demonstrates the appropriateness of the developed SAR models to reliably predict the BBB permeability of new chemicals, which can be used for initial screening of the molecules in the drug development process.

In silico de novo design of novel NNRTIs: a bio-molecular modelling approach

Six novel NNRTIs (DABO) with high efficacy are designed by assessing the interaction potential and structural requirements using chemometric analyses (SVM, BPNN and MLR) on structural descriptors.

Design of novel leads: ligand based computational modeling studies on non-nucleoside reverse transcriptase inhibitors (NNRTIs) of HIV-1

Researchers are on the constant lookout for new antiviral agents for the treatment of AIDS. In the present work, ligand based modeling studies are performed on analogues of substituted phenyl-thio-thymines, which act as non-nucleoside reverse transcriptase inhibitors (NNRTIs) and novel leads are extracted. Using alignment-dependent descriptors, based on group center overlap (SALL, HDALL, HAALL and RALL), an alignment-independent descriptor (S log P), a topological descriptor (Balaban index (J)) and a 3D descriptor dipole moment (μ) and shape based descriptors (Kappa 2 index ((2)κ)), a correlation is derived with inhibitory activity. Linear and non-linear techniques have been used to achieve the goal. Support Vector Machine (SVM, R = 0.929, R(2) = 0.863) and Back Propagation Neural Network (BPNN, R = 0.928, R(2) = 0.861) methods yielded near similar results and outperformed Multiple Linear Regression (MLR, R = 0.915, R(2) = 0.837). The predictive ability of the models are cross-validated using a test dataset (SVM: R = 0.846, R(2) = 0.716, BPNN: R = 0.841, R(2) = 0.707 and MLR: R = 0.833, R(2) = 0.694). It is concluded that the hydrophobicity (S log P) and the polarity (μ) of a ligand and the presence of hydrogen donor (HDALL) moieties are the deciding factors in improving antiviral activity and pharmaco-therapeutic properties. Based on the above findings, a virtual dataset is created to extract probable leads with reasonable antiviral activity as well as better pharmacophoric properties.

Predictive modeling studies for the ecotoxicity of ionic liquids towards the green algae Scenedesmus vacuolatus

Hazardous potential of ionic liquids is becoming an issue of high concern with increasing application of these compounds in various industrial processes. Predictive toxicological modeling on ionic liquids provides a rational assessment strategy and aids in developing suitable guidance for designing novel analogues. The present study attempts to explore the chemical features of ionic liquids responsible for their ecotoxicity towards the green algae Scenedesmus vacuolatus by developing mathematical models using extended topochemical atom (ETA) indices along with other categories of chemical descriptors. The entire study has been conducted with reference to the OECD guidelines for QSAR model development using predictive classification and regression modeling strategies. The best models from both the analyses showed that ecotoxicity of ionic liquids can be decreased by reducing chain length of cationic substituents and increasing hydrogen bond donor feature in cations, and replacing bulky unsaturated anions with simple saturated moiety having less lipophilic heteroatoms.

Exploring QSTR modeling and toxicophore mapping for identification of important molecular features contributing to the chemical toxicity in Escherichia coli

Biodiversity deprivation can affect functions and services of the ecosystem. Changes in biodiversity alter ecosystem processes and change the resilience of ecosystems to ecological changes. Bacterial communities are the main form of biomass in the ecosystem and one of largest populations on the planet. Bacterial communities provide important services to biodiversity. They break down pollutants, municipal waste and ingested food, and they are the primary route for recycling of organic matter to plants and other autotrophs, conversion of inorganic matter into new biological tissue using sunlight, management of energy crisis through use of biofuel. In the present study, computational chemistry and statistical modeling have been used to develop mathematical equations which can be applied to calculate toxicity of new/unknown chemicals/biofuels/metabolites in Escherichia coli. 2D and 3D descriptors were generated from molecular structure of compounds and mathematical models have been developed using genetic function approximation followed by multiple linear regression (GFA-MLR) method. Model validity was checked through defined internal (R(2)=0.751 and Q(2)=0.711), and external (Rpred(2)=0.773) statistical parameters. Molecular features responsible for toxicity were also assessed through 3D toxicophore study. The toxicophore-based model was validated (R=0.785) using qualitative statistical metrics and randomization test (Fischer validation).

QSTR with extended topochemical atom (ETA) indices. 16. Development of predictive classification and regression models for toxicity of ionic liquids towards Daphnia magna

Ionic liquids have been judged much with respect to their wide applicability than their considerable harmful effects towards the living ecosystem which has been observed in many instances. Hence, toxicological introspection of these chemicals by the development of predictive mathematical models can be of good help. This study presents an attempt to develop predictive classification and regression models correlating the structurally derived chemical information of a group of 62 diverse ionic liquids with their toxicity towards Daphnia magna and their interpretation. We have principally used the extended topochemical atom (ETA) indices along with various topological non-ETA and thermodynamic parameters as independent variables. The developed quantitative models have been subjected to extensive statistical tests employing multiple validation strategies from which acceptable results have been reported. The best models obtained from classification and regression studies captured necessary structural information on lipophilicity, branching pattern, electronegativity and chain length of the cationic substituents for explaining ecotoxicity of ionic liquids towards D. magna. The derived information can be successfully used to design better ionic liquid analogues acquiring the qualities of a true eco-friendly green chemical.