Development Of Quantitative Structure-activity Relationship Models Research Articles

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery.

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach.

In Silico Prediction of O⁶-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods.

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

Development and Validation of a Robust QSAR Model for Benzothiazole Hydrazone Derivatives as Bcl-XL Inhibitors

Background: B-cell Lymphoma Extra Large (Bcl-XL) belongs to B-cell Lymphoma two (Bcl-2) family. Due to its over-expression and anti-apoptotic role in many cancers, it has been proven to be a more biologically relevant therapeutic target in anti-cancer therapy. In this study, a Quantitative Structure Activity Relationship (QSAR) modeling was performed to establish the link between structural properties and inhibitory potency of benzothiazole hydrazone derivatives against Bcl-XL. Methods: The 53 benzothiazole hydrazone derivatives have been used for model development using genetic algorithm and multiple linear regression methods. The data set is divided into training and test set using Kennard-Stone based algorithm. The best QSAR model has been selected with statistically significant r2 = 0.931, F-test =55.488 RMSE = 0.441 and Q2 0.900. Results: The model has been tested successfully for external validation (r2 pred = 0.752), as well as different criteria for acceptable model predictability. Furthermore, analysis of the applicability domain has been carried out to evaluate the prediction reliability of external set molecules. The developed QSAR model has revealed that nThiazoles, nROH, EEig13d, WA, BEHv6, HATS6m, RDF035u and IC4 descriptors are important physico-chemical properties for determining the inhibitory activity of these molecules. Conclusion: The developed QSAR model is stable for this chemical series, indicating that test set molecules represent the training dataset. The model is statistically reliable with good predictability. The obtained descriptors reflect important structural features required for activity against Bcl-XL. These properties are designated by topology, shape, size, geometry, substitution information of the molecules (nThiazoles and nROH) and electronic properties. In a nutshell, these characteristics can be successfully utilized for designing and screening of novel inhibitors.

Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project.

The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure–activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

Integrating data gap filling techniques: A case study predicting TEFs for neurotoxicity TEQs to facilitate the hazard assessment of polychlorinated biphenyls

The application of toxic equivalency factors (TEFs) or toxic units to estimate toxic potencies for mixtures of chemicals which contribute to a biological effect through a common mechanism is one approach for filling data gaps. Toxic Equivalents (TEQ) have been used to express the toxicity of dioxin-like compounds (i.e., dioxins, furans, and dioxin-like polychlorinated biphenyls (PCBs)) in terms of the most toxic form of dioxin: 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD). This study sought to integrate two data gap filling techniques, quantitative structure–activity relationships (QSARs) and TEFs, to predict neurotoxicity TEQs for PCBs. Simon et al. (2007) previously derived neurotoxic equivalent (NEQ) values for a dataset of 87 PCB congeners, of which 83 congeners had experimental data. These data were taken from a set of four different studies measuring different effects related to neurotoxicity, each of which tested overlapping subsets of the 83 PCB congeners. The goals of the current study were to: (i) evaluate an alternative neurotoxic equivalent factor (NEF) derivations from an expanded dataset, relative to those derived by Simon et al. and (ii) develop QSAR models to provide NEF estimates for the large number of untested PCB congeners. The models used multiple linear regression, support vector regression, k-nearest neighbor and random forest algorithms within a 5-fold cross validation scheme and position-specific chlorine substitution patterns on the biphenyl scaffold as descriptors. Alternative NEF values were derived but the resulting QSAR models had relatively low predictivity (RMSE ∼0.24). This was mostly driven by the large uncertainties in the underlying data and NEF values. The derived NEFs and the QSAR predicted NEFs to fill data gaps should be applied with caution.

Role of Topological, Electronic, Geometrical, Constitutional and Quantum Chemical Based Descriptors in QSAR: mPGES-1 as a Case Study

Quantitative Structure Activity Relationship (QSAR) is one of the widely used ligand based drug design strategies. Although a number of QSAR studies have been reported, debates over the limitations and accuracy of QSAR models are at large. In this review the applicability of various classes of molecular descriptors in QSAR has been explained. Protocol for QSAR model development and validation is presented. Here we discuss a case study on 7-Phenyl-imidazoquinolin-4(5H)-one derivatives as potent mPGES-1 inhibitors to identify crucial physicochemical properties responsible for mPGES-1 inhibition. The case study explains the methodology for QSAR analysis, validation of the developed models and role of diverse classes of molecular descriptors in defining the inhibitory activity of considered inhibitors. Various molecular descriptors derived from 2D/3D structure and quantum mechanics were considered in the study. Initially, QSAR models for the training set compounds were developed individually for each class of molecular descriptors. Further, a combined QSAR model was developed using the best descriptor from all the classes. The models obtained were further validated using an external test set. Combined QSAR model exhibited the best correlation (r = 0.80) between the predicted and experimental biological activities of test set compounds. The results of the QSAR analysis were further backed by docking studies. From the results of the case study it is evident that rather than a single class of molecular descriptors, a combination of molecular descriptors belonging to different classes significantly improves the QSAR predictions. The techniques and protocol discussed in the present work might be of significant importance while developing QSAR models of various drug targets.

QSAR and Classification Study on Prediction of Acute Oral Toxicity of N-Nitroso Compounds.

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

Toxicity of polyhalogenated dibenzo-p-furans in the light of nucleic acid bases interaction

Quantitative structure-activity relationship (QSAR) investigation utilizing quantum chemical descriptors under density functional theory is performed to predict the toxicity (pEC50) of a series of polyhalogenated dibenzo-p-furans (PHDFs). PHDFs are very important concern to the researchers due to their presence and diverse effects in the environment. A successful two parameter QSAR model is developed with a combination of a global descriptor known as charge transfer (ΔN) between toxins and biosystem and a local descriptor as Fukui function (fmax+) for maximum nucleophilic attack at the toxin site. A systematic analysis is performed to identify the electron donation/acceptance nature of the considered PHDF compounds with the choice of a model biosystems comprising five different nucleic acid bases, namely Adenine, Thymine, Guanine, Cytosine and Uracil to identify proper ΔN descriptor. Accordingly, PHDFs are found to be electron acceptors with maximum charge transfer from Guanine and therefore, ΔNG is utilized as the charge transfer parameter for all the toxins in the present work. The selected combination of global and local descriptors (ΔNG andfmax+) are found to predict 93% of the observed toxicity (pEC50) of the PHDFs. The developed QSAR model is tested for two different test sets: PHDFs and polyhalogenated biphenyls (PHBs) with about 90% of prediction of their toxicity values, which confirms the importance of the selected descriptors.

Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach

ABSTRACTAngiotensin-converting enzyme (ACE) inhibitors have been acknowledged as first-line agents for the treatment of hypertension and a variety of cardiovascular disorders. In this context, quantitative structure–activity relationship (QSAR) models for a series of non-peptide compounds as ACE inhibitors are developed based on Simplified Molecular Input-Line Entry System (SMILES) notation and local graph invariants. Three random splits into the training and test sets are used. The Monte Carlo method is applied for model development. Molecular docking studies are used for the final assessment of the developed QSAR model and the design of novel inhibitors. The statistical quality of the developed model is good. Molecular fragments responsible for the increase/decrease of the studied activity are calculated. The computer-aided design of new compounds, as potential ACE inhibitors, is presented. The predictive potential of the applied approach is tested, and the robustness of the model is proven using different methods. The results obtained from molecular docking studies are in excellent correlation with the results from QSAR studies. The presented study may be useful in the search for novel cardiovascular therapeutics based on ACE inhibition.

Use of Simplified Molecular Input Line Entry System and molecular graph based descriptors in prediction and design of pancreatic lipase inhibitors.

The inhibition of pancreatic lipase (PL) enzyme is the most explored strategy for the treatment of obesity. The present study describes the development of quantitative structure-activity relationship (QSAR) models for a diverse set of 293 PL inhibitors by means of the Monte Carlo optimization technique. Methodology & results: The hybrid optimal descriptors were used to build QSAR models with three subsets of three splits. The developed QSAR models were further validated with corresponding external sets. The best QSAR model has the following statistical particulars: R2=0.752, Q LOO 2 = 0 . 736 for the test set and R2=0.768, Q F 1 2 = 0 . 628 , Q F 2 2 = 0 . 621 for the validation set. The developed QSAR models were robust, stable and predictive and led to the design of novel PL inhibitors.

Development and evaluation of MTLSER and QSAR models for predicting polyethylene-water partition coefficients

Current study was aimed to make further improvements in measuring low density polyethylene (LDPE) -water partition coefficient (KPE-w) for organic chemicals. Modified theoretical linear solvation energy relationship (MTLSER) model and quantitative structure activity relationship (QSAR) model were developed for predicting KPE-w values from chemical descriptors. With the MTLSER model, α (average molecular polarizability), μ (dipole moment) and q- (net charge of the most negative atoms) as significant variables were screened. With the QSAR model, main control factors of KPE-w values, such as CrippenLogP (Crippen octanol-water partition coefficient), CIC0 (neighborhood symmetry of 0-order) and GATS2p (Geary autocorrelation-lag2/weighted by polarizabilities) were studied. As per our best knowledge, this is the first attempt to predict polymer-water partition coefficient using the MTLSER model. Statistical parameters, correlation coefficient (R2) and cross-validation coefficients (Q2) were ranging from 0.811 to 0.951 and 0.761 to 0.949, respectively, which indicated that the models appropriately fit the results, and also showed robustness and predictive capacity. Mechanism interpretation suggested that the main factors governing the partition process between LDPE and water were the molecular polarizability and hydrophobicity. The results of this study provide an excellent tool for predicting log KPE-w values of most common hydrophobic organic compounds, within the applicability domains to reduce experimental cost and time for innovation.

Quantitative Structure-activity Relationship Study of Betulinic Acid Derivatives Against HIV using SMILES-based Descriptors.

Human Immunodeficiency Virus (HIV) is the causative agent of Acquired Immunodeficiency Syndrome (AIDS) that imposes a global health burden. Therefore, HIV therapeutic agents have been discovery and development. To construct Quantitative-structure Activity Relationship (QSAR) models of betulinic acid derivatives with anti-HIV activity using Simplified Molecular-Input Line-Entry System (SMILES)- based descriptors. A data set of 107 betulinic acid derivatives and their anti-HIV activity was used to develop QSAR models. The SMILES format of the compounds was employed as descriptors for model construction using the CORAL software by means of the Monte Carlo method. Constructed QSAR models provided good correlation coefficients (R2) and root mean square error (RMSE) with values in the range of 0.5660-0.5890 and 0.963-1.020, respectively, for the training set, R2 value of 0.7206-0.7837 and RMSE as 0.609-1.250, respectively, for the calibration set, and R2 value of 0.6257-0.7748 and RMSE as 0.837-0.995, respectively, for the validation set. The best QSAR model displayed statistical parameters for training set: R2 = 0.5660 and RMSE = 0.963; calibration set: R2 = 0.7273 and RMSE = 0.609, and validation set: R2 = 0.7748 and RMSE = 0.972. In addition, features of the molecular structure that are promoters of the endpoint increase and decrease were defined and discussed. These are the basis for the mechanistic interpretation of the suggested models. These findings provide useful knowledge for guiding the design of novel compounds with promising anti-HIV activity.

Computational insights into the interaction of small molecule inhibitors with HRI kinase domain

The Heme-Regulated Inhibitor (HRI) kinase regulates globin synthesis in a heme-dependent manner in reticulocytes and erythroid cells in bone marrow. Inhibitors of HRI have been proposed to lead to an increased amount of haemoglobin, benefitting anaemia patients. A series of indeno[1,2-c]pyrazoles were discovered to be the first known in vitro inhibitors of HRI. However, the structural mechanism of inhibition is yet to be understood. The aim of this study was to unravel the binding mechanism of these inhibitors using molecular dynamic simulations and docking. The docking scores were observed to correlate well with experimentally determined pIC50 values. The inhibitors were observed to bind in the ATP-binding site forming hydrogen bonds with the hinge region and van der Waals interactions with non-polar residues in the binding site. Further, quantitative structure–activity relationship (QSAR) studies were performed to correlate the structural features of the inhibitors with their biological activity. The developed QSAR models were found to be statistically significant in terms of internal and external predictabilities. The presence of chlorine atoms and the hydroxymethyl groups were found to correlate with higher activity. The identified binding modes and the descriptors can support future rational identification of more potent and selective small molecule inhibitors for this kinase which are of therapeutic importance in the context of various human pathological disorders.

Review on: quantitative structure activity relationship (QSAR) modeling

Quantitative Structure Activity Relationship QSAR are mathematical models that seek to predict complicated physicochemical biological properties of chemicals from their simpler experimental or calculated properties QSAR enables the investigator to establishes a reliable quantitative relationship between structure and activity which will be used to derive an insilico model to predict the activity of novel molecules prior to their synthesis The past few decades have witnessed much advances in the development of computational models for the prediction of a wide span of biological and chemical activities that are beneficial for screening promising compounds with robust properties This review covers the concept history of QSAR and also the components involved in the development of QSAR models

Development of QSAR model for predicting the inclusion constants of organic chemicals with α-cyclodextrin.

Solubility is a crucial limiting factor in pharmaceutical research and contaminated site remediation. Cyclodextrin, with its structure of hydrophilic exterior and hydrophobic cavity, has a potential ability to enhance the hydrophobic chemical's solubility through the formation of host-guest complex. The stability of host-guest complex is often quantified by the inclusion constant. In this study, the logarithm of 1:1 α-cyclodextrin inclusion constants (log Kα) for 195 organic chemicals was collected. With this parameter as the endpoint, a quantitative structure-activity relationship (QSAR) model was developed using DRAGON descriptors and stepwise multiple linear regression analysis. The model statistics parameters indicated that the established model had a good determination coefficient of 0.857, a high cross-validation coefficient of 0.835, a low root mean square error of 0.380, together with the acceptable results of external validation, which indicate a satisfactory goodness-of-fit, robustness, and predictive ability of the model. Based on the screened eight descriptors, we propose an appropriate mechanism interpretation for the inclusion interaction. Additionally, the applicability domain of the current model was characterized by the Euclidean distance-based method and Williams plot, and results indicated that the model covered a large number of structurally diverse chemicals belonging to 13 different classes. Comparing with the previous reported models, this model has obvious advantages with a larger dataset, a higher value of correlation coefficient, and a wider application domain.

Development of selective QSAR models and molecular docking study for inhibitory activity of sulfonamide derivatives against carbonic anhydrase isoforms II and IX

In this study, we presented four linear quantitative structure-activity relationship (QSAR) models, which were developed from calculated molecular descriptors and experimental inhibition constants of 43 sulfonamide compounds measured against carbonic anhydrase (CA) II and IX isoforms. In the first two models, binding affinity of the compounds against CA II and IX isoforms were treated as independent variables, whereas in the remaining two models, taking affinity ratio (RpKi) and differences (ΔpKi) with respect to the two CA isoforms were treated as independent variables. Physically interpretable molecular descriptors involved in the obtained QSAR models allowed us to put forth some structural features, which played a critical role in binding affinity selectivity of the ligands with respect to the two CA isoforms. Furthermore, we applied AUTODOCK protocol for docking simulation of two highly selective ligands, 7c and 8d in binding to CA isoforms. The obtained results demonstrated that interaction of tail moiety of the ligand with Phe131 residue is an important factor for a ligand being highly selective for CA II binding.

Modelling the water–plant cuticular polymer matrix membrane partitioning of diverse chemicals in multiple plant species using the support vector machine-based QSAR approach

In this study, a support vector machine (SVM) based multi-species QSAR (quantitative structure–activity relationship) model was developed for predicting the water–plant cuticular polymer matrix membrane (MX) partition coefficient, KMXw of diverse chemicals using two simple molecular descriptors derived from the chemical structures and following the OECD guidelines. Accordingly, the Lycopersicon esculentum Mill. data were used to construct the QSAR model that was externally validated using three other plant species data. The diversity in chemical structures and end-points were verified using the Tanimoto similarity index and Kruskal–Wallis statistics. The predictive power of the developed QSAR model was tested through rigorous validation, deriving a wide series of statistical checks. The MLOGP was the most influential descriptor identified by the model. The model yielded a correlation (r2) of 0.966 and 0.965 in the training and test data arrays. The developed QSAR model also performed well in another three plant species (r2 > 0.955). The results suggest the appropriateness of the developed model to reliably predict the plant chemical interactions in multiple plant species and it can be a useful tool in screening the new chemical for environmental risk assessment.

QSAR Study of Angiotensin I-Converting Enzyme Inhibitory Peptides Using SVHEHS Descriptor and OSC-SVM

In this paper, four models of quantitative structure–activity relationship (QSAR) for 84 angiotensin I-converting enzyme inhibitory (ACEI) dipeptides, 169 ACEI tripeptides and 15 ACEI tetrapeptides and the total 268 peptides were established, respectively. During every modeling process, the structure of the peptides were firstly described using a set of amino acid descriptor SVHEHS proposed previously in our laboratory, then QSAR models were developed successfully by orthogonal signal correction combined with support vector machine (OSC-SVM). Furthermore, all models were validated by the reasonable statistical parameters, such as correlation coefficient (R2), cross-validated correlation coefficient (q2) and external validation coefficient (q2ext), etc. The results demonstrated that all developed QSAR models had excellent fitting accuracy and predictive ability. It could be a very promising approach for QSAR study on bioactive peptides using SVHEHS descriptor and OSC-SVM.

Chemometric modeling of larvicidal activity of plant derived compounds against zika virus vector Aedes aegypti: application of ETA indices.

Dengue, zika and chikungunya have severe public health concerns in several countries. Human modification of the natural environment continues to create habitats in which mosquitoes, vectors of a wide variety of human and animal pathogens, thrive, which can bring about an enormous negative impact on public health if not controlled properly. Quantitative structure–activity relationship (QSAR) modeling has been applied in this work with the aim of exploring features contributing to promising larvicidal properties against the vector Aedes aegypti (Diptera: Culicidae). A dataset of 61 plant derived compounds reported in previous literature was used in this present study. A genetic algorithm (GA) was used for QSAR model development employing the “Double Cross Validation” (DCV) tool available at http://teqip.jdvu.ac.in/QSAR_Tools/. The DCV tool removes any bias in descriptor selection from a fixed composition of a training set and often provides an optimum solution in terms of predictivity. Simple topological descriptors, the “Extended Topochemical Atom” (ETA) indices developed by the present authors' group, were used for model development. These descriptors do not require pretreatment of molecular structures by conformational analysis or energy minimization before model development, thus saving computational time and resources. They also avoid ambiguities with respect to the existence of compounds in various conformational states leading to the loss of predictive capability in QSAR models. A number of models were generated from GA, and further, the descriptors appearing in the best model obtained from GA were subjected to partial least squares (PLS) regression to obtain the final robust model. The developed model was validated extensively using different validation metrics to check the reliability and predictivity of the model for enhancing confidence in QSAR predictions. Based on the insights obtained from the PLS model, we can conclude that the presence of hydrogen bond acceptor atoms, the presence of multiple bonds as well as sufficient lipophilicity and a limited polar surface area play crucial roles in regulating the activity of the compounds.

Predictive QSAR modeling study on berberine derivatives with hypolipidemic activity.

Berberine (BBR), isolated from a Chinese herb, is identified as a new cholesterol-lowering small molecule, and hundreds of berberine derivatives have been obtained for optimization of their hypolipidemic activities in recent years. However, so far there is no available quantitative structure-activity relationship (QSAR) model used for the development of novel BBR analogues with hypolipidemic activities, mainly due to lack of lipid-lowering molecular mechanisms and target identification of BBR. In this paper, the tactics using ligand efficiency indice instead of pIC50 as the activity could be adopted for the development of BBR QSAR models. A series of 59 BBR derivatives with hypolipidemic activities have been studied and split randomly into three sets of training and test sets. Statistical quality of most building models shows obviously robust. Best calculated model that employs LLE indice as the activity (Model 6) has the following statistical parameters: for training set R2 =.984, Q2 =0.981, RMSE=0.1160, and for test set R2 =.989, RMSE=0.0067. This model would be used for the development of novel BBR analogues with lipid-lowering activities as a hit discovery tool.