Reliable Quantitative Structure-activity Relationship Models Research Articles

QSAR models for the ozonation of diverse volatile organic compounds at different temperatures.

In order to assess the fate and persistence of volatile organic compounds (VOCs) in the atmosphere, it is necessary to determine their oxidation rate constants for their reaction with ozone (kO3). However, given that experimental values of kO3 are only available for a few hundred compounds and their determination is expensive and time-consuming, developing predictive models for kO3 is of great importance. Thus, this study aimed to develop reliable quantitative structure-activity relationship (QSAR) models for 302 values of 149 VOCs across a broad temperature range (178-409 K). The model was constructed based on the combination of a simplified molecular-input line-entry system (SMILES) and temperature as an experimental condition, namely quasi-SMILES. In this study, temperature was incorporated in the models as an independent feature. The hybrid optimal descriptor generated from the combination of quasi-SMILES and HFG (hydrogen-filled graph) was used to develop reliable, accurate, and predictive QSAR models employing the CORAL software. The balance between the correlation method and four different target functions (target function without considering IIC or CII, target function using each IIC or CII, and target function based on the combination of IIC and CII) was used to improve the predictability of the QSAR models. The performance of the developed models based on different target functions was compared. The correlation intensity index (CII) significantly enhanced the predictability of the model. The best model was selected based on the numerical value of Rm2 of the calibration set (split #1, Rtrain2 = 0.9834, Rcalibration2 = 0.9276, Rvalidation2 = 0.9136, and calibration = 0.8770). The promoters of increase/decrease for log kO3 were also computed based on the best model. The presence of a double bond (BOND10000000 and $10 000 000 000), absence of halogen (HALO00000000), and the nearest neighbor codes for carbon equal to 321 (NNC-C⋯321) are some significant promoters of endpoint increase.

R software for QSAR analysis in phytopharmacological studies.

In recent decades, quantitative structure-activity relationship (QSAR) analysis has become an important method for drug design and natural product research. With the availability of bioinformatic and cheminformatic tools, a vast number of descriptors have been generated, making it challenging to select potential independent variables that are accurately related to the dependent response variable. The objective of this study is to demonstrate various descriptor selection procedures, such as the Boruta approach, all subsets regression, the ANOVA approach, the AIC method, stepwise regression, and genetic algorithm, that can be used in QSAR studies. Additionally, we performed regression diagnostics using R software to test parameters such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity. The workflow designed in this study highlights the different descriptor selection procedures and regression diagnostics that can be used in QSAR studies. The results showed that the Boruta approach and genetic algorithm performed better than other methods in selecting potential independent variables. The regression diagnostics parameters tested using R software, such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity, helped in identifying and diagnosing model errors, ensuring the reliability of the QSAR model. QSAR analysis is vital in drug design and natural product research. To develop a reliable QSAR model, it is essential to choose suitable descriptors and perform regression diagnostics. This study offers an accessible, customizable approach for researchers to select appropriate descriptors and diagnose errors in QSAR studies.

In silico multiscale drug design to discover key structural features of potential JAK2 inhibitors.

Background: JAK2 inhibitors have been proposed as a new therapeutic option for thalassemia therapy. The objective of this study was to discover the key structural features for improving 2-aminopyrimidine derivatives as potential JAK2 inhibitors. Materials & methods: Quantitative structure-activity relationship (QSAR) approaches (hologram QSAR and comparative molecular similarity indices analysis), molecular dynamicssimulations, binding energy calculations and pharmacokinetic predictions were employed. Results: Reliable QSAR models, binding mode and binding interactions of JAK2 inhibitors were obtained and these obtained results were used as the key information for rational design of highly potent JAK2 inhibitors. Conclusion: The concept of new potential JAK2 inhibitors integrated from the obtained results was proved, producing two newly designed compounds, D01 and D02, with potential for use as JAK2 inhibitors.

Comprehensive Interrogation on Acetylcholinesterase Inhibition by Ionic Liquids Using Machine Learning and Molecular Modeling.

Quantitative structure-activity relationship (QSAR) modeling can be used to predict the toxicity of ionic liquids (ILs), but most QSAR models have been constructed by arbitrarily selecting one machine learning method and ignored the overall interactions between ILs and biological systems, such as proteins. In order to obtain more reliable and interpretable QSAR models and reveal the related molecular mechanism, we performed a systematic analysis of acetylcholinesterase (AChE) inhibition by 153 ILs using machine learning and molecular modeling. Our results showed that more reliable and stable QSAR models (R2 > 0.85 for both cross-validation and external validation) were obtained by combining the results from multiple machine learning approaches. In addition, molecular docking results revealed that the cations and organic anions of ILs bound to specific amino acid residues of AChE through noncovalent interactions such as π interactions and hydrogen bonds. The calculation results of binding free energy showed that an electrostatic interaction (ΔEele < -285 kJ/mol) was the main driving force for the binding of ILs to AChE. The overall findings from this investigation demonstrate that a systematic approach is much more convincing. Future research in this direction will help design the next generation of biosafe ILs.

Ensemble hologram quantitative structure activity relationship model of the chromatographic retention index of aldehydes and ketones

色谱保留指数(retention index, RI)是色谱分析中的重要参数,不同化合物在不同极性固定相上具有不同的保留行为。醛酮化合物种类众多,实验测定其RI值的时间和经济成本高。该论文采用集成建模(ensemble modeling)结合全息定量构效关系(HQSAR)方法研究了醛酮化合物在2种固定相(DB-210和HP-Innowax)上色谱保留指数的定量构效关系(QSAR)模型。用外部测试集验证法和留一交叉验证法评估了所建立模型的预测能力。首先建立了34种被研究化合物的个体HQSAR模型。在固定相DB-210上,片段特性(FD)为“供体/受体原子(DA)”且片段尺寸(FS)为1~9时可得到最优个体模型,在固定相HP-Innowax上,FD为“DA”且FS为4~7时可得到最优个体模型,这两个模型的交叉验证相关系数( )分别为0.935和0.909,外部验证相关系数( )分别为0.925和0.927,一致性相关系数(CCC)分别为0.953和0.960,预测平方相关系数F2( )分别为0.922和0.918,预测平方相关系数F3( )分别为0.931和0.927。研究结果表明醛酮化合物的分子结构与RI值之间存在定量关系,用HQSAR方法可以建立二者之间的QSAR模型。其次,以4个预测准确度最高的个体HQSAR模型作为子模型通过算术平均建立了集成HQSAR模型。建立的集成HQSAR模型预测被研究化合物在DB-210和HP-Innowax固定相上RI值的 分别为0.927和0.919, 分别为0.929和0.963, CCC分别为0.956和0.979, 分别为0.927和0.958, 分别为0.935和0.963。与个体HQSAR模型相比,建立的集成HQSAR模型预测准确度更高。这说明集成建模是提高HQSAR模型预测能力的有效方法,HQSAR与集成建模方法相结合可以用于研究和预测醛酮化合物的RI值。

QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids.

In recent years, there have been frequent reports on the adverse effects of synthetic cannabinoid (SC) abuse. SCs cause psychoactive effects, similar to those caused by marijuana, by binding and activating cannabinoid receptor 1 (CB1R) in the central nervous system. The aim of this study was to establish a reliable quantitative structure–activity relationship (QSAR) model to correlate the structures and physicochemical properties of various SCs with their CB1R-binding affinities. We prepared tetrahydrocannabinol (THC) and 14 SCs and their derivatives (naphthoylindoles, naphthoylnaphthalenes, benzoylindoles, and cyclohexylphenols) and determined their binding affinity to CB1R, which is known as a dependence-related target. We calculated the molecular descriptors for dataset compounds using an R/CDK (R package integrated with CDK, version 3.5.0) toolkit to build QSAR regression models. These models were established, and statistical evaluations were performed using the mlr and plsr packages in R software. The most reliable QSAR model was obtained from the partial least squares regression method via Y-randomization test and external validation. This model can be applied in vivo to predict the addictive properties of illicit new SCs. Using a limited number of dataset compounds and our own experimental activity data, we built a QSAR model for SCs with good predictability. This QSAR modeling approach provides a novel strategy for establishing an efficient tool to predict the abuse potential of various SCs and to control their illicit use.

QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm

ABSTRACT High-dimensionality is one of the major problems which affect the quality of the quantitative structure–activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSEtest compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC50 values of neuraminidase inhibitors that have not been experimentally tested.

Nucleobase sequence based building up of reliable QSAR models with the index of ideality correlation using Monte Carlo method

This study describes in silico designing of aptamers against the influenza virus using Monte Carlo method. Aptamers are short, single-stranded oligonucleotides and these bind to an ample range of biologically important proteins which are related to many disease conditions. The affinities and specificities of aptamers are comparable to antibodies. In the medicinal chemistry, quantitative structure-activity relationship (QSAR) is an important skill which is used for drug design and development. To study the inhibitory activity of aptamers, we have developed QSAR models based on Monte Carlo method. The nucleobase sequence descriptors Bk, BBk and BBBk are used to generate the QSAR models. A number of statistical benchmarks together with index of ideality of correlation (IIC) is considered to validate the build QSAR models. Data set of 98 aptamers is divided into four random splits. The statistical criteria R2 = 0.8711 and CCC = 0.9207 of the validation set of split 3 are best, so the build QSAR model of split 3 is the paramount model. The aptamer fragment responsible for the promotors of endpoint increase and decrease are also determined. These fragments are applied to design new nine aptamers from the lead aptamer APT01.Communicated by Ramaswamy H. Sarma

Antibacterial Properties of Polyphenols: Characterization and QSAR (Quantitative Structure-Activity Relationship) Models.

Besides their established antioxidant activity, many phenolic compounds may exhibit significant antibacterial activity. Here, the effect of a large dataset of 35 polyphenols on the growth of 6 foodborne pathogenic or food-spoiling bacterial strains, three Gram-positive ones (Staphylococcus aureus, Bacillus subtilis, and Listeria monocytogenes) and three Gram-negative ones (Escherichia coli, Pseudomonas aeruginosa, and Salmonella Enteritidis), have been characterized. As expected, the effects of phenolic compounds were highly heterogeneous ranging from bacterial growth stimulation to antibacterial activity and depended on bacterial strains. The effect on bacterial growth of each of the polyphenols was expressed as relative Bacterial Load Difference (BLD) between a culture with and without (control) polyphenols at a 1 g L−1 concentration after 24 h incubation at 37°C. Reliable Quantitative Structure-Activity Relationship (QSAR) models were developed (regardless of polyphenol class or the mechanism of action involved) to predict BLD for E. coli, S. Enteritidis, S. aureus, and B. subtilis, unlike for L. monocytogenes and P. aeruginosa. L. monocytogenes was generally sensitive to polyphenols whereas P. aeruginosa was not. No satisfactory models predicting the BLD of P. aeruginosa and L. monocytogenes were obtained due to their specific and quite constant behavior toward polyphenols. The main descriptors involved in reliable QSAR models were the lipophilicity and the electronic and charge properties of the polyphenols. The models developed for the two Gram-negative bacteria (E. coli, S. Enteritidis) were comparable suggesting similar mechanisms of toxic action. This was not clearly observed for the two Gram-positive bacteria (S. aureus and B. subtilis). Interestingly, a preliminary evaluation by Microbial Adhesion To Solvents (MATS) measurements of surface properties of the two Gram-negative bacteria for which QSAR models were based on similar physico-chemical descriptors, revealed that MATS results were also quite similar. Moreover, the MATS results of the two Gram-positive bacterial strains S. aureus and B. subtilis for which QSARs were not based on similar physico-chemical descriptors also strongly differed. These observations suggest that the antibacterial activity of most of polyphenols likely depends on interactions between polyphenols and bacterial cells surface, although the surface properties of the bacterial strains should be further investigated with other techniques than MATS.

QSAR study of anti-Human African Trypanosomiasis activity for 2-phenylimidazopyridines derivatives using DFT and Lipinski's descriptors

The quantitative structure-activity relationship (QSAR) of sixty 2-phenylimidazopyridines derivatives with anti-Human African Trypanosomiasis (anti-HAT) activity has been studied by using the density functional theory (DFT) and statistical methods. Becke's three-parameter hybrid method and the Lee-Yang-Parr B3LYP functional employing 6–31G(d) basis set are used to calculate quantum chemical descriptors using Gaussian 03W software, and the five Lipinski's parameters were calculated using ChemOffice software.In order to obtain robust and reliable QSAR model, the original dataset was randomly divided into training and prediction sets comprising 48 and 12 compounds, respectively. An optimal model for the training set with significant statistical quality was established. The same model was further applied to predict pEC50 values of the 12 compounds in the test set, further showing that this QSAR model has high predictive ability. It is very interesting to find that the anti-HAT of these compounds appear to be mainly governed by four factors, i.e., the number of H-bond donors, the lowest unoccupied molecular orbital energy, the molecular weight and the octanol/water partition coefficient. Here the possible action mechanism of these compounds was analysed and discussed, in particular, important structural requirements for great anti-HAT activity will be by increasing molecular size and substitute the 2-phenylimidazopyridines derivatives with polar, ionic, stronger accepting electron ability group and heteroatoms attached to one or more hydrogen atoms. Based on this proposed QSAR model, some new compounds with higher anti-HAT activities have been theoretically designed. Such results can offer useful theoretical references for future experimental works.

Investigation of MI-2 analogues as MALT1 inhibitors to treat of diffuse large B-Cell lymphoma through combined molecular dynamics simulation, molecular docking and QSAR techniques and design of new inhibitors

Mucosa-associated lymphoid tissue lymphoma translocation protein-1 (MALT1) has emerged as a promising therapeutic target for the treatment of activated B cell–like diffuse large B-cell lymphoma (ABC-DLBCL). To develop more potent MALT1 inhibitors, a series of MI-2 analogues were investigated using combined molecular modeling techniques such as molecular dynamics (MD) simulation, molecular docking and quantitative structure-activity relationship (QSAR). 40 ns MD simulations were successfully performed on the docked complexes of reference compound (MI-2) and compound 33 (one of highly active compounds of the data set) to investigate the binding stability and specifying the active site of the MALT1 enzyme in interaction with inhibitors. A few key residues (GLY416 and GLU500) at the binding site of MALT1 enzyme were identified. Using molecular docking was understood that hydroxyl groups along with groups of dichlorophenyl, chlorobenzyl and fluorobenzyl help to create interactions between the MI-2 analogues and enzyme residues to stabilize the conformation of compounds at the binding pocket. A robust and reliable QSAR model, as evidenced by the high R2, Q2LOO and R2ext values (0.933, 0.891 and 0.970, respectively), was gained based on the MI-2 analogues to predict the quantitative chemical-biological relationship and predict the inhibitory activity of new drug compounds. Then ten new MI-2 analogues have been designed that provide better inhibitory activity with the MALT1 enzyme than the reference compounds.

Molecular features of thymidine analogues governing the activity of human thymidine kinase

Modified uridines seem to be able to work as hypoxic tumour cell radiosensitisers. Before they sensitise cells to ionising radiation, they have to be incorporated into the genomic DNA and the latter process has to be preceded by the phosphorylation of the modified uridine, which in human cells is executed by human thymidine kinase 1 (hTK1). In the current study, we present the quantitative structure-activity relationship (QSAR) model allowing to identify and understand the molecular features of nucleoside derivatives governing the hTK1 kinase activity. The developed model meets all requirements of a reliable QSAR model and is based on only two molecular properties: the shape of the nucleoside determined by atom substitutions and the ability of the molecule to intermolecular interactions with the enzyme. These results have important implications for the rational designing of new hTK1 substrates and should significantly reduce the time and cost of studies on new radiosensitisers.

An automated framework for QSAR model building

BackgroundIn-silico quantitative structure–activity relationship (QSAR) models based tools are widely used to screen huge databases of compounds in order to determine the biological properties of chemical molecules based on their chemical structure. With the passage of time, the exponentially growing amount of synthesized and known chemicals data demands computationally efficient automated QSAR modeling tools, available to researchers that may lack extensive knowledge of machine learning modeling. Thus, a fully automated and advanced modeling platform can be an important addition to the QSAR community.ResultsIn the presented workflow the process from data preparation to model building and validation has been completely automated. The most critical modeling tasks (data curation, data set characteristics evaluation, variable selection and validation) that largely influence the performance of QSAR models were focused. It is also included the ability to quickly evaluate the feasibility of a given data set to be modeled. The developed framework is tested on data sets of thirty different problems. The best-optimized feature selection methodology in the developed workflow is able to remove 62–99% of all redundant data. On average, about 19% of the prediction error was reduced by using feature selection producing an increase of 49% in the percentage of variance explained (PVE) compared to models without feature selection. Selecting only the models with a modelability score above 0.6, average PVE scores were 0.71. A strong correlation was verified between the modelability scores and the PVE of the models produced with variable selection.ConclusionsWe developed an extendable and highly customizable fully automated QSAR modeling framework. This designed workflow does not require any advanced parameterization nor depends on users decisions or expertise in machine learning/programming. With just a given target or problem, the workflow follows an unbiased standard protocol to develop reliable QSAR models by directly accessing online manually curated databases or by using private data sets. The other distinctive features of the workflow include prior estimation of data modelability to avoid time-consuming modeling trials for non modelable data sets, an efficient variable selection procedure and the facility of output availability at each modeling task for the diverse application and reproduction of historical predictions. The results reached on a selection of thirty QSAR problems suggest that the approach is capable of building reliable models even for challenging problems.

Ezqsar: An R Package for Developing QSAR Models Directly From Structures.

Background:Quantitative Structure Activity Relationship (QSAR) is a difficult computational chemistry approach for beginner scientists and a time consuming one for even more experienced researchers.Method and Materials:Ezqsar which is introduced here addresses both the issues. It considers important steps to have a reliable QSAR model. Besides calculation of descriptors using CDK library, highly correlated descriptors are removed, a provided data set is divided to train and test sets, descriptors are selected by a statistical method, statistical parameter for the model are presented and applicability domain is investigated.Results:Finally, the model can be applied to predict the activities for an extra set of molecules for a purpose of either lead optimization or virtual screening. The performance is demonstrated by an example. Conclusion:The R package, ezqsar, is freely available via https://github.com/shamsaraj/ezqsar, and it runs on Linux and MS-Windows.

Characterizing the Chemical Space of ERK2 Kinase Inhibitors Using Descriptors Computed from Molecular Dynamics Trajectories.

Quantitative Structure-Activity Relationship (QSAR) models typically rely on 2D and 3D molecular descriptors to characterize chemicals and forecast their experimental activities. Previously, we showed that even the most reliable 2D QSAR models and structure-based 3D molecular docking techniques were not capable of accurately ranking a set of known inhibitors for the ERK2 kinase, a key player in various types of cancer. Herein, we calculated and analyzed a series of chemical descriptors computed from the molecular dynamics (MD) trajectories of ERK2-ligand complexes. First, the docking of 87 ERK2 ligands with known binding affinities was accomplished using Schrodinger's Glide software; then, solvent-explicit MD simulations (20 ns, NPT, 300 K, TIP3P, 1 fs) were performed using the GPU-accelerated Desmond program. Second, we calculated a series of MD descriptors based on the distributions of 3D descriptors computed for representative samples of the ligand's conformations over the MD simulations. Third, we analyzed the data set of 87 inhibitors in the MD chemical descriptor space. We showed that MD descriptors (i) had little correlation with conventionally used 2D/3D descriptors, (ii) were able to distinguish the most active ERK2 inhibitors from the moderate/weak actives and inactives, and (iii) provided key and complementary information about the unique characteristics of active ligands. This study represents the largest attempt to utilize MD-extracted chemical descriptors to characterize and model a series of bioactive molecules. MD descriptors could enable the next generation of hyperpredictive MD-QSAR models for computer-aided lead optimization and analogue prioritization.

In Silico Multivariate Regressio n Analysis and Validation Studies on Selective MMP-13 Inhibitors

QSAR(Quantitative Structure Activity Relationship) studies were carried out on a set of 72 α-sulfone hydroxamatesas Matrix Metalloproteinase-13 (MMP-13) inhibitors using multiple regression procedure. Outliers were removed based on Relative Error calculation and Extent of Extrapolation. The activity contributions of these compounds were determined from regression equation and the validation procedures such as external set cross-validation r, (R2cv, ext) and the regression of observed activities versus predicted activities and vice versa for validation set was described to analyze the predictive ability of the QSAR model. Parameters concerning predictive ability of QSAR model and Y-randomization tests were found to be within the limits. From a set of 5 models, an accurate and reliable QSAR model involving six descriptors was chosen based on the FIT Kubinyi function, which defines the statistical quality of the model. The generated model could be useful in designing more potent inhibitors of MMP-13.

QSAR of the free radical scavenging potency of selected hydroxybenzoic acids and simple phenolics

The quantitative structure–activity relationship (QSAR) of the free radical scavenging potency of 21 selected hydroxybenzoic acids and simple phenolics was examined. We found that descriptors related to the energetics and structural aspects of free radical scavenging processes enable the development of reliable QSAR models that possess better statistical characteristics than the models developed using more than thousand molecular descriptors from the large Dragon set. The lack of standardized antioxidant assays makes the successful use of the QSAR procedure doubtful. However, we showed that by taking into account some driving forces of free radical scavenging and the associated descriptors (bond dissociation enthalpy BDE, proton affinity PA, electron-transfer enthalpy ETE, and the number of vicinal phenolic OH groups, nOHvic), it is possible to generate fair antiradical QSAR models.

Evolutionary Computation and QSAR Research

The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.

A segmented principal component analysis—regression approach to QSAR study of peptides

We employed segmented principal component analysis and regression, as a new methodology in quantitative structure–activity relationship (QSAR), to define new amino acid indices. The descriptors are first classified into different groups (based on the similarity in the information contents they are possessing) and then each group is subjected to principal component analysis (PCA), separately. The extracted principal components (PC) from the descriptor data matrix of each group can be considered as new sources of amino acid indices. These indices were used as input variables for QSAR study of two dipeptide data sets (58 angiotensin-converting enzyme (ACE) inhibitor activity, and 48 bitter tasting threshold (BTT) activity). Modeling between the indices and biological activity was achieved utilizing segmented principal component regression (SPCR) and segmented partial least squares (SPLS) methods. Both methods resulted in reliable QSAR models. In comparison with conventional principal component regression (PCR) and partial least square (PLS), the segmented ones produced more predictive models. In addition, the developed models showed better performances with respect to the previously reported models for the same data sets. It can be concluded that by segmentation of variables and partitioning of the information into informative and redundant parts, it is possible to discard the redundant part of variables and to obtain more appropriate models.

Quantitative Structure–Activity Relationship Models for Predicting Biological Properties, Developed by Combining Structure‐ and Ligand‐Based Approaches: An Application to the Human Ether‐a‐go‐go‐Related Gene Potassium Channel Inhibition

A strategy for developing accurate quantitative structure-activity relationship models enabling predictions of biological properties, when suitable knowledge concerning both ligands and biological target is available, was tested on a data set where molecules are characterized by high structural diversity. Such a strategy was applied to human ether-a-go-go-related gene K(+) channel inhibition and consists of a combination of ligand- and structure-based approaches, which can be carried out whenever the three-dimensional structure of the target macromolecule is known or may be modeled with good accuracy. Molecular conformations of ligands were obtained by means of molecular docking, performed in a previously built theoretical model of the channel pore, so that descriptors depending upon the three-dimensional molecular structure were properly computed. A modification of the directed sphere-exclusion algorithm was developed and exploited to properly splitting the whole dataset into Training/Test set pairs. Molecular descriptors, computed by means of the codessa program, were used for the search of reliable quantitative structure-activity relationship models that were subsequently identified through a rigorous validation analysis. Finally, pIC(50) values of a prediction set, external to the initial dataset, were predicted and the results confirmed the high predictive power of the model within a quite wide chemical space.