Fuzzy structure-activity relationships

While quantitative structure-activity relationships attempt to predict the numerical value of the activities, it is found that statistically good predictors do not always do a good job of qualitatively determining the activity. This study shows how Fuzzy classifiers can be used to generate Fuzzy structure-activity relationships which can more accurately determine whether or not a compound will be highly inactive, moderately inactive or active, or highly active. Four examples of these classifiers are presented and applied to a well-studied activity dataset.     

A Novel Electron-Conformational Approach to Molecular Modeling for QSAR by Identification of Pharmacophore and Anti-Pharmacophore Shielding

Abstract

A novel method of pharmacophore identification and activity prediction in structure-activity (structure-property) relationships is worked out as an essential extension and improvement of previous publications. In this method each conformation of the molecular systems in the training set of the SAR problem is presented by both electronic structure and geometry parameters arranged in a matrix form. Multiple comparisons of these matrices for the active and inactive compounds allows one to separate a smaller number of matrix elements that are common for all the active compounds and are not present in the same arrangement in the inactive ones. This submatrix of activity represents the pharmacophore (Pha).

By introducing the Anti-Pharmacophore Shielding (APS) defined as molecular groups and competing charges outside the Pha that hinder the proper docking of the Pha with the bioreceptor, the procedure of Pha identification is essentially reduced to the treatment of a smaller number of simplest in structure most active and inactive compounds. A simple empirical scheme is suggested to estimate the APS numerically, while the contributions of different conformations of the same compound are taken into account by means of Boltzmann distribution. This enables us to make approximate quantitative predictions of activities.

In application to rice blast activity we reached an approximately 100% (within experimental error) prediction probability of the activity qualitatively (yes, no), and with r ² = 70% quantitatively.     

A novel fuzzy set based multifactor dimensionality reduction method for detecting gene–gene interaction

BackgroundGene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification.MethodsWe regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure.ResultsTwo simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD.ConclusionsWe propose a novel Fuzzy MDR method to detect gene–gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at https://statgen.snu.ac.kr/software/FuzzyMDR.     

Structure-Activity Relationships for Non-Congeneric Structures. Application of Substructural Balance Method for Antiallergic Activity

Abstract

This paper presents a new research method of structure-activity relationships (SAR) based on the concept of substructural balance. By using antiallergic activity (PCA, rat, iv) of a non-congeneri set of 267 structures, the structural feature of active group is expressed in terms of substructural balanance. Each structure was expressed with 100 new substructures and the number of each substructure in a molecule was counted. The substructural balance was expressed as their ratio. Structures were classified into three groups based on their potencies (ED₅₀), active (44), median (33) and inactactive (190) group. Using two substructural ratios, 80.53% of inactive and 57.58% of median structures were excluded from those that were active. Common features of active structures were shown as a zone indicating the optimal ranges of two substructural ratios. Two substructural ratios were determined out of 4950 substructural ratios, all possible combinations of 100 substructures (₁₀₀C₂), by selecting the greatest discriminatory power of inactive from active structures. The substructures used in this work include: the number of bonds comprising of the longest conjugate system, the number of skeletal atoms and the numbers of electron-donor pairs at certain distances in the molecule.     

Variable selection and model validation of 2D and 3D molecular descriptors>

We have found that molecular shape and electrostatics, in conjunction with 2D structural fingerprints, are important variables in discriminating classes of active and inactive compounds. The subject of this paper is how to explore the selection of these variables and identify their relative importance in quantitative structure-activity relationships (QSAR) analysis. We show the use of these variables in a form of similarity searching with respect to a crystal structure of a known bound ligand. This analysis is then validated through k-fold cross-validation of enrichments via several common classifiers. Additionally, we show an effective methodology using the variables in hypothesis generation; namely, when the crystal structure of a bound ligand is not known.     

Variable selection and model validation of 2D and 3D molecular descriptors>

We have found that molecular shape and electrostatics, in conjunction with 2D structural fingerprints, are important variables in discriminating classes of active and inactive compounds. The subject of this paper is how to explore the selection of these variables and identify their relative importance in quantitative structure–activity relationships (QSAR) analysis. We show the use of these variables in a form of similarity searching with respect to a crystal structure of a known bound ligand. This analysis is then validated through k-fold cross-validation of enrichments via several common classifiers. Additionally, we show an effective methodology using the variables in hypothesis generation; namely, when the crystal structure of a bound ligand is not known.     

HPLC of Nitrogen-Bridged Compounds

Abstract

In the area of structure-activity relationships of nitrogen bridged compounds, certain structure-coherent physical properties with ion exhange HPLC behaviour has been studied. This paper illustrates the results in finding the best conditions to separate the various structural types of model compounds. For this purpose, ion exhange HPLC technique has proved as highly advantageous.     

ISOQUINO[2,1-c][1,3,2] BENZODIAZAPHOSPHORINE DERIVATIVES: NEW POTENTIAL AGENTS FOR CANCER CHEMOTHERAPY

Abstract

Three derivatives of 2-chloro-5,8,9,13b-tetrahydro-5-methyl-6H-Isoquino[2,1 -GI[1,3,2]benzodiazaphosphorine 6-oxides as well as its sulphides were synthesized with the aim of evaluating their antitumor properties. Three of the twenty one compounds were found to be significantly active (inhibition of tumor growth > 80%) in the Ehrlich ascites carcinoma screen. Several structure-activity relationships were indicated for antitumor activity in this screen. An aziridinyl substituted derivative, bis-(2-chloroethyl)amino substitution (3) also exhibited significant activity against the growth of P-388 lymphocytic Leukemia cells in male BDF, mice (% T/C = 147; % T/C > 125 is considered significant). The reference for activity comparison is cyclophosphamide or cytoxan i.e. [bis(2-chloroethyl)aino]-5,6-dihydro-2H-1,3,2-oxazaphosphorinane 2-oxide [having TIC × 100 = 339 at a dose of 65 mg/kg]     

On the Topological Sub-Structural Molecular Design (TOSS-MODE) in QSPR/QSAR and Drug Design Research

Abstract

A recently introduced graph-theoretical approach to the study of structure-property-activity relationships is presented. The theoretical approach and the computational strategy for the use of the TOSS-MODE approach are given with details. Several QSPR and QSAR applications are reviewed including the study of physical properties of organic compounds, diamagnetic susceptibilities, and biological properties. The applications of the TOSS-MODE approach to discrimination of active/inactive compounds, the virtual screening of compounds with a desired property from databases of chemical structures, identification of active/inactive fragments and its relationships with 2D/3D pharmacophores, and to the design of novel compounds with desired biological activities are also reviewed.     

Design of multitarget activity landscapes that capture hierarchical activity cliff distributions

An activity landscape model of a compound data set can be rationalized as a graphical representation that integrates molecular similarity and potency relationships. Activity landscape representations of different design are utilized to aid in the analysis of structure-activity relationships and the selection of informative compounds. Activity landscape models reported thus far focus on a single target (i.e., a single biological activity) or at most two targets, giving rise to selectivity landscapes. For compounds active against more than two targets, landscapes representing multitarget activities are difficult to conceptualize and have not yet been reported. Herein, we present a first activity landscape design that integrates compound potency relationships across multiple targets in a formally consistent manner. These multitarget activity landscapes are based on a general activity cliff classification scheme and are visualized in graph representations, where activity cliffs are represented as edges. Furthermore, the contributions of individual compounds to structure-activity relationship discontinuity across multiple targets are monitored. The methodology has been applied to derive multitarget activity landscapes for compound data sets active against different target families. The resulting landscapes identify single-, dual-, and triple-target activity cliffs and reveal the presence of hierarchical cliff distributions. From these multitarget activity landscapes, compounds forming complex activity cliffs can be readily selected.     

Stereocontrolled synthesis of clerodin homolog: A Synthetic approach to structure-activity relationships

In order to elucidate the structure-activity relationships of the antifeeding di-terpenes, clerodin homolog $\text{5}$ was stereoselectively synthesized through 18 steps $\text{via}$ a key intermediate $\text{12}$. The perhydrofuro[2,3-b]furan ring in the synthesized homolog was less stable than that of the natural product, and its reactivity on methanolysis and potency of the antifeeding activity were almost the same as those of a 2,6-di-methylphenyl derivative $\text{10}$ which is more sterically restricted than a phenyl derivative $\text{2}$. The findings supported the hypothesis for the relationships on the structure (stereostructure) and activity of biological active substances. The methodology is conceptually termed “Dynamic structure-activity relationships,” and is effective from the standpoint of drug design.     

含有异噁唑环的β-氨基酮的合成及其抗糖尿病活性的初步研究

为了寻找新型高效低毒的抗糖尿病分子,由Mannich反应一步合成了15个新的含有异噁唑结构单元的β-氨基酮,制备方法简便、反应条件温和、产品易纯化,收率为51.2%～89.3%.所得化合物结构均通过1HNMR,13CNMR,ESIMS和HRMS表征.生物活性实验结果表明,在低浓度范围,目标分子对蛋白质酪氨酸磷酸酶1B和α-葡萄糖苷酶抑制活性不高,但对过氧化物酶体增殖物激活受体的激动活性强度中等,其中化合物1-(3,4-二氯苯基)-3-(5-甲基异噁唑-3-氨基)-3-(6-甲氧基萘-2-基)-1-丙酮(15)的过氧化物酶体增殖因子活化受体(PPAR)激动活性最好,达到75.3%,值得进一步研究.还讨论了目标分子的合成条件及其结构-活性关系.     

Molecular quantum similarity analysis of estrogenic activity

The main objective of this study was to evaluate the capability of 120 aromatic chemicals to bind to the human alpha estrogen receptor (hER alpha) by the use of quantum similarity methods. The experimental data were segregated into two categories, i.e., those compounds with and without estrogenicity activity (active and inactive). To identify potential ligands, semiquantitative structure-activity relationships were developed for the complete set correlating the presence or lack of binding affinity to the estrogen receptor with structural features of the molecules. The structure-activity relationships were based upon molecular similarity indices, which implicitly contain information related to changes in the electron distributions of the molecules, along with indicator variables, accounting for several structural features. In addition, the whole set was split into several chemical classes for modeling purposes. Models were validated by dividing the complete set into several training and test sets to allow for external predictions to be made.     

Sar modeling of unbalanced data sets

Abstract

The increased acceptance of SAR approaches to hazard identification has led us to investigate methods to improve the predictive performance of SAR models. In the present study we demonstrate that although on theoretical grounds the ratio of active to inactive chemicals in the learning set should be unity, SAR models can ?tolerate‘ an unbalanced range in ratios from 3 : 1 (i.e., 75% actives) to 1 : 2 (i.e., 33% actives) and still perform adequately. On the other hand SAR models derived from learning sets with ratios in excess of 4 : 1 (80% actives), even when corrected for the initial ratio do not perform satisfactorily.     

Antimicrobial properties of volatile phenylpropanes

The examination of antimicrobial structure-activity relationships of 93 volatile phenylpropanes (VPs) and 21 related aromatic compounds revealed a dependence of antimicrobial activity from the kind and number of substituents on the aromatic ring, their substitution pattern and microbial characteristics, such as Gram coloring and strain specific factors. Eugenol isomers were predominantly inhibitory in a concentration range from 25 to 2000 mg/L against all microorganisms tested, which were three strains of Escherichia coli and Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, Bacillus subtilis, Listeria monocytogenes, and Candida albicans. Etherified VPs were either less active or inactive depending on the type of side chain and/or substitution pattern. Differences in the antimicrobial activity of cis- and trans-isomers were observed. Species specific structure-activity relationships exist as was demonstrated with the Gram-negative bacteria (inactivity of E-ortho-eugenol) C. albicans (activity of di- and threefold methoxylated 1-propenylbenzenes), S. aureus and B. subtilis (activity of di-ortho methoxylated phenolic allylbenzenes and hydroquinone derivatives). With regard to the variety of observed specific effects and natural variation of susceptibility towards VPs according to literature reference data, the chances for successful prediction by computational analysis (QSAR) appear to be limited.     

α-氰基丙烯酸酯及其衍生物的合成与农药生物活性研究进展

丙烯酸酯是一类具有良好生物活性的化合物, 由于它作用机制新颖、环境友好, 故其分子设计和合成及生物活性成为目前农药创制的热点之一, 按其在农药应用方面的不同活性作用进行分类, 综述了丙烯酸酯及其类似物的合成、生物活性与除草活性方面构效关系的研究进展.