Mechanism of toxicity of esters of caffeic and dihydrocaffeic acids

Ten esters each of caffeic acid and dihydrocaffeic acid have recently been synthesized. Cytotoxicity evaluations of these esters versus L1210 leukemia and MCF-7 breast cancer cells in culture have led to the delineation of substantially different QSAR for each series. The L1210 QSAR for dihydrocaffeic acid esters resembles the QSAR obtained for simple phenols and estrogenic phenols. However, the QSAR pertaining to the caffeic acid esters differs considerably from its sister QSAR. This difference may be attributed to the presence of the olefinic linkage in the side chain. The octyl ester of caffeic acid is nearly ten times as toxic to the leukemia cells than the widely studied phenethyl ester, CAPE.     

Quantitative structure-activity relationships of cysteine hydrolases. Ficin hydrolysis of X-phenyl-N-methanesulfonyl glycinates

The hydrolysis of ficin of 33 X-Phenyl-N-methanesulfonyl glycinates has been studied. The resulting Km-values have been used to derive a quantitative structure-activity relationship (QSAR). The QSAR for ficin is compared with QSAR for other cysteine hydrolases. The comparisons show that although there are specific differences, overall the reaction mechanisms are very similar.     

ECVAM's activities on computer modelling and integrated testing

This paper introduces the basic concepts of quantitative structure-activity relationship (QSAR), expert system and integrated testing strategy, and explains how the analogy between QSARs and prediction models leads naturally to criteria for the validation of QSARs. ECVAM's in-house research programme on QSAR modelling and integrated testing is summarised, along with plans for the validation of QSAR models and expert system rulebases at the European Union level.     

Thermodynamic quantitative structure-activity relationship analysis for enzyme-ligand interactions in aqueous phosphate buffer and organic solvent

Thermodynamic quantitative structure-activity relationships (QSAR) for chymotrypsin-ligand binding is developed, and the results are compared for the effects of organic solvent on the substrate specificity of the enzymes to those in aqueous phosphate buffer. This is the first of such analysis utilizing thermodynamic QSAR. A possible explanation for the difference describing the effects of organic solvent for the binding of substituted phenyl esters of N-benzoyl L-alanine analogues [PhCONHCH(Me)COOC(6)H(4)-p-X, I] observed in both the classical and the thermodynamic QSAR is presented.     

QSAR of anticancer compounds. Bis(11-oxo-11H-indeno[1,2-b]quinoline-6-carboxamides), bis(phenazine-1-carboxamides), and bis(naphthalimides)

QSAR have been developed for the anticancer activity (growth inhibition) of various tumor cells by bis(11-oxo-11H-indeno[1,2-b]quinoline-6-carboxamides), bis(phenazine-1-carboxamides), and bis(naphthalimides). Of the seven QSAR, positive hydrophobic interactions are found in only two examples: bis(naphthalimides) versus human colon cancer cells. This is consistent with other QSAR of anticancer compounds where hydrophobic interactions are found to be unimportant.     

Quantitative structure–activity relationship analysis using deep learning based on a novel molecular image input technique

Quantitative structure–activity relationship (QSAR) analysis uses structural, quantum chemical, and physicochemical features calculated from molecular geometry as explanatory variables predicting physiological activity. Recently, deep learning based on advanced artificial neural networks has demonstrated excellent performance in the discipline of QSAR research. While it has properties of feature representation learning that directly calculate feature values from molecular structure, the use of this potential function is limited in QSAR modeling. The present study applied this function of feature representation learning to QSAR analysis by incorporating 360° images of molecular conformations into deep learning. Accordingly, I successfully constructed a highly versatile identification model for chemical compounds that induce mitochondrial membrane potential disruption with the external validation area under the receiver operating characteristic curve of ≥0.9.     

Binding affinity prediction of novel estrogen receptor ligands using receptor-based 3-D QSAR methods

We have recently reported the development of a 3-D QSAR model for estrogen receptor ligands showing a significant correlation between calculated molecular interaction fields and experimentally measured binding affinity. The ligand alignment obtained from docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection procedure, a significant and robust model was obtained (q²_LOO=0.921, SDEP=0.345). To further analyze the robustness and the predictivity of the established model several recently developed estrogen receptor ligands were selected as external test set. An excellent agreement between predicted and experimental binding data was obtained indicated by an external SDEP of 0.531. Two other traditionally used prediction techniques were applied in order to check the performance of the receptor-based 3-D QSAR procedure. The interaction energies calculated on the basis of receptor–ligand complexes were correlated with experimentally observed affinities. Also ligand-based 3-D QSAR models were generated using program FlexS. The interaction energy-based model, as well as the ligand-based 3-D QSAR models yielded models with lower predictivity. The comparison with the interaction energy-based model and with the ligand-based 3-D QSAR models, respectively, indicates that the combination of receptor-based and 3-D QSAR methods is able to improve the quality of prediction.     

QSAR studies on the activation of the human carbonic anhydrase cytosolic isoforms I and II and secretory isozyme VI with amino acids and amines

The first QSAR study on the activation of the human secretory isoform of the metalloenzyme carbonic anhydrase (CA, EC 4.2.1.1), CA VI, with a series of amines and amino acids is reported. A large set of topological indices have been used to obtain several tri-/tetra-parametric models. We compared the CA VI activating QSAR models with those calculated for activation of the cytosolic human isozymes hCA I and hCA II. In addition, the effect of D- and L-amino acids as activators of hCA I, hCA II and of hCA VI as compared to those of structurally related biogenic amines was investigated for obtaining statistically significant and predictive QSAR equations. The obtained models are discussed using a variety of statistical parameters. The best models were obtained for hCA II activation, followed by hCA I, whereas the QSAR models for the activation of hCA VI were statistically weaker.     

On the role of polarizability in QSAR

The polarizability of a molecule, an important physical property, is currently attracting our attention particularly in the area of QSAR for chemical-biological interactions. In this report, the polarizability effects on ligand-substrate interactions has been discussed in terms of NVE (number of valence electrons) using additive values for valence electrons and the formulation of a total number of 51 QSAR. The QSAR model can be illustrated by Eq. I. log 1/C = a(NVE) +/- constant     

Human protein tyrosine phosphatase 1B inhibitors: QSAR by genetic function approximation

Protein tyrosine phosphatase 1B (PTP 1B), a negative regulator of insulin receptor signaling system, has emerged as a highly validated, attractive target for the treatment of non-insulin dependent diabetes mellitus (NIDDM) and obesity. As a result there is a growing interest in the development of potent and specific inhibitors for this enzyme. This quantitative structure-activity relationship (QSAR) study for a series of formylchromone derivatives as PTP lB inhibitors was performed using genetic function approximation (GFA) technique. The QSAR models were developed using a training set of 29 compounds and the predictive ability of the QSAR model was evaluated against a test set of 7 compounds. The internal and external consistency of the final QSAR model was 0.766 and 0.785. The statistical quality of QSAR models was assessed by statistical parameters r2, r2 (crossvalidated r2), r2pred (predictive r2) and lack of fit (LOF) measure. The results indicate that PTP lB inhibitory activity of the formylchromone derivatives is strongly dependent on electronic, thermodynamic and shape related parameters.     

The role of quantitative structure--activity relationships (QSAR) in biomolecular discovery

Empirical methods for building predictive models of the relationships between molecular structure and useful properties are becoming increasingly important. This has arisen because drug discovery and development have become more complex. A large amount of biological target information is becoming available through molecular biology. Automation of chemical synthesis and pharmacological screening has also provided a vast amount of experimental data. Tools for designing libraries and extracting information from molecular databases and high-throughput screening experiments robustly and quickly enable leads to be discovered more effectively. As drug leads progress down the development pipeline, the ability to predict physicochemical, pharmacokinetic and toxicological properties of these leads is becoming increasingly important in reducing the number of expensive, late development failures. Quantitative structure-activity relationship (QSAR) methods have much to offer in these areas. However, QSAR analysis has many traps for unwary practitioners. This review introduces the concepts behind QSAR, points out problems that may be encountered, suggests ways of avoiding the pitfalls and introduces several exciting, new QSAR methods discovered during the last decade.     

QSAR of apoptosis induction in various cancer cells

In continuing our QSAR study of apoptosis, we consider in this report the action of phenolic compounds on Ramos cells (non-Hodgkins B-cell lymphoma): the effect of O-8-thapsigargin analogues on human prostate cancer cells, Tsu-Pr-1 and the induction of apoptosis of a complex set of congeners on human fibrosarcoma cells HT 1080. The human prostate cancer cells activity is very similar to that of the Ramos cells. While the QSAR for the fibrosarcoma cells resembles that of our earlier study with L1210 leukemia cells. The two different types of QSAR suggest at least two quite different types of receptors for the induction of apoptosis.     

Quantitative structure-activity relationships and the prediction of MHC supermotifs

The underlying assumption in quantitative structure-activity relationship (QSAR) methodology is that related chemical structures exhibit related biological activities. We review here two QSAR methods in terms of their applicability for human MHC supermotif definition. Supermotifs are motifs that characterise binding to more than one allele. Supermotif definition is the initial in silico step of epitope-based vaccine design. The first QSAR method we review here--the additive method--is based on the assumption that the binding affinity of a peptide depends on contributions from both amino acids and the interactions between them. The second method is a 3D-QSAR method: comparative molecular similarity indices analysis (CoMSIA). Both methods were applied to 771 peptides binding to 9 HLA alleles. Five of the alleles (A*0201, A*0202, A*0203, A*0206 and A*6802) belong to the HLA-A2 superfamily and the other four (A*0301, A*1101, A*3101 and A*6801) to the HLA-A3 superfamily. For each superfamily, supermotifs defined by the two QSAR methods agree closely and are supported by many experimental data.     

基于支持向量回归的酚类化合物QSAR建模

目的：采用定量构效关系（QSAR）方法探索酚类化合物的毒性与分子结构参数的关系。方法：基于支持向量回归（SVR）、依均方误差最小原则选择最优核函数,对酚类化合物及其衍生物进行了QSAR研究。结果：不同数据集选取的最优核函数有异,对小样本、非线性等问题,SVR具有较优的稳定性及预测能力,在酚类化合物及其衍生物的QSAR研究中得到了优于原文献方法的独立预测结果。结论：SVR模型具有较好的预测能力,在QSAR及相关研究中可得到更广泛应用。