三苯基丙烯腈衍生物的定量结构-活性关系研究

应用半经验量子化学AMl方法得到了24种三苯基丙烯腈衍生物的优势构象，利用AMl方法和分子图形学技术获得其电子结构、几何结构及拓扑结构参数，并将这些参数与三苯基丙烯腈衍生物对乳腺癌细胞(MCF-7)的生物活性相关联．结果表明，三苯基丙烯腈衍生物对MCF-7的活性与羟基指示数、分子表面积和B环上原子净电荷之和之间存在良好的多元线性相关性，成功地建立了三苯基丙烯腈衍生物的构效关系式．     

Three-dimensional quantitative structure-activity relationships of steroid aromatase inhibitors

Summary Inhibition of aromatase, a cytochrome P450 that converts androgens to estrogens, is relevant in the therapeutic control of breast cancer. We investigate this inhibition using a three-dimensional quantitative structure-activity relationship (3D QSAR) method known as Comparative Molecular Field Analysis, CoMFA [Cramer III, R.D. et al., J. Am. Chem. Soc., 110 (1988) 5959]. We analyzed the data for 50 steroid inhibitors [Numazawa, M. et al., J. Med. Chem., 37 (1994) 2198, and references cited therein] assayed against androstenedione on human placental microsomes. An initial CoMFA resulted in a three-component model for log(1/K_i), with an explained variance r² of 0.885, and a cross-validated q² of 0.673. Chemometric studies were performed using GOLPE [Baroni, M. et al., Quant. Struct.-Act. Relatsh., 12 (1993) 9]. The CoMFA/GOLPE model is discussed in terms of robustness, predictivity, explanatory power and simplicity. After randomized exclusion of 25 or 10 compounds (repeated 25 times), the q² for one component was 0.62 and 0.61, respectively, while r² was 0.674. We demonstrate that the predictive r² based on the mean activity (Y_m) of the training set is misleading, while the test set Y_m-based predictive r² index gives a more accurate estimate of external predictivity. Using CoMFA, the observed differences in aromatase inhibition among C6-substituted steroids are rationalized at the atomic level. The CoMFA fields are consistent with known, potent inhibitors of aromatase, not included in the model. When positioned in the same alignment, these compounds have distinct features that overlap with the steric and electrostatic fields obtained in the CoMFA model. The presence of two hydrophobic binding pockets near the aromatase active site is discussed: a steric bulk tolerant one, common for C4, C6-alpha and C7-alpha substitutents, and a smaller one at the C6-beta region.     

Quantitative structure-activity relationships for a series of selective estrogen receptor-beta modulators

The estrogen receptor-beta subtype (ERβ) is an attractive drug target for the development of novel therapeutic agents for hormone replacement therapy. Hologram quantitative structure-activity relationships (HQSAR) were conducted on a series of 6-phenylnaphthalene and 2-phenylquinoline derivatives, employing values of ERβ binding affinity. A training set of 65 compounds served to derive the models. The best statistical HQSAR model (q ²?=?0.73 and r ²?=?0.91) was generated using atoms, bonds, connections and donor and acceptor as fragment distinction parameters, and fragment size default (4–7) with hologram length of 199. The model was used to predict the binding affinity of an external test set of 16 compounds, and the predicted values were in good agreement with the experimental results. The final HQSAR model and the information obtained from 2D contribution maps should be useful for the design of novel ERβ modulators having improved affinity.     

Quantitative structure-activity relationships for a series of selective estrogen receptor-beta modulators

The estrogen receptor-beta subtype (ERbeta) is an attractive drug target for the development of novel therapeutic agents for hormone replacement therapy. Hologram quantitative structure-activity relationships (HQSAR) were conducted on a series of 6-phenylnaphthalene and 2-phenylquinoline derivatives, employing values of ERbeta binding affinity. A training set of 65 compounds served to derive the models. The best statistical HQSAR model (q(2) = 0.73 and r(2) = 0.91) was generated using atoms, bonds, connections and donor and acceptor as fragment distinction parameters, and fragment size default (4-7) with hologram length of 199. The model was used to predict the binding affinity of an external test set of 16 compounds, and the predicted values were in good agreement with the experimental results. The final HQSAR model and the information obtained from 2D contribution maps should be useful for the design of novel ERbeta modulators having improved affinity.     

Quantitative structure-activity relationships of H1-antihistaminic benzimidazole derivatives

The quantitative structure-activity relationships (QSAR) of 2-(4-substituted-1-piperazinyl)benzimidazole derivatives for antihistaminic activity were examined. Taking into consideration the specific conformations of some derivatives, a significant correlation was obtained using Verloop's STERIMOL parameters B3 and L of the substituent at the 1-position of the benzimidazole nucleus. The results indicated that the derivatives having a substituent with a small breadth and an appropriate length at the 1-position showed potent activity. From the results, a model of the binding site is proposed. The QSAR of side effects (anticholinergic activity and central nervous system depressive effect) were also examined and the results showed that a sterically small substituent at the 1-position was required to decrease side effects.     

Angiotensin-converting enzyme inhibitors: synthesis and structure-activity relationships of potent N-benzyloxycarbonyl tripeptide inhibitors

A new series of gamma-D-Glu-containing N-benzyloxycarbonyl (Z) tripeptide inhibitors of angiotensin-converting enzyme (ACE) was synthesized. The effect of varying the antepenultimate amino acid residue in this series on the biological activity was studied. Introduction of Lys and Orn residues at the P1 position provided the most potent inhibitors, 25a and 25b (IC50: 3.5 and 4.9 x 10(-9) M, respectively), which exhibited an oral antihypertensive activity. This result suggests that basic amino acid residues at the P1 position play an important role in binding with the S1 subsite of ACE in this series. Oral antihypertensive activity of selected compounds was evaluated.     

Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) modelling using comparative molecular similarity indices analysis (CoMSIA) was applied to a series of 406 structurally diverse dihydrofolate reductase (DHFR) inhibitors from Pneumocystis carinii (pc) and rat liver (rl). X-ray crystal structures of three inhibitors bound to pcDHFR were used for defining the alignment rule. For pcDHFR, a QSAR model containing 6 components was selected using leave-10%-out cross-validation (n= 240, q2 = 0.65), while a 4-component model was selected for rlDHFR (n= 237, q2 = 0.63); both include steric, electrostatic and hydrophobic contributions. The models were validated using a large test set, designed to maximise its diversity and to verify the predictive accuracy of models for extrapolation. The pcDHFR model has r2 = 0.60 and mean absolute error (MAE) = 0.57 for the test set after removing 4 outliers, and the rlDHFR model has r2 = 0.60 and MAE = 0.69 after removing 4 test set outliers. In addition, classification models predicting selectivity for pcDHFR over rlDHFR were developed using soft independent modelling by class analogy (SIMCA), with a selectivity ratio of 2 (IC50,rlDHFR/ IC50,pcDHFR) used for delimiting classes. A 5-component model including steric and electrostatic contributions has cross-validated and test set classification rates of 0.67 and 0.68 for selective inhibitors, and 0.85 and 0.72 for unselective inhibitors. The predictive accuracy of models, together with the identification of important contributions in QSAR and classification models, offer the possibility of designing potent selective inhibitors and estimating their activity prior to synthesis.     

Exploring structure-activity relationships of tricyclic farnesyltransferase inhibitors using ECLiPS libraries

The development of structure-activity relationships (SARs) relating to the function of a biological protein is often a long and protracted undertaking when using an iterative medicinal chemistry approach. High throughput screening of ECLiPS (Encoded Combinatorial Libraries on Polymeric Support) libraries can be used to simplify this process. In this paper, we illustrate how a large ECLiPS library of 26,908 compounds, based on a tricyclic core structure, was used to define a multitude of SARs for the oncogenic target, farnesyltransferase (FTase). This library, FT-2, was prepared using a split-and-pool approach in which small molecules are constructed on resin that contains tag/linker constructs to track the synthetic process [1-5] Highly defined SARs were produced from this screen that enhanced our understanding of FTase binding site interactions. The pivotal compounds culled from this library were potent in both cell-free and cell-based FTase assays, selective over the closely related enzyme, geranylgeranyltransferase I (GGTase I), and inhibited the adherent-independent growth of a transformed cell line.     

PDK1的吲哚酮类抑制剂定量构效关系研究

3'磷酸肌醇依赖的激酶1(3'-phosphoinositide-dependent kinase1,PDK1)是--个63kDa的丝氨酸/苏氨酸(Ser/Thr)蛋白酶,它是Akt1的上游活化激酶~([1,2]).PDK1可在磷酸化的Akt1的Thr308位点使Akt1的活性增加30倍以上.但这种活化以依赖PIP3或PIP2的方式完成,PDK1也就因此而得名~([3]).     

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.     

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.