Biomedical subjects

Yun-De Xiao

Publications and source records attributed to Yun-De Xiao.

9 recordsLinked to original sources

PubMed2005

Quantitative structure-activity relationships of methotrexate and methotrexate analogues transported by the rat multispecific resistance-associated protein 2 (rMrp2).

Quantitative structure-activity relationship (QSAR) computational methods were performed to characterize structural requirements and molecular features for rMrp2-mediated methotrexate (MTX) transport. The compounds used in this analysis included MTX and 24 MTX analogues, with activity assessed by measuring inhibition of (3)H-DNP-SG ((3)H-S-(2,4)-dinitrophenyl glutathione) uptake in rat canalicular membrane vesicles. 2D-QSAR modeling using simulated annealing partial least squares (SA-PLS) method identified octanol/water partition coefficient, hydrophobicity, and negative charge as three important factors for MTX and MTX analogue affinity to rMrp2. Further analysis using 3D-QSAR method identified a pharmacophore model consisting of two hydrophobes, two aromatic rings, and a negative ionizable group as the critical molecular features that predict binding affinity of these compounds to rMRP2. The addition of a benzoyl ornithine group at a 9.3A distance and 136.5 degrees vector from the negative ionizable structure of MTX resulted in a 40-fold more potent inhibition of DNP-SG transport, suggesting that this chemical modification, while not essential for activity, contributes to the transport of MTX analogue by rMrp2. These observations provide important insights to the rationale development of analogues of MTX for the treatment of neoplastic and immunological diseases that may be devoid of hepatotoxicity or lack drug resistance.

ATP-Binding Cassette Transporters↗

PubMed2004

Targacept active conformation search: a new method for predicting the conformation of a ligand bound to its protein target.

Targacept active conformation search (TACS) is a novel variation of well-established three-dimensional quantitative structure--activity relationship methodologies that seeks to determine probable conformation(s) of ligands bound to their protein targets. A combination of affinity or activity data and energetically accessible conformational ensembles, each conformer described by three-dimensional (3-D) sensitive descriptors, forms the basis of the TACS data model. Recursive pruning is used to reduce the size of both the conformational ensemble and the descriptor space until the TACS data model contains just enough information to determine probable conformation(s) of ligands bound to their protein targets. The TACS algorithm is comprised of five components: (1) conformational ensemble generation, (2) 3-D sensitive descriptor calculation, (3) ensemble descriptor preprocessing, (4) model generation, and (5) prediction of bound conformation(s). Significantly, this method precludes the need for subjective or objective molecular alignment. We report the application of this technique to five benchmark protein-ligand couples where the conformation of a bound ligand has been previously established using X-ray crystallography: 9-cis-retinoic (1) and 9-trans-retinoic acid (2), both agonists for the retinoic acid receptor gamma, compounds KH1060 (3) and MC1288 (4), which bind to the vitamin D3 receptor, and R04 (5), an inhibitor bound to human rhinovirus 14 thermolysin. The binding conformations predicted by TACS were compared to the crystallographic structures extracted from their respective binding sites using root-mean-squared deviation (rmsd) criteria. Three of the conformations found using TACS were within crystallographic error. 9-cis-Retinoic acid, 9-trans-retinoic acid, and MC1288, when superimposed on their crystallographic structures, gave rmsd values of 0.22, 0.17, and 0.34 A, respectively. The rmsd values for KH1060 (1.54 A) and R04 (1.01 A) were larger but still reasonable.

Binding Sites↗

PubMed2004

Modeling of p38 mitogen-activated protein kinase inhibitors using the Catalyst HypoGen and k-nearest neighbor QSAR methods.

We have employed in parallel the Catalyst HypoGen pharmacophore modeling approach and the variable selection k-nearest neighbor quantitative structure-activity relationship (kNN QSAR) method to model a diverse data set of p38 mitogen-activated protein (MAP) kinase inhibitors. The HypoGen pharmacophore model, developed from a novel automated training set selection protocol, identified chemical functional features that were characteristic of the active compounds and differentiated the active from the inactive inhibitors. The kNN QSAR modeling employed topological descriptors and afforded predictive QSAR models with consistently high values of both leave-one-out cross-validated R2 for the training set and predictive R2 for the test set. The results of both modeling approaches were sensitive to the selection of the training and test sets used for model development and validation. The resulting Catalyst pharmacophore and kNN QSAR models can be used concurrently for rapid virtual screening of chemical databases to identify novel p38 MAP kinase inhibitors.

Algorithms↗

PubMed2002

Antitumor agents. 213. Modeling of epipodophyllotoxin derivatives using variable selection k nearest neighbor QSAR method.

We have applied a variable selection k nearest neighbor quantitative structure-activity relationship (kNN QSAR) method to develop predictive QSAR models for 157 epipodophyllotoxins synthesized previously in our ongoing effort to develop potential anticancer agents. QSAR models were generated using multiple topological descriptors of chemical structures, including molecular connectivity indices (MCI) and molecular operating environment descriptors. The 157 compounds were separated into several training and test sets. The robustness of QSAR models was characterized by the values of the internal leave one out cross-validated R2 (q2) for the training set and external predictive R2 for the test set. The significance of the training set models was confirmed by statistically higher values of q2 for the original data set as compared to q2 values for the same data set with randomly shuffled activities. kNN QSAR models were compared with those obtained with the comparative molecular field analysis method; the kNN QSAR approach afforded models with higher values of both q2 and predictive R2. One of the best models obtained from kNN analysis using MCI as descriptors provided q2 and predictive R2 values of 0.60 and 0.62, respectively. QSAR models developed in these studies shall aid in future design of novel potent epipodophyllotoxin derivatives.

Antineoplastic Agents↗

PubMedDate not supplied

Genetic algorithms and self-organizing maps: a powerful combination for modeling complex QSAR and QSPR problems.

Modeling non-linear descriptor-target activity/property relationships with many dependent descriptors has been a long-standing challenge in the design of biologically active molecules. In an effort to address this problem, we couple the supervised self-organizing map with the genetic algorithm. Although self-organizing maps are non-linear and topology-preserving techniques that hold great potential for modeling and decoding relationships, the large number of descriptors in typical quantitative structure-activity relationship or quantitative structure-property relationship analysis may lead to spurious correlation(s) and/or difficulty in the interpretation of resulting models. To reduce the number of descriptors to a manageable size, we chose the genetic algorithm for descriptor selection because of its flexibility and efficiency in solving complex problems. Feasibility studies were conducted using six different datasets, of moderate-to-large size and moderate-to-great diversity; each with a different biological endpoint. Since favorable training set statistics do not necessarily indicate a highly predictive model, the quality of all models was confirmed by withholding a portion of each dataset for external validation. We also address the variability introduced onto modeling through dataset partitioning and through the stochastic nature of the combined genetic algorithm supervised self-organizing map method using the z-score and other tests. Experiments show that the combined method provides comparable accuracy to the supervised self-organizing map alone, but using significantly fewer descriptors in the models generated. We observed consistently better results than partial least squares models. We conclude that the combination of genetic algorithms with the supervised self-organizing map shows great potential as a quantitative structure-activity/property relationship modeling tool.

Algorithms↗

PubMedDate not supplied

Combinatorial QSAR of ambergris fragrance compounds.

A combinatorial quantitative structure-activity relationships (Combi-QSAR) approach has been developed and applied to a data set of 98 ambergris fragrance compounds with complex stereochemistry. The Combi-QSAR approach explores all possible combinations of different independent descriptor collections and various individual correlation methods to obtain statistically significant models with high internal (for the training set) and external (for the test set) accuracy. Seven different descriptor collections were generated with commercially available MOE, CoMFA, CoMMA, Dragon, VolSurf, and MolconnZ programs; we also included chirality topological descriptors recently developed in our laboratory (Golbraikh, A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158). CoMMA descriptors were used in combination with MOE descriptors. MolconnZ descriptors were used in combination with chirality descriptors. Each descriptor collection was combined individually with four correlation methods, including k-nearest neighbors (kNN) classification, Support Vector Machines (SVM), decision trees, and binary QSAR, giving rise to 28 different types of QSAR models. Multiple diverse and representative training and test sets were generated by the divisions of the original data set in two. Each model with high values of leave-one-out cross-validated correct classification rate for the training set was subjected to extensive internal and external validation to avoid overfitting and achieve reliable predictive power. Two validation techniques were employed, i.e., the randomization of the target property (in this case, odor intensity) also known as the Y-randomization test and the assessment of external prediction accuracy using test sets. We demonstrate that not every combination of the data modeling technique and the descriptor collection yields a validated and predictive QSAR model. kNN classification in combination with CoMFA descriptors was found to be the best QSAR approach overall since predictive models with correct classification rates for both training and test sets of 0.7 and higher were obtained for all divisions of the ambergris data set into the training and test sets. Many predictive QSAR models were also found using a combination of kNN classification method with other collections of descriptors. The combinatorial QSAR affords automation, computational efficiency, and higher probability of identifying significant QSAR models for experimental data sets than the traditional approaches that rely on a single QSAR method.

Algorithms↗

PubMedDate not supplied

Supervised self-organizing maps in drug discovery. 1. Robust behavior with overdetermined data sets.

The utility of the supervised Kohonen self-organizing map was assessed and compared to several statistical methods used in QSAR analysis. The self-organizing map (SOM) describes a family of nonlinear, topology preserving mapping methods with attributes of both vector quantization and clustering that provides visualization options unavailable with other nonlinear methods. In contrast to most chemometric methods, the supervised SOM (sSOM) is shown to be relatively insensitive to noise and feature redundancy. Additionally, sSOMs can make use of descriptors having only nominal linear correlation with the target property. Results herein are contrasted to partial least squares, stepwise multiple linear regression, the genetic functional algorithm, and genetic partial least squares, collectively referred to throughout as the "standard methods". The k-nearest neighbor (kNN) classification method was also performed to provide a direct comparison with a different classification method. The widely studied dihydrofolate reductase (DHFR) inhibition data set of Hansch and Silipo is used to evaluate the ability of sSOMs to classify unknowns as a function of increasing class resolution. The contribution of the sSOM neighborhood kernel to its predictive ability is assessed in two experiments: (1) training with the k-means clustering limit, where the neighborhood radius is zero throughout the training regimen, and (2) training the sSOM until the neighborhood radius is reduced to zero. Results demonstrate that sSOMs provide more accurate predictions than standard linear QSAR methods.

Algorithms↗

PubMedDate not supplied

Supervised self-organizing maps in drug discovery. 2. Improvements in descriptor selection and model validation.

The modeling of nonlinear descriptor-target relationships is a topic of considerable interest in drug discovery. We, herein, continue reporting the use of the self-organizing map-a nonlinear, topology-preserving pattern recognition technique that exhibits considerable promise in modeling and decoding these relationships. Since simulated annealing is an efficient tool for solving optimization problems, we combined the supervised self-organizing map with simulated annealing to build high-quality, highly predictive quantitative structure-activity/property relationship models. This technique was applied to six data sets representing a variety of biological endpoints. Since a high statistical correlation in the training set does not indicate a highly predictive model, the quality of all the models was confirmed by withholding a portion of each data set for external validation. Finally, we introduce new cross-validation and dynamic partitioning techniques to address model overfitting and assessment.

Algorithms↗

PubMedDate not supplied

Rational selection of training and test sets for the development of validated QSAR models.

Quantitative Structure-Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors (kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R2 (q2) is an inadequate characteristic to assess the predictive ability of the models [Golbraikh, A., Tropsha, A. Beware of q2! J. Mol. Graphics Mod. 20, 269-276, (2002)]. Herein, we provide additional evidence that there exists no correlation between the values of q2 for the training set and accuracy of prediction (R2) for the test set and argue that this observation is a general property of any QSAR model developed with LOO cross-validation. We suggest that external validation using rationally selected training and test sets provides a means to establish a reliable QSAR model. We propose several approaches to the division of experimental datasets into training and test sets and apply them in QSAR studies of 48 functionalized amino acid anticonvulsants and a series of 157 epipodophyllotoxin derivatives with antitumor activity. We formulate a set of general criteria for the evaluation of predictive power of QSAR models.

Algorithms↗