Biomedical subjects

B T Fan

Publications and source records attributed to B T Fan.

At least 19 recordsLinked to original sources

PubMed2006

MolDIA: XML based system of molecular diversity analysis towards virtual screening and QSPR.

In this paper a new chemoinformatics tool for Molecular Diversity Analysis (MolDIA) is introduced. The objective of this system is the analysis of molecular similarity and diversity through the treatment of structural and physicochemical information. Current needs for chemical databases include the analysis, the management and the retrieval of chemical information. The implementation of eXtended Markup Languages (XML) is proposed as a basis for representing and structuring the chemical information contained in data structures and databases. The adequate descriptor vector and related physicochemical properties have been defined and constructed. The benefits of XML in chemoinformatics are discussed, as well as, the applications of this system in a virtual screening environment.

Computational Biology↗

PubMed2006

Quantitative structure-toxicity relationships (QSTRs): a comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis.

Prediction of toxicity of 203 nitro- and cyano-aromatic chemicals to Tetrahymena pyriformis was carried out by radial basis function neural network, general regression neural network and support vector machine, in non-linear response surface methodology. Toxicity was predicted from hydrophobicity parameter (log Kow) and maximum superdelocalizability (Amax). Special attention was drawn to prediction ability and robustness of the models, investigated both in a leave-one-out and 10-fold cross validation (CV) processes. The influence that the corresponding changes in the learning sets during these CV processes could have on a common external test set including 41 compounds was also examined. This allowed us to establish the stability of the models. The non linear results slightly outperform (as expected) multilinear relationships (MLR) and also favourably compete with various other non linear approaches recently proposed by Ren (J. Chem. Inf. Comput. Sci., 43 1679 (2003)).

Animals↗

PubMed2006

Elucidation of chiral recognition processes of macrocyclic antibiotic vancomycin.

A theoretical investigation was carried out on the retention and separation of enantiomeric molecules including nonsteroidal anti-inflammatory drugs, anti-neoplastic compounds and N-derivatized amino acids by capillary electrophoresis using macrocyclic antibiotics, a new class of chiral selectors, as stationary phase. Firstly docking methods were used to study the enantiorecognition in chiral electrophoresis. The molecular dynamics simulations of the two diastereoisomer complexes were then performed in order to understand how these antibiotics recognize the enantiomers. Another approach was applied in this study to establish a quantitative structure-enantioselectivity relationship (QSER) model, able to describe the resolution of a series of chiral compounds in capillary electrophoresis using vancomycin as the resolving agent.

Anti-Bacterial Agents↗

PubMed2005

Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines.

The accurate nonlinear model for predicting the tissue/blood partition coefficients (PC) of organic compounds in different tissues was firstly developed based on least-squares support vector machines (LS-SVM), as a novel machine learning technique, by using the compounds' molecular descriptors calculated from the structure alone and the composition features of tissues. The heuristic method (HM) was used to select the appropriate molecular descriptors and build the linear model. The prediction result of the LS-SVM model is much better than that obtained by HM method and the prediction values of tissue/blood partition coefficients based on the LS-SVM model are in good agreement with the experimental values, which proved that nonlinear model can simulate the relationship between the structural descriptors, the tissue composition and the tissue/blood partition coefficients more accurately as well as LS-SVM was a powerful and promising tool in the prediction of the tissue/blood partition behaviour of compounds. Furthermore, this paper provided a new and effective method for predicting the tissue/blood partition behaviour of the compounds in the different tissues from their structures and gave some insight into structural features related to the partition process of the organic compounds in different tissues.

Least-Squares Analysis↗

PubMed2005

Quantitative structure-activity relationship models for prediction of sensory irritants (logRD50) of volatile organic chemicals.

Quantitative classification and regression models for prediction of sensory irritants (logRD50) of volatile organic chemicals (VOCs) have been developed. Each compound was represented by the calculated structural descriptors to encode constitutional, topological, geometrical, electrostatic, and quantum-chemical features. The heuristic method (HM) was then used to search the descriptor space and select the descriptors responsible for activity. The best classification results were found using support vector machine (SVM): the accuracy for training, test and overall data set is 96.5%, 85.7% and 94.4%, respectively. The nonlinear regression models were built by radial basis function neural networks (RNFNN) and SVM, respectively. The root mean squared errors (RMS) in prediction for the training, test and overall data set are 0.4755, 0.6322 and 0.5009 for reactive group, 0.2430, 0.4798 and 0.3064 for nonreactive group by RBFNN. The comparative results obtained by SVM are 0.4415, 0.7430 and 0.5140 for reactive group, 0.3920, 0.4520 and 0.4050 for nonreactive group, respectively. This paper proposes an effective method for poisonous chemicals screening and considering.

Irritants↗

PubMed2005

Application of support vector machine (SVM) for prediction toxic activity of different data sets.

As a new method, support vector machine (SVM) were applied for prediction of toxicity of different data sets compared with other two common methods, multiple linear regression (MLR) and RBFNN. Quantitative structure-activity relationships (QSAR) models based on calculated molecular descriptors have been clearly established. Among them, SVM model gave the highest q(2) and correlation coefficient R. It indicates that the SVM performed better generalization ability than the MLR and RBFNN methods, especially in the test set and the whole data set. This eventually leads to better generalization than neural networks, which implement the empirical risk minimization principle and may not converge to global solutions. We would expect SVM method as a powerful tool for the prediction of molecular properties.

Algorithms↗

PubMed2005

Support vector machine and the heuristic method to predict the solubility of hydrocarbons in electrolyte.

A new method support vector machine (SVM) and the heuristic method (HM) were used to develop nonlinear and linear models between the solubility in electrolyte containing sodium chloride and three molecular descriptors of 217 nonelectrolytes. The molecular descriptors representing the structural features of the compounds include two topological and one electrostatic descriptor. The three molecular descriptors selected by HM in CODESSA were used as inputs for SVM. The results obtained by HM and SVM both were satisfactory. The model of HM leads to a correlation coefficient (R) of 0.980 and root-mean-square error (RMS) of 0.219 for the test set. The same descriptors were also employed to build the model in pure water, and the prediction results were consistent with the experimental solubilities. Furthermore, a predictive correlation coefficient R = 0.988 and RMS error of 0.170 for the test set were obtained by SVM. The prediction results are in very good agreement with the experimental values. This paper provides a new and effective method for predicting the solubility in electrolyte and reveals some insight into the structural features that are related to the noneletrolytes.

Journal Article↗

PubMed2005

Study of probabilistic neural networks to classify the active compounds in medicinal plants.

Probabilistic neural networks (PNNs) were utilized for the classifications of 102 active compounds from diverse medicinal plants with anticancer activity against human rhinopharyngocele cell line KB. Molecular descriptors calculated from structure alone were used to represent molecular structures. A subset of the calculated descriptors selected using factor correlation analysis and forward stepwise regression was used to construct the prediction models. Linear discriminant analysis (LDA) was also utilized to construct the classification model to compare the results with those obtained by PNNs. The accuracy of the training set, the cross-validation set, and the test set given by PNNs and LDA were 100, 92.3, 90.9% and 71.8, 92.3, 54.5%, respectively, which indicated that the results obtained by PNNs agree well with the experimental values of these compounds and also revealed the superiority of PNNs over LDA approach for the classification of anticancer activities of compounds. The models built in this work would be of potential help in the design of novel and more potent anticancer agents.

Algorithms↗

PubMed2005

The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine.

Support vector machine (SVM), as a novel machine learning technique, was used for the prediction of the human oral absorption for a large and diverse data set using the five descriptors calculated from the molecular structure alone. The molecular descriptors were selected by heuristic method (HM) implemented in CODESSA. At the same time, in order to show the influence of different molecular descriptors on absorption and to well understand the absorption mechanism, HM was used to build several multivariable linear models using different numbers of molecular descriptors. Both the linear and non-linear model can give satisfactory prediction results: the square of correlation coefficient R(2) was 0.78 and 0.86 for the training set, and 0.70 and 0.73 for the test set respectively. In addition, this paper provides a new and effective method for predicting the absorption of the drugs from their structures and gives some insight into structural features related to the absorption of the drugs.

Administration, Oral↗

PubMed2005

QSAR study of natural, synthetic and environmental endocrine disrupting compounds for binding to the androgen receptor.

A large data set of 146 natural, synthetic and environmental chemicals belonging to a broad range of structural classes have been tested for their relative binding affinity (expressed as log (RBA)) to the androgen receptor (AR). These chemicals commonly termed endocrine disrupting compounds (EDCs) present a variety of adverse effects in humans and animals. As assays for binding affinity remains a time-consuming task, it is important to develop predictive methods. In this work, quantitative structure-activity relationships (QSARs) were determined using three methods, multiple linear regression (MLR), radical basis function neural network (RBFNN) and support vector machine (SVM). Five descriptors, accounting for hydrogen-bonding interaction, distribution of atomic charges and molecular branching degree, were selected from a heuristic method to build predictive QSAR models. Comparison of the results obtained from three models showed that the SVM method exhibited the best overall performances, with a RMS error of 0.54 log (RBA) units for the training set, 0.59 for the test set, and 0.55 for the whole set. Moreover, six linear QSAR models were constructed for some specific families based on their chemical structures. These predictive toxicology models, should be useful to rapidly identify potential androgenic endocrine disrupting compounds.

Algorithms↗

PubMed2004

QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines.

The support vector machine, which is a novel algorithm from the machine learning community, was used to develop quantitation and classification models which can be used as a potential screening mechanism for a novel series of COX-2 selective inhibitors. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum-chemical features. The heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modelling results in a nonlinear, seven-descriptor model based on SVMs with root mean-square errors of 0.107 and 0.136 for training and prediction sets, respectively. The best classification results are found using SVMs: the accuracy for training and test sets is 91.2% and 88.2%, respectively. This paper proposes a new and effective method for drug design and screening.

Algorithms↗

PubMed2004

3D QSAR studies on antimalarial alkoxylated and hydroxylated chalcones by CoMFA and CoMSIA.

The 3D QSAR analyses of antimalarial alkoxylated and hydroxylated chalcones were first conducted by Comparative molecular field analysis (CoMFA) and Comparative similarity indices analysis (CoMSIA) to determine the factors required for the activity of these compounds. Satisfactory results were obtained after performing a leave-one-out (LOO) cross-validation study with cross-validation q(2) and conventional r(2) values of 0.740 and 0.972 by the CoMFA model, 0.714 and 0.976 by the CoMSIA model, respectively. The results provided the tools for predicting the affinity of related compounds, and for guiding the design and synthesis of novel and more potent antimalarial agents.

Antimalarials↗

PubMed2004

CISOC-PSCT: a predictive system for carcinogenic toxicity.

A SAR based carcinogenic toxicity prediction system, CISOC-PSCT, was developed. It consisted of two principal phases: the construction of relationships between structural descriptors and carcinogenic toxicity indices, and prediction of the toxicity from the SAR model. The training set included 2738 carcinogenic and 4130 non-carcinogenic compounds. Three predefined topological types of substructures termed Star, Path and Ring were used to generate the descriptors for each structure in the training set. In this system, the defined carcinogenic toxicity index (CTI) was obtained from the probability of a structural descriptor to either belong to the carcinogenic or non-carcinogenic compounds. Based on these structural descriptors and their CTI, a SAR model was derived. Then the carcinogenic possibility (CP) and the carcinogenic impossibility (CIP) of compounds were predicted. The model was tested from a testing set of 304 carcinogenic compounds (MDL toxicity database), 460 non-carcinogenic compounds (CMC database) and 94 compounds extracted from two traditional Chinese medicine herbs.

Carcinogens↗

PubMed2003

Virtual screening and rational drug design method using structure generation system based on 3D-QSAR and docking.

An efficient virtual and rational drug design method is presented. It combines virtual bioactive compound generation with 3D-QSAR model and docking. Using this method, it is possible to generate a lot of highly diverse molecules and find virtual active lead compounds. The method was validated by the study of a set of anti-tumor drugs. With the constraints of pharmacophore obtained by DISCO implemented in SYBYL 6.8, 97 virtual bioactive compounds were generated, and their anti-tumor activities were predicted by CoMFA. Eight structures with high activity were selected and screened by the 3D-QSAR model. The most active generated structure was further investigated by modifying its structure in order to increase the activity. A comparative docking study with telomeric receptor was carried out, and the results showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data. This investigation showed that the proposed method was a feasible way for rational drug design with high screening efficiency.

Colchicine↗

PubMed2003

Scavenging effects of natural phenols on oxidizing intermediates of peroxynitrite.

Most of peroxynitrite (ONOO-/ONOOH) is formed via the diffusion-limited reaction between nitric oxide and superoxide. In biological systems, the decomposition of ONOO- yields 30-35% of carbonate radical (CO3*-) and nitrogen dioxide (NO2*), which are strongly oxidizing intermediates and are suggested to take a part of the responsibility for the toxicity of nitric oxide (NO*) or ONOO-. Therefore, the current study focuses on the scavenging activities of phenols toward CO3*- and NO2* to protect biomolecules from damage caused by NO* or ONOO- using the technique of pulse radiolysis. From the build-up kinetic of the phenoxyl radicals and the decay kinetic of CO3*- radical, the rate constants of scavenging reactions were determined to be 1.9-3.4 x 10(8) M(-1) x s(-1) and 0.11-1.9 x 10(8) M(-1) x s(-1) for CO3*- and NO2* respectively. The results indicated that the tested phenols are the efficient scavengers of CO3*- and NO2*.

Carbonates↗

PubMed2002

Development of a quantitative structure-property relationship model for predicting the electrophoretic mobilities.

Electrophoretic mobility (mu0) is the most important parameter governing the separation of solutes in capillary zone electrophoresis. In this paper, a new model was constructed by means of a multilayer neural network using extended delta-bar-delta (EDBD) algorithm to estimate complex property of electrophoretic mobilities of aliphatic carboxylates and amines from simpler experimental properties. The molecular weight (W), molecular volume (V), the code (+1 or -1) of acid and base and pK value were used as input parameters to predict electrophoretic mobility. The networks' architecture and the learning times were optimized. The optimum artificial neural networks (ANNs) could give excellent prediction results.

Journal Article↗

PubMed2002

Docking study of cistanoside C to telomeric DNA fragment.

Experiments show that the natural products phenyl propanoid glycosides (PPGs) extracted from the plant Pedicularis spicata are capable of repairing DNA damaged by oxygen radicals. Based on kinetic measurements and experiments on tumor cells, a theoretical study of the interaction between PPG molecule Cistanoside C and telomeric DNA fragment has been carried out. The docking calculations performed using JUMNA software showed that the Cistanoside C could be docked into the minor groove of telomeric DNA and form complexes with the geometry suitable for an electron transfer between guanine radical and the ligand. Such complexes can be formed without major distortions of DNA structure and are further stabilized by the interaction with the saccharide side-groups.

Catechols↗

PubMed2002

Theoretical study of fast repair of DNA damage by cistanoside C and analogs: mechanism and docking.

Experiments show that the natural substances phenylpropanoid glycosides (PPGs) extracted from pelicularis spicata are capable of repairing DNA damaged by oxygen radicals. Based on kinetic measurements and experiments on tumor cells, a theoretical study of the interaction between PPG molecules and isolated DNA bases, as well as a DNA fragment has been performed. An interaction mechanism reported early has been refined. The docking calculations performed using junction minimization of nucleic acids (JUMNA) software showed that the PPG molecules can be docked into the minor groove of DNA and form complexes with the geometry suitable for an electron transfer between guanine radical and the ligand. Such complexes can be formed without major distortions of DNA structure and are further stabilized by the interaction with the rhamnosyl side-groups.

DNA Damage↗