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D J Livingstone

Publications and source records attributed to D J Livingstone.

At least 19 recordsLinked to original sources
PubMed2002

Modelling mutagenicity using properties calculated by computational chemistry.

The recent advances in combinatorial chemistry and high throughput screening technologies have led to an explosion in the numbers of possible therapeutic candidates being produced at the early stages of drug discovery. This rapid increase in the number of chemicals to be classified results in a greater need for alternative methods for the prediction of toxicity. Most QSAR models for mutagenicity have been constructed for congeneric series. The prediction requirements of the pharmaceutical industry, however, cover quite diverse chemical structures. This paper reports a study of mutagenicity data for a diverse set of 90 compounds. Good discriminant models have been built for this data set using properties calculated by the techniques of computational chemistry. Jack-knifed (leave one out) predictions for these models are of the order of 85%.

Discriminant Analysis↗
PubMed2001

Quantitative measurements of edge-to-face aromatic interactions by using chemical double-mutant cycles.

Synthetic H-bonded zipper complexes have been used to quantify the magnitude of an edge-to-face aromatic interaction between a benzoyl group and an aniline ring. Four chemical double-mutant cycles were constructed by using a matrix of nine closely related complexes in which the aromatic rings were sequentially substituted for alkyl substituents. The stability constants and three-dimensional structures of the complexes were determined by using 1H NMR titrations in deuterochloroform at room temperature. The value of the interaction energy is similar in all cases, the average is -1.4 +/- 0.5 kJ mol(-1). The scope and limitations of the double-mutant approach are explored, and the consequences of conformational equilibria are discussed.

Journal Article↗
PubMed2001

Volume learning algorithm artificial neural networks for 3D QSAR studies.

The current study introduces a new method, the volume learning algorithm (VLA), for the investigation of three-dimensional quantitative structure-activity relationships (QSAR) of chemical compounds. This method incorporates the advantages of comparative molecular field analysis (CoMFA) and artificial neural network approaches. VLA is a combination of supervised and unsupervised neural networks applied to solve the same problem. The supervised algorithm is a feed-forward neural network trained with a back-propagation algorithm while the unsupervised network is a self-organizing map of Kohonen. The use of both of these algorithms makes it possible to cluster the input CoMFA field variables and to use only a small number of the most relevant parameters to correlate spatial properties of the molecules with their activity. The statistical coefficients calculated by the proposed algorithm for cannabimimetic aminoalkyl indoles were comparable to, or improved, in comparison to the original study using the partial least squares algorithm. The results of the algorithm can be visualized and easily interpreted. Thus, VLA is a new convenient tool for three-dimensional QSAR studies.

Algorithms↗
PubMed2001

Simultaneous prediction of aqueous solubility and octanol/water partition coefficient based on descriptors derived from molecular structure.

It has been shown that water solubility and octanol/water partition coefficient for a large diverse set of compounds can be predicted simultaneously using molecular descriptors derived solely from a two dimensional representation of molecular structure. These properties have been modelled using multiple linear regression, artificial neural networks and a statistical method known as canonical correlation analysis. The neural networks give slightly better models both in terms of fitting and prediction presumably due to the fact that they include non-linear terms. The statistical methods, on the other hand, provide information concerning the explanation of variance and allow easy interrogation of the models. Models were fitted using a training set of 552 compounds, a validation set and test set each containing 68 molecules and two separate literature test sets for solubility and partition.

1-Octanol↗
PubMed2000

Polynomial neural network for linear and non-linear model selection in quantitative-structure activity relationship studies on the internet.

This article presents a self-organising multilayered iterative algorithm that provides linear and non-linear polynomial regression models thus allowing the user to control the number and the power of the terms in the models. The accuracy of the algorithm is compared to the partial least squares (PLS) algorithm using fourteen data sets in quantitative-structure activity relationship studies. The calculated data show that the proposed method is able to select simple models characterized by a high prediction ability and thus provides a considerable interest in quantitative-structure activity relationship studies. The software is developed using client-server protocol (Java and C++ languages) and is available for world-wide users on the Web site of the authors.

Internet↗
PubMed1999

Pharmaceutical fingerprinting in phase space. 2. Pattern recognition.

The current study introduces an approach for pattern recognition of drug manufacturers according to their HPLC trace impurity data. This method considers signals in phase space and accounts for two different types of noise: additive and perturbative. The pharmaceutical fingerprints are estimated as mean trajectories of HPLC trace impurity data and are used as reference models for recognition of new data by the minimal length classifier. The chromatographic trace organic impurity patterns collected from six different manufacturers of L-tryptophan are analyzed as an example. The prediction ability of the new method tested using three different cross-validation procedures remains about 95% even if the number of available data in the training sets decreases by 5 times. The accuracy of prediction in phase space is superior compared to results calculated using a Window Preprocessing method and artificial neural networks. The difference in performance between new and previous methods becomes more significant under particular conditions that are more adequate for practical application of the method. In addition, the current approach enables simple and comprehensive interpretation of the calculated results.

Artificial Intelligence↗
PubMed1998

Evaluation of an extended period of use for preserved eye drops in hospital practice.

AIM: To evaluate and compare the microbial contamination arising from 1 and 2 weeks' use of eye drops by hospital inpatients and hence determine the validity of apportioning a 2 week in use expiry date for these preparations. METHODS: Eye drop residues were collected from inpatients of Worthing, Southlands, and Brighton General hospitals after 7 days' use (341 samples) and also after 14 days' use (295 samples). The contents of the containers were examined for the presence of contaminating bacteria and fungi. RESULTS: The incidence of microbial contamination was shown to be not significantly different (p > 0.1 chi 2 test) between the 7 and 14 day samples. In addition, the contaminating micro-organisms were of a broadly similar pattern between the two sample groups and were mostly those normally associated with the skin. Less frequent contaminants were organisms of environmental origin. None of the micro-organisms isolated were considered to be of clinical significance and the mean number of cells found per sample was very low. CONCLUSIONS: The evidence therefore suggests that increasing the period of use for eye drops in hospitals from 7 to 14 days would not present a clinically significant threat to patients' health and yet may lead to annual savings to the NHS of Pounds 0.5 million.

Bacteria↗
PubMed1997

Data modelling with neural networks: advantages and limitations.

The origins and operation of artificial neural networks are briefly described and their early application to data modelling in drug design is reviewed. Four problems in the use of neural networks in data modelling are discussed, namely overfitting, chance effects, overtraining and interpretation, and examples are given of the means by which the first three of these may be avoided. The use of neural networks as a variable selection tool is shown and the advantage of networks as a nonlinear data modelling device is discussed. The display of multivariate data in two dimensions employing a neural network is illustrated using experimental and theoretical data for a set of charge transfer complexes.

Data Interpretation, Statistical↗
PubMed1994

Analysis of linear and nonlinear QSAR data using neural networks.

The use of feed forward back propagation neural networks to perform the equivalent of multiple linear regression has been examined using artificial structured data sets and real literature data. Their predictive ability has been assessed using leave-one-out cross-validation and training/test set protocols. While networks have been shown to fit data sets well, they appear to suffer from a number of disadvantages. In particular, they have performed poorly in prediction for the QSAR data examined here, they are susceptible to chance effects, and the relationships developed by the networks are difficult to interpret. This investigation reports results for one particular form of artificial neural network; other architectures and applications, however, may be more suitable.

Neural Networks, Computer↗
PubMed1993

Chiral chromatography and multivariate quantitative structure-property relationships of benzimidazole sulphoxides.

Various benzimidazole sulphoxides were chirally resolved employing an amylase-based chiral stationary phase. The structure-property relationships of these compounds were investigated using calculated physicochemical properties, molecular modelling and multivariate statistical techniques. A data set of 254 molecular descriptors was used to represent the series of compounds. Analysis of the data set using principal components analysis and non-linear mapping suggested that the separation factor of each enantiomeric pair was associated with nine molecular properties and, in particular, molar refractivity of the Z substituent and the partial charge of atom 6. The separation factor of a sulphoxide not used in the analysis was well predicted thus suggesting that these models may be used to generalize.

Anti-Ulcer Agents↗
PubMed1993

Analysis of the biological and molecular properties of phencyclidine-like compounds by chemometrics.

The quantitative structure-activity relationships (QSAR's) of 3 series of arylcyclohexylamines have been investigated using computational chemistry and multivariate statistics. Principal component analysis of the aromatic ring data set demonstrated some clustering of activity categories. Biological activity of the cyclohexane ring data set was correlated with molar refractivity. These findings may be useful for predicting the activity of novel neuroprotective agents.

Cyclohexanes↗
PubMed1992

Multivariate quantitative structure-toxicity relationships in a series of dopamine mimetics.

The techniques of principal components analysis and non-linear mapping are routinely used by computer chemists at SmithKline Beecham Pharmaceuticals in the process of drug development by relating the structure of a compound to its chemical activity. To our knowledge these techniques had not previously been applied to the association between the structure of a compound and its toxicological properties. Using a series of 12 structurally related compounds (11 were active dopamine mimetics and one was inactive), of which five were known to be teratogenic and seven were non-teratogenic, it was possible to demonstrate that molecular modelling techniques could be applied to differentiate toxicological data. The structure/property relationships of these compounds were investigated using calculated physicochemical properties, molecular modelling and multivariate statistical techniques. A data set of 56 molecular descriptors was used to represent this series of compounds. Analysis of the data set using principal components analysis and non-linear mapping suggested that teratogenicity was associated with four molecular properties. Moreover, the electronic nature of the 4-phenyl group appeared to be an important determinant of the teratogenesis.

Abnormalities, Drug-Induced↗
PubMed1992

Structure-activity relationships of pyrethroid insecticides. Part 2. The use of molecular dynamics for conformation searching and average parameter calculation.

Molecular dynamics simulations have been performed on a number of conformationally flexible pyrethroid insecticides. The results indicate that molecular dynamics is a suitable tool for conformational searching of small molecules given suitable simulation parameters. The structures derived from the simulations are compared with the static conformation used in a previous study. Various physicochemical parameters have been calculated for a set of conformations selected from the simulations using multivariate analysis. The averaged values of the parameters over the selected set (and the factors derived from them) are compared with the single conformation values used in the previous study.

Computer Simulation↗
PubMed1991

A quantitative structure-activity relationship approach to the minimization of albumin binding.

The binding of 2,6-disubstituted xanthones to human serum albumin (HSA) has been investigated using an ultrafiltration technique. A set of 26 compounds was chosen for study using a selection procedure aimed at minimizing the interparameter correlations, while ensuring that the physicochemical properties covered the maximum possible range of values. The magnitude of binding has been expressed as the compound concentration required to produce a specified bound concentration, in preference to equilibrium constants and number of albumin binding sites. Albumin binding was found to have a nonlinear dependence on the octanol-water partition coefficient (log P) and has been rationalized in terms of a simple binding model.

Binding Sites↗
PubMed1991

Pattern recognition methods in rational drug design.

Pattern recognition methods have much to offer the drug designer, particularly as the calculation and collation of data, both biological and physicochemical, becomes easier with the widespread use of computer databases, molecular modeling systems, and property prediction packages. Some of the techniques, however, suffer from difficulties in interpretation and the dangers of chance effects have received little attention. The wider use and understanding of these methods is expected to enhance their utility in drug design. Finally, it should be mentioned here that these methods are becoming applied increasingly in other areas of pharmaceutical research, e.g., the analysis of clinical data, and that new techniques for analysis continue to be developed and applied in this field.

Drug Design↗
PubMed1991

Novel method for the display of multivariate data using neural networks.

A neural network has been used to reduce the dimensionality of multivariate data sets to produce two-dimensional (2D) displays of these sets. The data consisted of physicochemical properties for sets of biologically active molecules calculated by computational chemistry methods. Previous work has demonstrated that these data contain sufficient relevant information to classify the compounds according to their biological activity. The plots produced by the neural network are compared with results from two other techniques for linear and nonlinear dimension reduction, and are shown to give comparable and, in one case, superior results. Advantages of this technique are discussed.

Antimycin A↗
PubMed1990

Structure-activity relationships of antifilarial antimycin analogues: a multivariate pattern recognition study.

The structure-activity relationships of a series of novel antifilarial antimycin A1 analogues have been investigated by using computational chemistry and multivariate statistical techniques. The physiochemical descriptors calculated in this way contained information which was useful in the classification of compounds according to their in vitro antifilarial activity. This approach generated a 53 parameter descriptor set, which was reduced with a multivariate pattern recognition package, ARTHUR. Regression analysis of the reduced set yielded several statistically significant regression equations; e.g.-log in vitro activity = 0.017 mp + 0.65 log P - 0.81ESDL10-7.33 (R = 0.9). With use of this equation, it was possible to make predictions for further untested analogues. The analysis indicated that membrane or lipid solubility is an important determinant in biological activity agreeing with the proposed primary mode of action of the compounds as disrupters of cuticular glucose uptake.

Animals↗