Evolutionary algorithms in computer-aided molecular design

Clark, David E.; Westhead, David R.

doi:10.1007/bf00124503

Cited by 145 publications

(76 citation statements)

References 197 publications

(203 reference statements)

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“…We selected an optimal, biologically relevant subset of descriptors for the tree-based model using GFA. GFA is a genetic algorithm-based statistical approach (31,32), which has been widely used for QSAR model development. Basically, GFA starts with a randomly selected set of descriptors from the original descriptor pool to generate a population of QSAR equations (100 equations in this study) using multivariate regression techniques such as the least-square regression method used in this study.…”

Section: Methodsmentioning

confidence: 99%

“…Basically, GFA starts with a randomly selected set of descriptors from the original descriptor pool to generate a population of QSAR equations (100 equations in this study) using multivariate regression techniques such as the least-square regression method used in this study. Then the quality of each individual equation is estimated using a lack-of-fit (LOF) score function (31,32). These equations can be rank-ordered based on the model's quality.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts.

Hong¹,

Tong²,

Fang³

et al. 2002

Environ Health Perspect

140

116

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ArticlesA number of environmental chemicals, by mimicking natural hormones, can disrupt endocrine function in experimental animals, wildlife, and humans. These chemicals, called "endocrine-disrupting chemicals" (EDCs), are such a scientific and public concern that screening and testing 58,000 chemicals for EDC activities is now statutorily mandated. Computational chemistry tools are important to biologists because they identify chemicals most important for in vitro and in vivo studies. Here we used a computational approach with integration of two rejection filters, a tree-based model, and three structural alerts to predict and prioritize estrogen receptor (ER) ligands. The models were developed using data for 232 structurally diverse chemicals (training set) with a 10 6 range of relative binding affinities (RBAs); we then validated the models by predicting ER RBAs for 463 chemicals that had ER activity data (testing set). The integrated model gave a lower false negative rate than any single component for both training and testing sets. When the integrated model was applied to approximately 58,000 potential EDCs, 80% (~46,000 chemicals) were predicted to have negligible potential (log RBA < -4.5, with log RBA = 2.0 for estradiol) to bind ER. The ability to process large numbers of chemicals to predict inactivity for ER binding and to categorically prioritize the remainder provides one biologic measure to prioritize chemicals for entry into more expensive assays (most chemicals have no biologic data of any kind).

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts.

Hong¹,

Tong²,

Fang³

et al. 2002

Environ Health Perspect

140

116

View full text Add to dashboard Cite

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“…Genetic algorithms have been applied to a wide range of scientific problems and provide a typically rapid method of discovering optimal or near-optimal solutions to problems that are otherwise insoluble in a realistic time frame (for a review of the application of GAs in the chemoinformatics domain, the reader is referred to the work by Clark 24 ). The GA developed for the optimization strategy adopted in this study considers the selection of p unique plates from a larger pool of P plates such that the subset is optimized in terms of maximal coverage of the entire SpeedScreen library.…”

Section: Molecular Scaffold Frameworkmentioning

confidence: 99%

A Chemoinformatics Analysis of Hit Lists Obtained from High-Throughput Affinity-Selection Screening

Brown¹,

Zehender²,

Azzaoui³

et al. 2006

SLAS Discovery

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The high-throughput affinity-selection screening platform SpeedScreen was recently reported by the Novartis Institutes for BioMedical Research as a homogeneous, label-free screening technology with mass-spectrometry readout. SpeedScreen relies on the screening of compound mixtures with various target proteins and uses fast size-exclusion chromatography to separate target-bound from unbound substances. After disintegration of the target-binder complex, the binder molecules are identified by their molecular masses using liquid chromatography/mass spectrometry. The authors report an analysis of the molecular properties of hits obtained with SpeedScreen on 26 targets screened within the past few years at Novartis using this technology. Affinity-based SpeedScreen is a robust high-throughput screening technology that does not accumulate frequent hitters or potential covalent binders. The hits are representative of the most commonly identified scaffold classes observed for known drugs. Validated SpeedScreen hits tend to be enriched on more lipophilic and larger-molecular-weight compounds compared to the whole library. The potential for a reduced SpeedScreen screening set to be used in case only limited protein quantities are available is evaluated. Such a reduced compound set should also maximize the coverage of the high-performing regions of the chemical property and class spaces; chemoinformatics methods including genetic algorithms and divisive Kmeans clustering are used for this aim. (Journal of Biomolecular Screening 2006:123-130)

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“…Genetic algorithms [10,11] provide an obvious and convenient means to approach the stated problem. GAs are now a well-established stochastic technique for performing directed random searches of a problem space and have been widely applied to drug design and chemometric problems [12]. A wide variety of alternative formulations are available the selection of which are to some extent arbitrary; details of the chosen methods are given below while Figure 3 B. Chromosome fitness evaluation.…”

Section: Searching For An Optimal Solution (V Opt ) Eva_gamentioning

confidence: 99%

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Turner

Willett

2000

Journal of Computer-Aided Molecular Design

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SummaryThe EVA structural descriptor, based upon calculated fundamental molecular vibrational frequencies, has proved to be an effective descriptor for both QSAR and database similarity calculations. The descriptor is sensitive to 3D structure but has an advantage over fieldbased 3D-QSAR methods inasmuch as structural superposition is not required. The original technique involves a standardisation method wherein uniform Gaussians of fixed standard deviation (σ) are used to smear out frequencies projected onto a linear scale. This smearing function permits the overlap of proximal frequencies and thence the extraction of a fixed dimensional descriptor regardless of the number and precise values of the frequencies. It is proposed here that there exist optimal localised values of σ in different spectral regions; that is, the overlap of frequencies using uniform Gaussians may, at certain points in the spectrum, either be insufficient to pick up relationships where they exist or mix up information to such an extent that significant correlations are obscured by noise. A genetic algorithm is used to search for optimal localised σ values using crossvalidated PLS regression scores as the fitness score to be optimised. The resultant models are then validated against a previously unseen test set of compounds. The performance of EVA_GA is compared to that of EVA and analogous CoMFA studies.

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Evolutionary algorithms in computer-aided molecular design

Cited by 145 publications

References 197 publications

Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts.

Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts.

A Chemoinformatics Analysis of Hit Lists Obtained from High-Throughput Affinity-Selection Screening

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