Structure‐activity relationship approaches and applications

Tong, Weida; Welsh, William J.; Shi, Leming; Fang, Hong; Perkins, Roger

doi:10.1897/01-198

Cited by 73 publications

(53 citation statements)

References 147 publications

(201 reference statements)

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“…These studies will define the structure-activity relationship (SAR) for the hit (40). These studies can be executed by purchase of similar existing compounds (SAR by catalog) and/or by the synthesis of small, carefully designed sets.…”

Section: The Drug Discovery Processmentioning

confidence: 99%

Drug discovery in academia

Verkman

2004

American Journal of Physiology-Cell Physiology

113

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Verkman, A. S. Drug discovery in academia. Am J Physiol Cell Physiol 286: C465-C474, 2004;10.1152/ajpcell.00397.2003.-Drug discovery and development is generally done in the commercial rather than the academic realm. Drug discovery involves target discovery and validation, lead identification by high-throughput screening, and lead optimization by medicinal chemistry. Follow-up preclinical evaluation includes analysis in animal models of compound efficacy and pharmacology (ADME: administration, distribution, metabolism, elimination) and studies of toxicology, specificity, and drug interactions. Notwithstanding the high-cost, labor-intensive, and non-hypothesis-driven aspects of drug discovery, the academic setting has a unique and expanding niche in this important area of investigation. For example, academic drug discovery can focus on targets of limited commercial value, such as third-world and rare diseases, and on the development of research reagents such as high-affinity inhibitors for pharmacological "gene knockout" in animal models ("chemical genetics"). This review describes the practical aspects of the preclinical drug discovery process for academic investigators. The discovery of small molecule inhibitors and activators of the cystic fibrosis transmembrane conductance regulator is presented as an example of an academic drug discovery program that has yielded new compounds for physiology research and clinical development.high-throughput screening; drug development; pharmacology; fluorescence; cystic fibrosis transmembrane conductance regulator DRUG DISCOVERY is the process whereby compounds with activity against a specified target or function are identified, evaluated, and optimized for clinical applications. As diagrammed in Fig.

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Section: The Drug Discovery Processmentioning

confidence: 99%

Drug discovery in academia

Verkman

2004

American Journal of Physiology-Cell Physiology

113

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“…Classification methods, because of their power to unravel patterns in biologically complex data, have become one of the most important bioinformatics approaches investigated for use with omics data. Classification uses supervised learning techniques (Tong et al 2003b) to fit the samples into the predefined categories based on patterns of omics profiles or predictor variables (e.g., gene expressions in DNA microarray). The fitted model is then validated using either a cross-validation method or an external test set.…”

Section: Toxicogenomics | Articlementioning

confidence: 99%

Using Decision Forest to Classify Prostate Cancer Samples on the Basis of SELDI-TOF MS Data: Assessing Chance Correlation and Prediction Confidence

Tong¹,

Xie²,

Hong³

et al. 2004

Environ Health Perspect

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Class prediction using "omics" data is playing an increasing role in toxicogenomics, diagnosis/prognosis, and risk assessment. These data are usually noisy and represented by relatively few samples and a very large number of predictor variables (e.g., genes of DNA microarray data or m/z peaks of mass spectrometry data). These characteristics manifest the importance of assessing potential random correlation and overfitting of noise for a classification model based on omics data. We present a novel classification method, decision forest (DF), for class prediction using omics data. DF combines the results of multiple heterogeneous but comparable decision tree (DT) models to produce a consensus prediction. The method is less prone to overfitting of noise and chance correlation. A DF model was developed to predict presence of prostate cancer using a proteomic data set generated from surface-enhanced laser deposition/ ionization time-of-flight mass spectrometry (SELDI-TOF MS). The degree of chance correlation and prediction confidence of the model was rigorously assessed by extensive cross-validation and randomization testing. Comparison of model prediction with imposed random correlation demonstrated biologic relevance of the model and the reduction of overfitting in DF. Furthermore, two confidence levels (high and low confidences) were assigned to each prediction, where most misclassifications were associated with the low-confidence region. For the high-confidence prediction, the model achieved 99.2% sensitivity and 98.2% specificity. The model also identified a list of significant peaks that could be useful for biomarker identification. DF should be equally applicable to other omics data such as gene expression data or metabolomic data. The DF algorithm is available upon request.

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“…Here, we performed SAR analysis of these compounds to further define the features of these molecules important for activity and to develop a simple, but accurate SAR model for predicting biological activity in future compound screening. The analysis of structure-activity relationships is an important approach for defining the critical combination of various structural and physicochemical descriptors responsible for the biological activity of a given molecule [e.g., 23,24 ]. In the present study, we utilized 2D atom pair descriptors together with physicochemical molecular descriptors (Route 1) or 2D parameters alone (Route 2) for SAR analysis of a series N-benzoylpyrazoles with various levels of experimentally determined elastase inhibitory activity.…”

Section: Discussionmentioning

confidence: 99%

Structure–activity relationship analysis of N-benzoylpyrazoles for elastase inhibitory activity: A simplified approach using atom pair descriptors

Khlebnikov

Schepetkin

Quinn

2008

Bioorganic & Medicinal Chemistry

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Previously, we utilized high throughput screening of a chemical diversity library to identify potent inhibitors of human neutrophil elastase and found that many of these compounds had Nbenzoylpyrazole core structures. We also found individual ring substituents had significant impact on elastase inhibitory activity and compound stability. In the present study, we utilized computational structure-activity relationship (SAR) analysis of a series of 53 N-benzoylpyrazole derivatives to further optimize these lead molecules. We present an improved approach to SAR methodology based on atom pair descriptors in combination with 2-dimentional (2D) molecular descriptors. This approach utilizes the rich representation of chemical structure and leads to SAR analysis that is both accurate and intuitively easy to understand. A sequence of ANOVA, linear discriminant, and binary classification tree analyses of the molecular descriptors led to the derivation of SAR rule-based algorithms. These rules revealed that the main factors influencing elastase inhibitory activity of Nbenzoylpyrazole molecules were the presence of methyl groups in the pyrazole moiety and orthosubstituents in the benzoyl radical. Furthermore, our data showed that physicochemical characteristics (energy of frontier molecular orbitals, molar refraction, lipophilicity) were not necessary for achieving good SAR, as comparable quality of SAR classification was obtained with atom pairs and 2D descriptors only. This simplified SAR approach may be useful to qualitative SAR recognition problems in a variety of data sets.

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Structure‐activity relationship approaches and applications

Cited by 73 publications

References 147 publications

Drug discovery in academia

Drug discovery in academia

Using Decision Forest to Classify Prostate Cancer Samples on the Basis of SELDI-TOF MS Data: Assessing Chance Correlation and Prediction Confidence

Structure–activity relationship analysis of N-benzoylpyrazoles for elastase inhibitory activity: A simplified approach using atom pair descriptors

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