Optimal neighbor selection in molecular similarity: comparison of arbitrary<i>versus</i>tailored prediction spaces†

Gute, Brian D.; Basak, Subhash C.

doi:10.1080/10659360600560933

Cited by 9 publications

(8 citation statements)

References 36 publications

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“…While a variety of molecular parameters can be used in the computational methods for (Q)SAR analysis [18,19], some of these parameters are complex physicochemical or geometrical 3D descriptors whose calculation is associated with difficulties conditioned by molecular flexibility and adequate sampling of conformational space. Conversely, topological indices, or 2D descriptors, obtainable from the structural formula of a compound are very attractive because of their simplicity.…”

Section: Resultsmentioning

confidence: 99%

Computational structure–activity relationship analysis of small-molecule agonists for human formyl peptide receptors

Khlebnikov

Schepetkin

Quinn

2010

European Journal of Medicinal Chemistry

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N-formyl peptide receptors (FPR) are important in host defense. Because of the potential for FPRs as therapeutic targets, recent efforts have focused on identification of non-peptide agonists for two FPR subtypes, FPR1 and FPR2. Given that a number of specific small molecule agonists have recently been identified, we hypothesized that computational structure-activity relationship (SAR) analysis of these molecules could provide new information regarding molecular features required for activity. We used a training set of 71 compounds, including 10 FPR1-specific agonists, 36 FPR2-specific agonists, and 25 non-active analogs. A sequence of (1) one-way analysis of variance selection, (2) cluster analysis, (3) linear discriminant analysis, and (4) classification tree analysis led to the derivation of SAR rules with high (95.8%) accuracy for correct classification of compounds. These SAR rules revealed key features distinguishing FPR1 versus FPR2 agonists. To verify predictive ability, we evaluated a test set of 17 additional FPR agonists, and found that the majority of these agonists (>94%) were classified correctly as agonists. This study represents the first successful application of classification tree methodology based on atom pairs to SAR analysis of FPR agonists. Importantly, these SAR rules represent a relatively simple classification approach for virtual screening of FPR1/FPR2 agonists.

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Section: Resultsmentioning

confidence: 99%

Computational structure–activity relationship analysis of small-molecule agonists for human formyl peptide receptors

Khlebnikov

Schepetkin

Quinn

2010

European Journal of Medicinal Chemistry

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“…It has previously been demonstrated that QMSA can be used to generate excellent estimates of boiling point, vapour pressure, log P (octanol/water), mutagenicity, and in vitro oestrogenicity [3][4][5]20]. Still, none of these is a fundamental environmental fate and transport parameter.…”

Section: Demonstrating Qmsa's Effectiveness For Estimation Of Environmentioning

confidence: 99%

“…The premise of QMSA is that 'similar' chemicals behave similarly, such that measured property values can be used as a starting point for estimation of the same property for unmeasured chemicals exhibiting structural similarity to previously measured chemicals. To date, this approach has been used to estimate a variety of environmentally relevant parameters, including mutagenicity [3], vapour pressure [4], and oestrogenicity [5].…”

Section: Introductionmentioning

confidence: 99%

Improving the usefulness of molecular similarity-based chemical prioritization strategies

Li¹,

Colosi

2013

SAR and QSAR in Environmental Research

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Quantitative molecular similarity analysis (QMSA) is a seemingly useful tool for estimating environmental properties for the hundreds of emerging contaminants that have not yet been fully evaluated. Moreover, calibrated QMSA models are also useful for prioritizing research among currently unmeasured chemicals of interest. Previous work has demonstrated that prioritization based on molecular 'representativeness', as parameterized using summed Euclidean distances in n dimensions corresponding to n molecular descriptors, improves the prediction accuracy of QMSA models compared to random selection of compounds to be measured. In this study, we use two datasets of environmental parameters (i.e. in vitro oestrogenicity and sorption distribution coefficient Kd ) to demonstrate that maximizing representativeness alone cannot deliver optimal improvement in prediction accuracy if many of the chemicals that have already been measured are themselves highly representative. Thus, proper QMSA-based prioritization among unmeasured chemicals constitutes a balance between maximizing representativeness and minimizing redundancy. It is demonstrated that redundancy considerations are especially critical for highly heterogeneous datasets, and some discussion about achieving a proper balance between the two prioritization criteria is presented.

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“…Our limited experience in this area shows that this method holds great promise for application to chemical domains relevant to drug design and the hazard assessment of chemicals. [90][91][92][93] However, much work remains to be done in validating this methodology. Also, techinques are needed to identify the domain of applicability of the similarity space.…”

Section: Discussionmentioning

confidence: 99%

“…[90][91][92][93] In this scheme we begin by selecting the elements of the structure space based on a specific property. The structure space thus created is then used for the selection of analogs and estimation of properties of chemicals from their analogs.…”

Section: Introductionmentioning

confidence: 99%

Similarity methods in analog selection, property estimation and clustering of diverse chemicals

Basak¹,

Gute²,

Mills³

2006

Arkivoc

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This account summarizes Dr. Subhash Basak's work in the field of molecular similarity. In particular, it looks at the development and application of quantitative molecular similarity analysis (QMSA) techniques using physicochemical properties, topological indices, and atom pairs as descriptors for developing structure-or property-based similarity spaces and the use of a k-nearest neighbors (kNN) technique to estimate the properties or activities of chemicals within the space. Additionally, the account discusses the novel tailored similarity technique pioneered by Dr. Basak's research group and discusses the future of molecular similarity analysis techniques.

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Optimal neighbor selection in molecular similarity: comparison of arbitraryversustailored prediction spaces†

Cited by 9 publications

References 36 publications

Computational structure–activity relationship analysis of small-molecule agonists for human formyl peptide receptors

Computational structure–activity relationship analysis of small-molecule agonists for human formyl peptide receptors

Improving the usefulness of molecular similarity-based chemical prioritization strategies

Similarity methods in analog selection, property estimation and clustering of diverse chemicals

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