Drug-motif-based diverse monomer selection: Method and application in combinatorial chemistry

Lewell, Xiao Qing; Smith, Ryan

doi:10.1016/s1093-3263(97)00009-0

Cited by 14 publications

(9 citation statements)

References 2 publications

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“…It was observed that a limited number of fragments could be found in drugs, and the top 50 drug scaffolds cover about half of the drugs in the two datasets. Most previous studies only analyzed the frequencies of the scaffolds found in drugs or drug-like molecules by applying different dissecting methods [30,31,63,64]. In this study, we grouped the building blocks into different categories based on the ratio of its occurrence frequency in the drug datasets to that in the screening dataset (ACD).…”

Section: Relationships Between Drug-likeness and Molecular Fragmentsmentioning

confidence: 99%

The application of in silico drug-likeness predictions in pharmaceutical research

Tian

Wang

et al. 2015

Advanced Drug Delivery Reviews

360

205

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Section: Relationships Between Drug-likeness and Molecular Fragmentsmentioning

confidence: 99%

The application of in silico drug-likeness predictions in pharmaceutical research

Tian

Wang

et al. 2015

Advanced Drug Delivery Reviews

360

205

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“…Methods to analyze drug-like reagents have been developed by Lewell et al 8,38 Potential monomers for drug-like molecules have been analyzed and clustered calculating the similarity to 30,000 compounds of the Derwent Standard Drug File. 38 Several Daylight databases for specific reagents such as aldehydes, carboxylic acids, primary and secondary amines have been generated. However, with this approach reagents that are not in the databases already cannot be suggested for combinatorial synthesis.…”

Section: B Structural Framework and Side Chains Of Known Drugsmentioning

confidence: 99%

Selection criteria for drug‐like compounds

Muegge

2003

Medicinal Research Reviews

333

140

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The fast identification of quality lead compounds in the pharmaceutical industry through a combination of high throughput synthesis and screening has become more challenging in recent years. Although the number of available compounds for high throughput screening (HTS) has dramatically increased, large-scale random combinatorial libraries have contributed proportionally less to identify novel leads for drug discovery projects. Therefore, the concept of 'drug-likeness' of compound selections has become a focus in recent years. In parallel, the low success rate of converting lead compounds into drugs often due to unfavorable pharmacokinetic parameters has sparked a renewed interest in understanding more clearly what makes a compound drug-like. Various approaches have been devised to address the drug-likeness of molecules employing retrospective analyses of known drug collections as well as attempting to capture 'chemical wisdom' in algorithms. For example, simple property counting schemes, machine learning methods, regression models, and clustering methods have been employed to distinguish between drugs and non-drugs. Here we review computational techniques to address the drug-likeness of compound selections and offer an outlook for the further development of the field.

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“…10 Diversity has generally been considered to be important for lead discovery, and much method development has focused on the use of commercial and corporate databases to development diversity/similarity assessment and sampling methods. [11][12][13][14][15] A key consideration in combinatorial library design is that small numbers of synthons yield large numbers of products; thus, it becomes essential to find a means to optimally choose these small building blocks from the large superset of available reagents to achieve the desired level of diversity. While it is intuitively reasonable and can be demonstrated 16 that the analysis of product diversity is more desirable than of synthon diversity, reagent/synthon selection is generally more feasible in terms of scale.…”

Section: Introductionmentioning

confidence: 99%

Design and Diversity Analysis of Large Combinatorial Libraries Using Cell-Based Methods

Schnur¹

1999

J. Chem. Inf. Comput. Sci.

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Generation of large libraries of small molecules has required a reexamination of the methods chemists use to select their starting materials for synthesis. When a few reagents can be used to generate vast numbers of compounds, selection of an appropriately diverse set of starting materials is important. Since syntheses involve reactions between specific functional groups, it is reasonable to sort reagents by these groups. Then the diversity of the fragments attached to the given reactive functionality may be examined. This diversity may be defined in terms of the biologically relevant properties of a three-dimensional structure. Cell-based methodology can be used to divide reagents into convenient subsets from which representative diverse reagents can be selected for library synthesis. For ECLiPS libraries, analyses of the reagents used in each individual step have proven to be a useful strategy. Further cell-based analyses of the actual libraries in conjunction with biological activity data have shown clustering of actives in the selected diversity spaces.

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Drug-motif-based diverse monomer selection: Method and application in combinatorial chemistry

Cited by 14 publications

References 2 publications

The application of in silico drug-likeness predictions in pharmaceutical research

The application of in silico drug-likeness predictions in pharmaceutical research

Selection criteria for drug‐like compounds

Design and Diversity Analysis of Large Combinatorial Libraries Using Cell-Based Methods

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