Strategies for the Use of Mixture-Based Synthetic Combinatorial Libraries: Scaffold Ranking, Direct Testing In Vivo, and Enhanced Deconvolution by Computational Methods

Houghten, Richard A.; Pinilla, Clemencia; Giulianotti, Marc A.; Appel, Jon R.; Dooley, Colette T.; Nefzi, Adel; Ostresh, John M.; Yu, Yongping; Maggiora, Gerald M.; Medina‐Franco, José L.; Brunner, Daniela; Schneider, Jeff

doi:10.1021/cc7001205

Cited by 111 publications

(176 citation statements)

References 80 publications

Supporting

Mentioning

176

Contrasting

Order By: Relevance

“…The scaffold diversity was analyzed for seven compound collections: five natural products databases, a general screening commercial library obtained from Maybridge, and a data set of compounds obtained from combinatorial libraries (24)(25)(26). Table 1 summarizes the source of each database and the number of compounds in each collection (downloaded July 2011).…”

Section: Compound Databasesmentioning

confidence: 99%

Molecular Scaffold Analysis of Natural Products Databases in the Public Domain

2012

View full text Add to dashboard Cite

Natural products represent important sources of bioactive compounds in drug discovery efforts. In this work, we compiled five natural products databases available in the public domain and performed a comprehensive chemoinformatic analysis focused on the content and diversity of the scaffolds with an overview of the diversity based on molecular fingerprints. The natural products databases were compared with each other and with a set of molecules obtained from in-house combinatorial libraries, and with a general screening commercial library. It was found that publicly available natural products databases have different scaffold diversity. In contrast to the common concept that larger libraries have the largest scaffold diversity, the largest natural products collection analyzed in this work was not the most diverse. The general screening library showed, overall, the highest scaffold diversity. However, considering the most frequent scaffolds, the general reference library was the least diverse. In general, natural products databases in the public domain showed low molecule overlap. In addition to benzene and acyclic compounds, flavones, coumarins, and flavanones were identified as the most frequent molecular scaffolds across the different natural products collections. The results of this work have direct implications in the computational and experimental screening of natural product databases for drug discovery.Key words: chemoinformatics, cyclic system, molecular diversity, natural product, Shannon entropy, Traditional Chinese Medicine.Abbreviations: CSR, cumulative scaffold recovery; MACCS, Molecular ACCess System; MEQI, Molecular Equivalence Index; MOE, molecular operating environment; SE, Shannon entropy; TCM, Traditional Chinese Medicine.Received 22 June 2012, revised 18 July 2012 and accepted for publication 19 July 2012Traditionally, natural products have played a major role in drug discovery and development by providing novel chemical scaffolds, and serving as leads or drugs (1,2). For many years, 80% of drugs were either natural products or natural product-derived compounds. Even in the modern era, after the advent of techniques such as high-throughput screening of synthetic libraries, half of the drugs approved since 1994 are based on natural products research (3,4). Recent comprehensive reviews of natural products, or compounds inspired by natural products, indicate that more than 100 natural product compounds are currently in clinical trials. Natural products offer the advantage of discovering novel structural classes (5,6) because of their well-documented better coverage of chemical space relative to large synthetic compounds (7). Hence, the structural or chemical diversity of natural products can be utilized to access bioactive compounds with novel scaffolds (7-9). The inclusion of sources of natural products in drug discovery would open up more avenues for new classes of drugs (10) as well as new chemical entities, evidenced by studies that show structural or chemical complementarity between natural...

show abstract

Section: Compound Databasesmentioning

confidence: 99%

Molecular Scaffold Analysis of Natural Products Databases in the Public Domain

2012

View full text Add to dashboard Cite

show abstract

“…Sample 2229 in the scaffold-ranking library contains 345,600 polyamines in approximately equal molar amounts. These scaffold-ranking library samples can be prepared by mixing the cleaved products of the complete positional-scanning library, or they can be synthesized directly as a single mixture, as was the case with sample 2229 (31,(36)(37)(38).…”

Section: Methodsmentioning

confidence: 99%

“…The positional-scanning library contains 216 samples and incorporates both individual and mixtures of amino acids (R1 and R2) and carboxylic acid (R3). The synthetic technique facilitates the generation of information regarding the likely activity of individual compounds from the screening of the library (31,33,39). Equimolar isokinetic ratios have been previously determined and calculated for each of the amino and carboxylic acids utilized for the respective mixtures (40,41).…”

Section: Methodsmentioning

confidence: 99%

Small-Molecule Inhibitors Targeting Topoisomerase I as Novel Antituberculosis Agents

Sandhaus

Annamalai

Welmaker

et al. 2016

Antimicrob Agents Chemother

Self Cite

View full text Add to dashboard Cite

“…PS-SCL have been used to successfully identify active molecules for a variety of biological targets. (Houghten, Pinilla et al 1999;Pinilla, Appel et al 2003;Houghten, Pinilla et al 2008) In the case of opioid receptors highly active peptides (Dooley, Chung et al 1994;Houghten, Dooley et al 2006) and peptidomimetics have been identified. (Houghten, Dooley et al 2006) This technique has recently found new applications in the search of conotoxins (Armishaw, Singh et al) and in-vivo screening (Reilley, Giulianotti et al).…”

Section: Identification Of Opioid Receptor Ligandsmentioning

confidence: 99%

“…(Brooijmans and Kuntz 2003) Computational methods are largely applied to corporate chemical collections (Bajorath 2002) as well as combinatorial chemical libraries. (Houghten, Pinilla et al 2008) However, limited efforts have been reported so far to explicitly integrate information from mixture-based combinatorial libraries and computational techniques (López-Vallejo, Caulfield et al 2011;Yongye, Pinilla et al 2011). The structural analogy contained in combinatorial libraries in general and in mixture-based libraries in particular deserves particular considerations.…”

Section: Identification Of Opioid Receptor Ligandsmentioning

confidence: 99%