Towards Unified Compound Screening Strategies: A Critical Evaluation of Error Sources in Experimental and Virtual High‐Throughput Screening

Parker, Christian N.; Bajorath, Jürgen

doi:10.1002/qsar.200610069

Cited by 22 publications

(11 citation statements)

References 60 publications

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“…Studies have demonstrated that models built on a structurally similar set of compounds occupying closely adjacent areas of chemical space are likely to have limited applicability in terms of identifying novel inhibitor classes, and thus may result in unreliable predictions when used in virtual screening of structurally diverse chemical databases. [34, 35]…”

Section: Introductionmentioning

confidence: 99%

Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors

Zhang¹,

Amin²

2016

Journal of Molecular Graphics and Modelling

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Anthrax is a highly lethal, acute infectious disease caused by the rod-shaped, Gram-positive bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF), a zinc metalloprotease secreted by the bacilli, plays a key role in anthrax pathogenesis and is chiefly responsible for anthrax-related toxemia and host death, partly via inactivation of mitogen-activated protein kinase kinase (MAPKK) enzymes and consequent disruption of key cellular signaling pathways. Antibiotics such as fluoroquinolones are capable of clearing the bacilli but have no effect on LF-mediated toxemia; LF itself therefore remains the preferred target for toxin inactivation. However, currently no LF inhibitor is available on the market as a therapeutic, partly due to the insufficiency of existing LF inhibitor scaffolds in terms of efficacy, selectivity, and toxicity. In the current work, we present novel support vector machine (SVM) models with high prediction accuracy that are designed to rapidly identify potential novel, structurally diverse LF inhibitor chemical matter from compound libraries. These SVM models were trained and validated using 508 compounds with published LF biological activity data and 847 inactive compounds deposited in the Pub Chem BioAssay database. One model, M1, demonstrated particularly favorable selectivity toward highly active compounds by correctly predicting 39 (95.12%) out of 41 nanomolar-level LF inhibitors, 46 (93.88%) out of 49 inactives, and 844 (99.65%) out of 847 Pub Chem inactives in external, unbiased test sets. These models are expected to facilitate the prediction of LF inhibitory activity for existing molecules, as well as identification of novel potential LF inhibitors from large datasets.

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Section: Introductionmentioning

confidence: 99%

Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors

Zhang¹,

Amin²

2016

Journal of Molecular Graphics and Modelling

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“…Although each has its own distinct advantages, these methods share a common goal in that they both aim to identify a small number of true biological positives amidst a vast amount of biological negatives. [4, 5] …”

Section: Introductionmentioning

confidence: 99%

Investigating combinatorial approaches in virtual screening on human inducible 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFKFB3): A case study for small molecule kinases

Crochet

Cavalier

Seo

et al. 2011

Analytical Biochemistry

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Fruitful efforts toward improving the predictiveness in tier-based approaches to virtual screening (VS) have mainly focused on protein kinases. Despite their significance as drug targets, small molecule kinases have been rarely tested with these approaches. In this paper, we investigate the efficacy of a pharmacophore screening-combined structure-based docking approach on the human inducible 6-Phosphofructo-2-kinase/Fructose-2,6-bisphosphatase, an emerging target for cancer chemotherapy. Six out of a total 1,364 compounds from NCI's Diversity Set II were selected as true actives via throughput screening. Using a database constructed from these compounds, five programs were tested for structure-based docking (SBD) performance, of which, MOE showed the highest enrichments and second highest screening rates. Separately, using the same database, pharmacophore screening was performed, reducing 1,364 compounds to 287 with no loss in true actives, yielding an enrichment of 4.75. When SBD was retested with the pharmacophore filtered database, 4 of the 5 SBD programs showed significant improvements to enrichment rates at only 2.5% of the database, with a 7-fold decrease in an average VS time. Our results altogether suggest that combinatorial approaches of VS technologies are easily applicable to small molecule kinases and, moreover, that such methods can decrease the variability associated with single-method SBD approaches.

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“…[1,2] Some of these efforts have been facilitated by the use of such virtual screening (VS) tools as ligand-based QSAR, [9][10][11][12][13] 3D-QSAR, [14][15][16][17][18][19] and pharmacophore, [20] and structure-based molecular docking. [21][22][23][24][25][26] The applicability domains of these ligand-based methods in some cases are restricted [27,28] by limited diversity (< 200 compounds in most cases) [29][30][31] or structural types (e.g. hydroxamic acid derivatives only) in training dataset.…”

Section: Introductionmentioning

confidence: 99%

Identifying Novel Type ZBGs and Nonhydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach

Liu

Song

Zhang

et al. 2010

Molecular Informatics

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Histone deacetylase inhibitors (HDACi) have been successfully used for the treatment of cancers and other diseases. Search for novel type ZBGs and development of non-hydroxamate HDACi has become a focus in current research. To complement this, it is desirable to explore a virtual screening (VS) tool capable of identifying different types of potential inhibitors from large compound libraries with high yields and low false-hit rates similar to HTS. This work explored the use of support vector machines (SVM) combined with our newly developed putative non-inhibitor generation method as such a tool. SVM trained by 702 pre-2008 hydroxamate HDACi and 64334 putative non-HDACi showed good yields and low false-hit rates in cross-validation test and independent test using 220 diverse types of HDACi reported since 2008. The SVM hit rates in scanning 13.56 M PubChem and 168K MDDR compounds are comparable to HTS rates. Further structural analysis of SVM virtual hits suggests its potential for identification of non-hydroxamate HDACi. From this analysis, a series of novel ZBG and cap groups were proposed for HDACi design.

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Towards Unified Compound Screening Strategies: A Critical Evaluation of Error Sources in Experimental and Virtual High‐Throughput Screening

Cited by 22 publications

References 60 publications

Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors

Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors

Investigating combinatorial approaches in virtual screening on human inducible 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFKFB3): A case study for small molecule kinases

Identifying Novel Type ZBGs and Nonhydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach

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