Development of a Virtual Screening Method for Identification of “Frequent Hitters” in Compound Libraries

Roche, Olivier; Schneider, Petra; Zuegge, Jochen; Guba, Wolfgang; Kansy, Manfred; Alanine, Alexander; Bleicher, Konrad H.; Danel, Franck; Gutknecht, Eva-Maria; Rogers‐Evans, Mark; Neidhart, Werner; Stalder, Henri; Dillon, Michael E.; Sjögren, Eric; Fotouhi, Nader; Gillespie, Paul; Goodnow, Robert A.; Harris, William R.; Jones, Phillip W.; Taniguchi, Mikio; Tsujii, Shinji; Saal, Wolfgang von der; Zimmermann, Gerd W.

doi:10.1021/jm010934d

Cited by 259 publications

(298 citation statements)

References 22 publications

(38 reference statements)

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“…On the Frequent Hitters classification problem, we were able to improve previous results 4 by reducing the misclassification rate from 10% to 4%-5% (methods SP/EP/RRS/RRE).…”

Section: Discussionmentioning

confidence: 80%

Ensemble Methods for Classification in Cheminformatics

Merkwirth

Mauser

Schulz‐Gasch

et al. 2004

J. Chem. Inf. Comput. Sci.

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We describe the application of ensemble methods to binary classification problems on two pharmaceutical compound data sets. Several variants of single and ensembles models of k-nearest neighbors classifiers, support vector machines (SVMs), and single ridge regression models are compared. All methods exhibit robust classification even when more features are given than observations. On two data sets dealing with specific properties of drug-like substances (cytochrome P450 inhibition and "Frequent Hitters", i.e., unspecific protein inhibition), we achieve classification rates above 90%. We are able to reduce the cross-validated misclassification rate for the Frequent Hitters problem by a factor of 2 compared to previous results obtained for the same data set with different modeling techniques.

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“…On the Frequent Hitters classification problem, we were able to improve previous results 4 by reducing the misclassification rate from 10% to 4%-5% (methods SP/EP/RRS/RRE).…”

Section: Discussionmentioning

confidence: 80%

Ensemble Methods for Classification in Cheminformatics

Merkwirth

Mauser

Schulz‐Gasch

et al. 2004

J. Chem. Inf. Comput. Sci.

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Section: What Sorts Of Compounds Aggregate?mentioning

confidence: 99%

“…These dye-like compounds would probably have been detected by the computational filters implemented in pharmaceutical companies to flag nuisance compounds. For example, there is a large overlap between such molecules and those flagged by programs such as REOS [3] and the 'frequent hitters' virtual screening program used at Roche [11].We wanted to learn if more drug-like, arguably more pharmaceutically relevant, molecules might also form aggregates.Initially, thirty compounds from an in-house Pharmacia screening deck were tested [8]. Twenty of these inhibited a panel of unrelated enzymes (β-lactamase, DHFR and chymotrypsin), noncompetitively in a time-dependent manner at micromolar or tens-of-micromolar concentrations.…”

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confidence: 99%

Screening in a spirit haunted world

Shoichet

2006

Drug Discovery Today

262

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High-throughput screening (HTS) campaigns can be dominated by hits that ultimately turn out to be non-drug-like. These 'nuisance' compounds often behave strangely, with steep dose-response curves, absence of clear structure-activity relationships, and high sensitivity to assay conditions. Several mechanisms contribute to these artifacts, including chemically reactive molecules, those that absorb light in assays and those that affect redox conditions. One of the most common mechanisms behind artifactual inhibition is discussed in this review: at micromolar concentrations organic molecules can aggregate to form particles in aqueous buffers, and these aggregates can sequester and thereby inhibit protein targets. Aggregation-based inhibition is baffling from a chemical perspective, but viewed biophysically such behavior is expected. The range of molecules that behave this way, their rapid detection in a screening environment and their possible biological implications will be considered here.If high-throughput screening (HTS) has changed drug discovery, it has also introduced into it a bestiary of peculiar molecules. Some of these have turned out to be interesting and important; others have proven to be 'nuisance' compounds with strange properties. Steep dose-response curves, flat structure-activity relationships and high sensitivity to assay conditions are unusual with classic, well-behaved drugs and reagents, but are common among nuisance hits. These are rarely suited for development, but much time and passion can be wasted chasing them before they are abandoned. Their prevalence has contributed to the evolution of screening practices towards high-quality compound libraries, the maintenance of dry stocks of pure compounds and ever-lower concentrations of compound in initial screens.'Nonsense is always nonsense, but the study of nonsense can be scholarship', said Saul Lieberman of the Kabbalist Gershom Scholem. Much scholarly ink has been spilled on compounds in screening decks that are prone to artifactual inhibition. Lipinski's now famous rules [1] focused on the physical properties of drugs, reacting to an early tendency in HTS libraries toward large and hydrophobic molecules that were unlikely to be orally bioavailable. Subsequent studies, typically using retrospective analysis of hit lists, have focused on chemical reactivity [2], assay interference [3], high flexibility [4], oxidation potential [5], formal molecular charge [6], or liability to degradation and precipitation [7]. Indeed, these characteristics have been incorporated at most pharmaceutical companies using computational filters that flag likely nuisance compounds in screening collections, so that they can be scrutinized when reviewing screening hit lists. Whereas these filters have been implemented since the late 1990s, identifying pathological hits unambiguously using these criteria has proven difficult. As one class of nuisance inhibitor is identified, another emerges Hydralike. This is partly a problem of the apparent specificity of nuis...

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“…3 Other inhibitors contain "privileged" substructures that can bind several different members of a protein family. 10 Still other molecules are known to interfere with colorimetric or fluorimetric detection methods used in screening assays, thereby creating the impression of inhibition via experimental artifact. 10 Even after considering these mechanisms, there remained a population of nonspecific enzyme inhibitors that defied explanation.…”

Section: Introductionmentioning

confidence: 99%

A Specific Mechanism of Nonspecific Inhibition

Helfand²,

et al. 2003

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Promiscuous small molecules plague screening libraries and hit lists. Previous work has found that several nonspecific compounds form submicrometer aggregates, and it has been suggested that this aggregate species is responsible for the inhibition of many different enzymes. It is not understood how aggregates inhibit their targets. To address this question, biophysical, kinetic, and microscopy methods were used to study the interaction of promiscuous, aggregateforming inhibitors with model proteins. By use of centrifugation and gel electrophoresis, aggregates and protein were found to directly interact. This is consistent with a subsequent observation from confocal fluorescence microscopy that aggregates concentrate green fluorescent protein.-Lactamase mutants with increased or decreased thermodynamic stability relative to wild-type enzyme were equally inhibited by an aggregate-forming compound, suggesting that denaturation by unfolding was not the primary mechanism of interaction. Instead, visualization by electron microscopy revealed that enzyme associates with the surface of inhibitor aggregates. This association could be reversed or prevented by the addition of Triton X-100. These observations suggest that the aggregates formed by promiscuous compounds reversibly sequester enzyme, resulting in apparent inhibition. They also suggest a simple method to identify or reverse the action of aggregate-based inhibitors, which appear to be widespread.

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Development of a Virtual Screening Method for Identification of “Frequent Hitters” in Compound Libraries

Cited by 259 publications

References 22 publications

Ensemble Methods for Classification in Cheminformatics

Ensemble Methods for Classification in Cheminformatics

Screening in a spirit haunted world

A Specific Mechanism of Nonspecific Inhibition

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