Data Shaving:  A Focused Screening Approach

Abstract

The number of compounds available for evaluation as part of the drug discovery process continues to increase. These compounds may exist physically or be stored electronically allowing screening by either actual or virtual means. This growing number of compounds has generated an increasing need for effective strategies to direct screening efforts. Initial efforts toward this goal led to the development of methods to select diverse sets of compounds for screening, methods to cluster actives into related groups of compounds, and tools to select compounds similar to actives of interest for further screening. In this work we extend these earlier efforts to exploit information about inactive compounds to help make rational decisions about which sets of compounds to include as part of a continuing screening campaign, or as part of a focused follow-up effort. This method uses the information from inactive compounds to "shave" off or deprioritize compounds similar to inactives from further consideration. This methodology can be used in two ways: first, to provide a rational means of deciding when sufficient compounds containing certain structural features have been tested and second as a tool to enhance similarity searching around known actives. Similarity searching is improved by deprioritizing compounds predicted to be inactive, due to the presence of structural features associated with inactivity.

Virtual Screening: Are We There Yet?

“…These indices have been shown to identify interesting features [96] based on the topological shape of a molecule and its set of functional groups that are strongly linked with activity. A data-shaving study [97] has exploited the presence and absence of a subset of these indices in both active and inactive compounds to "shave" off or deprioritize compounds similar to inactives from LBVS. Similarity searching was shown to improve when compounds predicted to be inactive were deprioritized.…”

Multiparametric Analysis of Screening Data: Growing Beyond the Single Dimension to Infinity and Beyond

“…59 In essence, this approach is similar to that first presented for analysis of microarray results 84 and HTS results. 85 It may also be possible to extend concepts such as a maximum common substructure (a way to identify the maximal common feature in sets of compounds), which could be applied to identify the maximum set of common features that define a phenotype.…”

In Silico Screening

“…In principle, three types of databases can be distinguished: targeted or 3.8. Using a fast similarity searching algorithm, compounds being very similar to known inactive structures can be identified and omitted, a technique introduced as ''data shaving'' [105]. Targeted and focused libraries are designed to have a high probability to be enriched with compounds that are active against individual targets or a target family.…”