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Virtual Screening with Generative Topographic Maps: How Many Maps Are Required?

Abstract：

:Universal generative topographic maps (GTMs) provide two-dimensional representations of chemical space selected for their "polypharmacological competence", that is, the ability to simultaneously represent meaningful activity and property landscapes, associated with many distinct targets and properties. Several such GTMs can be generated, each based on a different initial descriptor vector, encoding distinct structural features. While their average polypharmacological competence may indeed be equivalent, they nevertheless significantly diverge with respect to the quality of each property-specific landscape. In this work, we show that distinct universal maps represent complementary and strongly synergistic views of biologically relevant chemical space. Eight universal GTMs were employed as support for predictive classification landscapes, using more than 600 active/inactive ligand series associated with as many targets from the ChEMBL database (v.23). For nine of these targets, it was possible to extract, from the Directory of Useful Decoys (DUD), truly external sets featuring sufficient "actives" and "decoys" not present in the landscape-defining ChEMBL ligand sets. For each such molecule, projected on every class landscape of a particular universal map, a probability of activity was estimated, in analogy to a virtual screening (VS) experiment. Cross-validated (CV) balanced accuracy on landscape-defining ChEMBL data was unable to predict the success of that landscape in VS. Thus, the universal map with best CV results for a given property should not be prioritized as the implicitly best predictor. For a given map, predictions for many DUD compounds are not trustworthy, according to applicability domain considerations. By contrast, simultaneous application of all universal maps, and rating of the likelihood of activity as the mean returned by all applicable maps, significantly improved prediction results. Performance measures in consensus VS using multiple maps were always superior or similar to those of the best individual map.

journal_name

J Chem Inf Model

journal_title

Journal of chemical information and modeling

authors

Casciuc I,Zabolotna Y,Horvath D,Marcou G,Bajorath J,Varnek A

doi

10.1021/acs.jcim.8b00650

subject

Has Abstract

pub_date

2019-01-28 00:00:00

pages

564-572

issue

1

eissn

1549-9596

issn

1549-960X

journal_volume

59

pub_type

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