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Multi-task generative topographic mapping in virtual screening

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Abstract

The previously reported procedure to generate “universal” Generative Topographic Maps (GTMs) of the drug-like chemical space is in practice a multi-task learning process, in which both operational GTM parameters (example: map grid size) and hyperparameters (key example: the molecular descriptor space to be used) are being chosen by an evolutionary process in order to fit/select “universal” GTM manifolds. After selection (a one-time task aimed at optimizing the compromise in terms of neighborhood behavior compliance, over a large pool of various biological targets), for any further use the manifolds are ready to provide “fit-free” predictive models. Using any structure–activity set—irrespectively whether the associated target served at map fitting stage or not—the generation or “coloring” a property landscape enables predicting the property for any external molecule, with zero additional fitable parameters involved. While previous works have signaled the excellent behavior of such models in aggressive three-fold cross-validation assessments of their predictive power, the present work wished to explore their behavior in Virtual Screening (VS), here simulated on hand of external DUD ligand and decoy series that are fully disjoint from the ChEMBL-extracted landscape coloring sets. Beyond the rather robust results of the universal GTM manifolds in this challenge, it could be shown that the descriptor spaces selected by the evolutionary multi-task learner were intrinsically able to serve as an excellent support for many other VS procedures, starting from parameter-free similarity searching, to local (target-specific) GTM models, to parameter-rich, nonlinear Random Forest and Neural Network approaches.

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Abbreviations

GTM:

Generative topographic mapping

UGTM:

Universal generative topographic mapping

GA:

Genetic algorithm

CV:

Cross-validation

DUD:

Directory of Useful Decoys

NN:

Neural network

RF:

Random forest

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Funding

The project leading to this article has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant agreement No 676434, “Big Data in Chemistry” (“BIGCHEM”, http://bigchem.eu).

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Authors and Affiliations

  1. Laboratory of Chemoinformatics, Faculty of Chemistry, University of Strasbourg, 4, Blaise Pascal Str., 67081, Strasbourg, France

    Arkadii Lin, Dragos Horvath, Gilles Marcou & Alexandre Varnek

  2. Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorferstrasse 65, 88397, Biberach an der Riss, Germany

    Arkadii Lin & Bernd Beck

Authors
  1. Arkadii Lin
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  2. Dragos Horvath
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  3. Gilles Marcou
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  4. Bernd Beck
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  5. Alexandre Varnek
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The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

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Correspondence to Alexandre Varnek.

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Lin, A., Horvath, D., Marcou, G. et al. Multi-task generative topographic mapping in virtual screening. J Comput Aided Mol Des 33, 331–343 (2019). https://doi.org/10.1007/s10822-019-00188-x

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