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Multi-task generative topographic mapping in virtual screening.

Arkadii Lin1,2, Dragos Horvath1, Gilles Marcou1

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

Journal of Computer-Aided Molecular Design
|February 11, 2019
PubMed
Summary
This summary is machine-generated.

Generative Topographic Maps (GTMs) offer "fit-free" drug discovery predictions. Optimized GTMs excel in virtual screening and support diverse predictive modeling approaches.

Keywords:
Big dataChEMBLDUDGenerative topographic mappingLigand-based virtual screeningMulti-task learningNeural networksUniversal maps

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Area of Science:

  • Computational Chemistry
  • Cheminformatics
  • Machine Learning in Drug Discovery

Background:

  • Generative Topographic Maps (GTMs) are powerful tools for visualizing and analyzing chemical space.
  • Previous methods for generating universal GTMs involved complex multi-task learning and evolutionary optimization.
  • These universal GTMs offer predictive models without requiring target-specific parameter fitting.

Purpose of the Study:

  • To evaluate the performance of universal GTMs in Virtual Screening (VS) tasks.
  • To assess the utility of descriptor spaces selected by evolutionary multi-task learning for various VS methods.
  • To explore the adaptability of universal GTMs for diverse predictive modeling applications.

Main Methods:

  • Utilized an evolutionary multi-task learning process to select GTM parameters and hyperparameters, including molecular descriptor spaces.
  • Generated "universal" GTM manifolds optimized for neighborhood behavior compliance across multiple biological targets.
  • Simulated Virtual Screening using external DUD ligand and decoy sets, disjoint from the GTM training data.
  • Applied GTM "coloring" to generate property landscapes for predictive modeling.
  • Evaluated the performance of descriptor spaces in parameter-free similarity searching, local GTM models, Random Forest, and Neural Network approaches.

Main Results:

  • Universal GTM manifolds demonstrated robust performance in Virtual Screening challenges.
  • The descriptor spaces selected by the evolutionary multi-task learner proved highly effective for various VS procedures.
  • These descriptor spaces supported a range of methods, from parameter-free similarity searching to complex machine learning models.
  • The "fit-free" nature of universal GTM predictions was confirmed in external validation.

Conclusions:

  • Universal GTMs provide a reliable and efficient framework for "fit-free" property prediction and Virtual Screening.
  • Evolutionary multi-task learning effectively identifies optimal descriptor spaces for chemical space analysis and prediction.
  • The selected descriptor spaces enhance the performance of diverse Virtual Screening methodologies, highlighting their broad applicability.