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Stochastic Proximity Embedding: Methods and Applications.

Dimitris K Agrafiotis1, Huafeng Xu2, Fangqiang Zhu3

  • 1Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, PA 19477, USA tel: (215) 628-6814. dagrafio@its.jnj.com.

Molecular Informatics
|July 28, 2016
PubMed
Summary
This summary is machine-generated.

The stochastic proximity embedding (SPE) algorithm generates Euclidean coordinates to satisfy geometric constraints. Its simplicity, speed, and broad applicability make it valuable in computational chemistry and biology.

Keywords:
AlignmentBoostingConformational analysisDimensionality reductionDockingLoop modelingManifold learningNonlinear mappingPharmacophoreProtein loopSelf-organizing superpositionStochastic proximity embeddingStochastic searchStructure depictionSystematic search

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Area of Science:

  • Computational Chemistry
  • Computational Biology
  • Bioinformatics
  • Cheminformatics

Background:

  • The stochastic proximity embedding (SPE) algorithm, developed in 1996, is a powerful tool for constraint satisfaction in geometric problems.
  • SPE has demonstrated significant success across diverse applications in computational chemistry and biology.
  • Its core function involves generating Euclidean coordinates that adhere to specified geometric constraints.

Purpose of the Study:

  • To review the key applications of the stochastic proximity embedding (SPE) algorithm.
  • To outline the known limitations of SPE and discuss methods to overcome them.
  • To highlight potential new domains for SPE application that could yield significant breakthroughs.

Main Methods:

  • The review focuses on the conceptual framework and algorithmic properties of SPE.
  • Analysis of published studies showcasing SPE's utility in various scientific domains.
  • Identification of limitations and proposed strategies for their circumvention.

Main Results:

  • SPE's conceptual and programmatic simplicity is a key factor in its widespread adoption.
  • The algorithm exhibits superior speed and scalability, making it efficient for large datasets.
  • SPE's broad applicability has been demonstrated across numerous computational problems.

Conclusions:

  • The stochastic proximity embedding (SPE) algorithm remains a highly relevant and effective technique.
  • Addressing current limitations can further expand its utility and impact.
  • Future research should explore novel applications of SPE in emerging scientific challenges.