Stochastic Proximity Embedding: Methods and Applications

Since its inception in 1996, the stochastic proximity embedding (SPE) algorithm and its variants have been applied to a wide range of problems in computational chemistry and biology with notable success. At its core, SPE attempts to generate Euclidean coordinates for a set of points so that they satisfy a prescribed set of geometric constraints. The algorithm's appeal rests on three factors: 1) its conceptual and programmatic simplicity; 2) its superior speed and scaling properties; and 3) its broad applicability. Here, we review some of the key applications, outline known limitations and ways to circumvent them, and highlight additional problem domains where the use of this technique could lead to significant breakthroughs.