SCISSORS: a linear-algebraical technique to rapidly approximate chemical similarities.
Algorithms for several emerging large-scale problems in cheminformatics have as their rate-limiting step the evaluation of relatively slow chemical similarity measures, such as structural similarity or three-dimensional (3-D) shape comparison. In this article we present SCISSORS, a linear-algebraical technique (related to multidimensional scaling and kernel principal components analysis) to rapidly estimate chemical similarities for several popular measures. We demonstrate that SCISSORS faithfully reflects its source similarity measures for both Tanimoto calculation and rank ordering. After an efficient precalculation step on a database, SCISSORS affords several orders of magnitude of speedup in database screening. SCISSORS furthermore provides an asymptotic speedup for large similarity matrix construction problems, reducing the number of conventional slow similarity evaluations required from quadratic to linear scaling.
Haque
Imran S
IS
Department of Computer Science and Department of Chemistry, Stanford University, Stanford, CA, USA.
Pande
Vijay S
VS
D000465:Algorithms; D016208:Databases, Factual; D004353:Drug Evaluation, Preclinical; D048088:Informatics / Q000379:methods*; D016014:Linear Models; D025341:Principal Component Analysis; D013997:Time Factors; D014584:User-Computer Interface