Weighted Tanimoto Coefficient for 3D Molecule Structure Similarity Measurement

TL;DR

This paper proposes a Weighted Tanimoto Coefficient using weighted Euclidean distance to improve 3D molecular similarity searching, demonstrating that weighting significantly affects search results.

Contribution

Introduces a novel weighted Tanimoto coefficient based on weighted Euclidean distance for 3D molecule similarity measurement, especially for non-binary data.

Findings

Weighting influences similarity search outcomes.

Weighted Tanimoto differs from traditional methods.

Effective for non-binary molecular data.

Abstract

Similarity searching of molecular structure has been an important application in the Chemoinformatics, especially in drug discovery. Similarity searching is a common method used for identification of molecular structure. It involve three main principal component of similarity searching: structure representation; weighting scheme; and similarity coefficient. In this paper, we introduces Weighted Tanimoto Coefficient based on weighted Euclidean distance in order to investigate the effect of weight function on the result for similarity searching. The Tanimoto coefficient is one of the popular similarity coefficients used to measure the similarity between pairs of the molecule. The most of research area found that the similarity searching is based on binary or fingerprint data. Meanwhile, we used non-binary data and was set amphetamine structure as a reference or targeted structure and the rest of the dataset becomes a database structure. Throughout this study, it showed that there is definitely gives a different result between a similarity searching with and without weight.