<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>molecular-similarity on Sinem's Blog</title><link>https://sinembudak.com/tags/molecular-similarity/</link><description>Recent content in molecular-similarity on Sinem's Blog</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><copyright>© 2026. Sinem Demirkaya-Budak</copyright><lastBuildDate>Mon, 06 Apr 2026 16:29:27 +0300</lastBuildDate><atom:link href="https://sinembudak.com/tags/molecular-similarity/index.xml" rel="self" type="application/rss+xml"/><item><title>Understanding Tanimoto Similarity in Cheminformatics</title><link>https://sinembudak.com/posts/understanding-tanimoto-similarity-in-cheminformatics/</link><pubDate>Mon, 06 Apr 2026 16:29:27 +0300</pubDate><guid>https://sinembudak.com/posts/understanding-tanimoto-similarity-in-cheminformatics/</guid><description>The Tanimoto coefficient (also known as Jaccard similarity) is a statistical measure that quantifies how similar two objects are, producing a value between 0 and 1. In cheminformatics, it&amp;rsquo;s the gold standard for comparing molecular fingerprints and is widely used in drug discovery, virtual screening, and chemical database searches.
Value Range:
0: Completely dissimilar 1: Identical For example, if you&amp;rsquo;re searching for drugs similar to aspirin, the Tanimoto coefficient tells you quantitatively how similar each candidate compound is to aspirin.</description></item></channel></rss>