<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>drug discovery on Wonderland of Deisy Gysi</title><link>https://deisygysi.github.io/tags/drug-discovery/</link><description>Recent content in drug discovery on Wonderland of Deisy Gysi</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Tue, 01 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://deisygysi.github.io/tags/drug-discovery/index.xml" rel="self" type="application/rss+xml"/><item><title>Drug Discovery &amp; Repurposing</title><link>https://deisygysi.github.io/projects/drugrepurpusing/</link><pubDate>Tue, 01 Sep 2026 00:00:00 +0000</pubDate><guid>https://deisygysi.github.io/projects/drugrepurpusing/</guid><description>Summary Discovering a therapy requires connecting evidence across molecular profiles, chemical structures, protein targets, pathways, clinical data and experimental models. We develop and evaluate computational strategies that bring these sources together to prioritize drug-target relationships and therapeutic candidates.
Our work combines network medicine, machine learning, molecular docking and experimental validation. A central goal is to understand when predictive models generalize to new proteins, compounds and diseases, rather than performing well only on well-annotated examples.</description></item></channel></rss>