A novel real-time editor for protein-ligand binding affinity prediction using structural comparison

Entity: Universitat Rovira i Virgili (URV)

Confidenciality: No

Education area(s): Enginyeria de la Seguretat Informàtica i Intel·ligència Artificial

APS: No

Title in different languages: A novel real-time editor for protein-ligand binding affinity prediction using structural comparison

Abstract: This thesis presents a novel method for protein-ligand binding affinity prediction, integrating structural comparisons using Graph Edit Distance (GED) and molecular descriptors. A real-time graphical editor is developed for visualizing molecular structures, calculating GED, and comparing binding affinities. Experimental results on the SARS-CoV-2 main protease dataset demonstrate the approach’s efficiency, combining interpretability and prediction accuracy. Additionally, a K-Nearest Neighbors (KNN) model is utilized for affinity prediction, supported by quantitative error analysis and visualization tools. This research aligns with ongoing efforts at Universitat Rovira i Virgili (URV) to innovate computational drug discovery methods, emphasizing transparency and practical applications in cheminformatics.

Subject: Proteïnes

Academic year: 2024-2025

Language: en

Work's public defense date: 2025-06-12

Subject areas: Computer engineering

Student: Parade Patil, Kuldeep Shivaji

Department: Enginyeria Informàtica i Matemàtiques

Creation date in repository: 2026-03-13

TFM credits: 9

Keywords: real-time editor, binding affinity, structural comparison

Title in original language: A novel real-time editor for protein-ligand binding affinity prediction using structural comparison

Access Rights: info:eu-repo/semantics/openAccess

Project director: Serratosa Casanelles, Francesc d'Assís