GraphDTA for predicting drug–target binding affinity

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: GraphDTA for predicting drug–target binding affinity

Abstract: Drug development is costly and time-consuming, but drug repurposing offers a quicker alternative by finding new uses for approved drugs. Knowing drug-protein interactions is crucial for this process. GraphDTA, a model that predicts drug-target affinity using graph neural networks, outperforms established models like DeepDTA and WideDTA in accuracy and adaptability. While GraphDTA relies on SMILES representations, incorporating 3D structural data could retain vital molecular information, enhancing predictions. This highlights GraphDTA's potential to improve with further development, making it a robust and versatile tool for drug-target affinity predictions

Subject: Medicaments--Monitoratge

Academic year: 2023-2024

Language: en

Work's public defense date: 2024-09-16

Subject areas: Computer engineering

Student: Gálvez Rísquez, Natzaret

Department: Enginyeria Informàtica i Matemàtiques

Creation date in repository: 2025-10-23

Keywords: GraphDTA, drug-target binding affinity, prediction

Title in original language: GraphDTA for predicting drug–target binding affinity

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

Project director: Serratosa Casanelles, Francesc D'assís