Tesi etd-02132025-131258 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
RONCOLI, ANDREA
URN
etd-02132025-131258
Titolo
Impact of Pre-trained Representations in Machine Learning-Based Protein-Ligand Docking
Dipartimento
INFORMATICA
Corso di studi
INFORMATICA
Relatori
relatore Prof. Bacciu, Davide
correlatore Prof. AlQuraishi, Mohammed
correlatore Prof. AlQuraishi, Mohammed
Parole chiave
- biology
- docking
- drug discovery
- machine learning
- pretraining
Data inizio appello
28/02/2025
Consultabilità
Completa
Riassunto
This work analyzes possible avenues for improving performance and generalization of machine learning-based models for protein-ligand docking. Learned representations from models pre-trained on different chemical tasks are leveraged by introducing them as features in the molecular graphs, which generative docking models are then trained on.
File
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Thesis_Roncoli.pdf | 6.27 Mb |
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