Work

Impact of Pre-trained Representations in Machine Learning-Based Protein-Ligand Docking

Year: 2025

Type: dissertation

Abstract: 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.

Source: Electronic Theses and Dissertations Repository (University of Pisa)

Author: RONCOLI, ANDREA

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7047794758

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