A5092031248- Computational Drug Discovery Methods
- Chemical Synthesis and Analysis
- Machine Learning in Materials Science
- Protein Structure and Dynamics
- Click Chemistry and Applications
- Bioinformatics and Genomic Networks
- Advanced Graph Neural Networks
ETH Zurich
2023-2025
Abstract De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- structure-based generation of drug-like molecules. This method capitalizes on the unique strengths both graph neural networks language models, offering an alternative need application-specific reinforcement, transfer, or few-shot learning. It enables “zero-shot"...
We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on hierarchical tree constructed from attention values extracted graph neural network (GNN), which was trained to predict atomic accurate quantum-mechanical (QM) calculations. resulting dynamic attention-based substructure hierarchy (DASH) provides fast assignment with the same accuracy as GNN itself, software-independent, can easily be integrated existing...
De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- structure-based generation of drug-like molecules. This method capitalizes on the unique strengths both graph neural networks language models, offering an alternative need application-specific reinforcement, transfer, or few-shot learning. It allows construction compound libraries...
We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on hierarchical tree constructed from attention values extracted graph neural network (GNN), which was trained to predict atomic accurate quantum-mechanical (QM) calculations. resulting dynamic attention-based substructure hierarchy (DASH) provides fast assignment with the same accuracy as GNN itself, software-independent, can easily be integrated existing...
Interpretability and reliability of deep learning models are important for computer-based drug discovery. Aiming to understand feature perception by such a model, we investigate graph neural network affinity prediction protein-ligand complexes. We assess latent representation ligand binding sites underlying geometric structure in this space its relation protein function. introduce an automated computational pipeline dimensionality reduction, clustering, hypothesis testing, visualization...