3 citations · 4 across the 5 of their papers we have counts for
5 papers
Structure-guided molecular design with contrastive 3D protein-ligand learning
Carles Navarro, Philipp Tholke, Gianni de Fabritiis
Structure-based drug discovery faces the dual challenge of accurately capturing 3D protein-ligand interactions while navigating ultra-large chemical spaces to identify syntheticall…
Speak to a Protein: An Interactive Multimodal Co-Scientist
Carles Navarro, Mariona Torrens, Philipp Thölke +2
Building a working mental model of a protein typically requires weeks of reading, cross-referencing crystal and predicted structures, and inspecting ligand complexes, an effort tha…
On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction
Nikolai Schapin, Carles Navarro, Albert Bou +1
Binding affinity optimization is crucial in early-stage drug discovery. While numerous machine learning methods exist for predicting ligand potency, their comparative efficacy rema…
ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
Albert Bou, Morgan Thomas, Sebastian Dittert +10
In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. Howeve…
Top-down machine learning of coarse-grained protein force-fields
Carles Navarro, Maciej Majewski, Gianni de Fabritiis
Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended timescales. Our me…