239 citations · 676 across the 20 of their papers we have counts for
20 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…
AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules
Stephen E. Farr, Stefan Doerr, Antonio Mirarchi +2
We introduce AceFF, a pre-trained machine learning interatomic potential (MLIP) optimized for small molecule drug discovery. While MLIPs have emerged as efficient alternatives to D…
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…
Test-Time Training Scaling Laws for Chemical Exploration in Drug Design
Morgan Thomas, Albert Bou, Gianni De Fabritiis
Chemical Language Models (CLMs) leveraging reinforcement learning (RL) have shown promise in de novo molecular design, yet often suffer from mode collapse, limiting their explorati…
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
Antonio Mirarchi, Raul P. Pelaez, Guillem Simeon +1
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological process…
Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
Gianni De Fabritiis
Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. Here, I explore thei…