1 citations · 1 across the 4 of their papers we have counts for
11 papers
MiLAC-Aided Beamforming for MIMO Over-the-Air Computation
Yaru Wang, Deyou Zhang, Qingchao Li +2
Over-the-air computation (AirComp) enables low-latency wireless data aggregation, but its accuracy is limited by imperfect signal alignment over fading channels and receiver noise.…
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…
Thermodynamics-Informed Accurate pKa Prediction and Protonation State Generation in PlayMolecule AI
Francesco Pesce, Stephen Farr, Gianni de Fabritiis
Accurate prediction of acid dissociation constants (p) and the determination of dominant protonation states is critical in drug discovery, influencing molecular properti…
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…
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…
REINFORCE-ING Chemical Language Models for Drug Discovery
Morgan Thomas, Albert Bou, Jose Carlos Gómez-Tamayo +3
Chemical language models, combined with reinforcement learning (RL), have shown significant promise to efficiently traverse large chemical spaces for drug discovery. However, the p…