4 citations · 10 across the 7 of their papers we have counts for
7 papers
EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Serving
Vittorio Palladino, Gianluca Palermo, Michael E. Papka +1
As large language models span dense, mixture-of-experts, and state-space architectures and are deployed on heterogeneous accelerators under increasingly diverse multimodal workload…
A Physically-Informed Subgraph Isomorphism Approach to Molecular Docking Using Quantum Annealers
Francesco Micucci, Matteo Barbieri, Gabriella Bettonte +6
Molecular docking is a crucial step in the development of new drugs as it guides the positioning of a small molecule (ligand) within the pocket of a target protein. In the literatu…
Towards High-Performance and Portable Molecular Docking on CPUs through Vectorization
Gianmarco Accordi, Jens Domke, Theresa Pollinger +2
Recent trends in the HPC field have introduced new CPU architectures with improved vectorization capabilities that require optimization to achieve peak performance and thus pose ch…
Molecular Docking via Weighted Subgraph Isomorphism on Quantum Annealers
Emanuele Triuzzi, Riccardo Mengoni, Francesco Micucci +4
Molecular docking is an essential step in the drug discovery process involving the detection of three-dimensional poses of a ligand inside the active site of the protein. In this p…
ZSMILES: an approach for efficient SMILES storage for random access in Virtual Screening
Gianmarco Accordi, Davide Gadioli, Giorgio Seguini +2
Virtual screening is a technique used in drug discovery to select the most promising molecules to test in a lab. To perform virtual screening, we need a large set of molecules as i…
Tunable and Portable Extreme-Scale Drug Discovery Platform at Exascale: the LIGATE Approach
Gianluca Palermo, Gianmarco Accordi, Davide Gadioli +32
Today digital revolution is having a dramatic impact on the pharmaceutical industry and the entire healthcare system. The implementation of machine learning, extreme-scale computer…
GENEOnet: A new machine learning paradigm based on Group Equivariant Non-Expansive Operators. An application to protein pocket detection
Giovanni Bocchi, Patrizio Frosini, Alessandra Micheletti +5
Nowadays there is a big spotlight cast on the development of techniques of explainable machine learning. Here we introduce a new computational paradigm based on Group Equivariant N…