Tootfinder

Predicting solvation free energies with an implicit solvent machine learning potential
Sebastien R\"ocken, Anton F. Burnet, Julija Zavadlav
https://arxiv.org/abs/2406.00183 <…

Predicting solvation free energies with an implicit solvent machine learning potential
Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations employing best-performing graph neural network architectures are still too expensive for applications requiring extensive sampling, such as free energy computations. Implicit solvent models could provide the necessary speed-up due to reduced degrees of freedom and faster dynamics. Here, we introduce a Solvation Free …

Graph Algorithms with Neutral Atom Quantum Processors
Constantin Dalyac, Lucas Leclerc, Louis Vignoli, Mehdi Djellabi, Wesley da Silva Coelho, Bruno Ximenez, Alexandre Dareau, Davide Dreon, VIncent E. Elfving, Adrien Signoles, Louis-Paul Henry, Lo\"ic Henriet
https://arxiv.org/abs/2403.11931…

Graph Algorithms with Neutral Atom Quantum Processors
Neutral atom technology has steadily demonstrated significant theoretical and experimental advancements, positioning itself as a front-runner platform for running quantum algorithms. One unique advantage of this technology lies in the ability to reconfigure the geometry of the qubit register, from shot to shot. This unique feature makes possible the native embedding of graph-structured problems at the hardware level, with profound consequences for the resolution of complex optimization and mach…

Message-Passing Interatomic Potentials Learn Non-Local Electrostatic Interactions
Sungwoo Kang
https://arxiv.org/abs/2405.00290 https://

Message-Passing Interatomic Potentials Learn Non-Local Electrostatic Interactions
This work demonstrates that E(3)-equivariant graph neural network interatomic potentials (GNN-IPs) effectively learn non-local electrostatic interactions and charge transfers. Using a toy model with point charges, it is confirmed that GNN-IPs adeptly interpolate and extrapolate electrostatic interactions, independent of geometric coupling. It is also found that the electrostatic energy between an atom pair is distributed among neighboring atoms, rather than localized solely within the two atoms…

HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction
Bin Liu, Siqi Wu, Jin Wang, Xin Deng, Ao Zhou
https://arxiv.org/abs/2404.10561

HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction
The discovery of drug-target interactions (DTIs) plays a crucial role in pharmaceutical development. The deep learning model achieves more accurate results in DTI prediction due to its ability to extract robust and expressive features from drug and target chemical structures. However, existing deep learning methods typically generate drug features via aggregating molecular atom representations, ignoring the chemical properties carried by motifs, i.e., substructures of the molecular graph. The a…

Graph neural network coarse-grain force field for the molecular crystal RDX
Brian H. Lee, James P. Larentzos, John K. Brennan, Alejandro Strachan
https://arxiv.org/abs/2403.15266 …

Graph neural network coarse-grain force field for the molecular crystal RDX
Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable appro…

HEroBM: a deep equivariant graph neural network for universal backmapping from coarse-grained to all-atom representations
Daniele Angioletti, Stefano Raniolo, Vittorio Limongelli
https://arxiv.org/abs/2404.16911

HEroBM: a deep equivariant graph neural network for universal backmapping from coarse-grained to all-atom representations
Molecular simulations have assumed a paramount role in the fields of chemistry, biology, and material sciences, being able to capture the intricate dynamic properties of systems. Within this realm, coarse-grained (CG) techniques have emerged as invaluable tools to sample large-scale systems and reach extended timescales by simplifying system representation. However, CG approaches come with a trade-off: they sacrifice atomistic details that might hold significant relevance in deciphering the inv…