Tootfinder

Replaced article(s) found for q-bio.BM. https://arxiv.org/list/q-bio.BM/new
[1/1]:
- Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings
Aditya Sengar, Ali Hariri, Daniel Probst, Patrick Barth, Pierre Vandergheynst

Quantum Optimization on Rydberg Atom Arrays with Arbitrary Connectivity: Gadgets Limitations and a Heuristic Approach
Pierre Cazals, Amalia Sorondo, Victor Onofre, Constantin Dalyac, Wesley da Silva Coelho, Vittorio Vitale
https://arxiv.org/abs/2508.06130

Quantum Optimization on Rydberg Atom Arrays with Arbitrary Connectivity: Gadgets Limitations and a Heuristic Approach
Programmable quantum systems based on Rydberg atom arrays have recently emerged as a promising testbed for combinatorial optimization. Indeed, the Maximum Weighted Independent Set problem on unit-disk graphs can be efficiently mapped to such systems due to their geometric constraints. However, extending this capability to arbitrary graph instances typically necessitates the use of reduction gadgets, which introduce additional experimental overhead and complexity. Here, we analyze the complexity…

Attributed-graphs kernel implementation using local detuning of neutral-atoms Rydberg Hamiltonian
Mehdi Djellabi, Matthias Hecker, Shaheen Acheche
https://arxiv.org/abs/2509.09421

Attributed-graphs kernel implementation using local detuning of neutral-atoms Rydberg Hamiltonian
We extend the quantum-feature kernel framework, which relies on measurements of graph-dependent observables, along three directions. First, leveraging neutral-atom quantum processing units (QPUs), we introduce a scheme that incorporates attributed graphs by embedding edge features into atomic positions and node features into local detuning fields of a Rydberg Hamiltonian. We demonstrate both theoretically and empirically that local detuning enhances kernel expressiveness. Second, in addition to…

On the Characteristic polynomial of ABS Matrix and ABS-Energy of Some Graphs
Sharad Barde, Ganesh Mundhe, Mayur Kshirsagar, A. N. Bhavale
https://arxiv.org/abs/2509.02155 https:…

On the Characteristic polynomial of ABS Matrix and ABS-Energy of Some Graphs
For a graph $G$ with $n$ vertices and $m$ edges, Lin \textit{et al.} \cite{Lin} define the \textit{atom--bond sum-connectivity} ($ABS$) matrix of $G$ such that the $(i,j)^{\text{th}}$ entry is \[ \sqrt{1 - \frac{2}{d_i + d_j}} \] if vertex $v_i$ is adjacent to the vertex $v_j$, and $0$ otherwise. In this article, we determine the characteristic polynomial of the $ABS$ matrix for certain specific classes of graphs. Furthermore, we compute the $ABS$ eigenvalues and the $ABS$ energy for these clas…

Pi-SAGE: Permutation-invariant surface-aware graph encoder for binding affinity prediction
Sharmi Banerjee, Mostafa Karimi, Melih Yilmaz, Tommi Jaakkola, Bella Dubrov, Shang Shang, Ron Benson
https://arxiv.org/abs/2508.01924

Pi-SAGE: Permutation-invariant surface-aware graph encoder for binding affinity prediction
Protein surface fingerprint encodes chemical and geometric features that govern protein-protein interactions and can be used to predict changes in binding affinity between two protein complexes. Current state-of-the-art models for predicting binding affinity change, such as GearBind, are all-atom based geometric models derived from protein structures. Although surface properties can be implicitly learned from the protein structure, we hypothesize that explicit knowledge of protein surfaces can …

A Scalable Heuristic for Molecular Docking on Neutral-Atom Quantum Processors
Mathieu Garrigues, Victor Onofre, Wesley Coelho, S. Acheche
https://arxiv.org/abs/2508.18147 https:…

A Scalable Heuristic for Molecular Docking on Neutral-Atom Quantum Processors
Molecular docking is a critical computational method in drug discovery used to predict the binding conformation and orientation of a ligand within a protein's binding site. Mapping this challenge onto a graph-based problem, specifically the Maximum Weighted Independent Set (MWIS) problem, allows it to be addressed by specialized hardware such as neutral-atom quantum processors. However, a significant bottleneck has been the size mismatch between biologically relevant molecular systems and the l…

From Atoms to Dynamics: Learning the Committor Without Collective Variables
Sergio Contreras Arredondo, Chenyu Tang, Radu A. Talmazan, Alberto Meg\'ias, Cheng Giuseppe Chen, Christophe Chipot
https://arxiv.org/abs/2507.17700

From Atoms to Dynamics: Learning the Committor Without Collective Variables
This Brief Communication introduces a graph-neural-network architecture built on geometric vector perceptrons to predict the committor function directly from atomic coordinates, bypassing the need for hand-crafted collective variables (CVs). The method offers atom-level interpretability, pinpointing the key atomic players in complex transitions without relying on prior assumptions. Applied across diverse molecular systems, the method accurately infers the committor function and highlights the i…

DeepMech: A Machine Learning Framework for Chemical Reaction Mechanism Prediction
Manajit Das, Ajnabiul Hoque, Mayank Baranwal, Raghavan B. Sunoj
https://arxiv.org/abs/2509.15872

DeepMech: A Machine Learning Framework for Chemical Reaction Mechanism Prediction
Prediction of complete step-by-step chemical reaction mechanisms (CRMs) remains a major challenge. Whereas the traditional approaches in CRM tasks rely on expert-driven experiments or costly quantum chemical computations, contemporary deep learning (DL) alternatives ignore key intermediates and mechanistic steps and often suffer from hallucinations. We present DeepMech, an interpretable graph-based DL framework employing atom- and bond-level attention, guided by generalized templates of mechani…