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Quantum geometrical molecular dynamics provides a quantum geometric picture for understanding reactive dynamics, especially excited-state conical intersection dynamics, and also a numerically exact method for strongly correlated…

Chemical Physics · Physics 2026-01-26 Mo Sha , Bing Gu

We introduce a method to bring nearly atomistic resolution to coarse-grained models, and we apply the method to proteins. Using a small number of coarse-grained sites (about one per eight atoms) but assigning an independent…

Statistical Mechanics · Physics 2014-10-01 Thomas K. Haxton

An RNA sequence is a word over an alphabet on four elements $\{A,C,G,U\}$ called bases. RNA sequences fold into secondary structures where some bases match one another while others remain unpaired. Pseudoknot-free secondary structures can…

Data Structures and Algorithms · Computer Science 2018-03-28 Édouard Bonnet , Paweł Rzążewski , Florian Sikora

RNA molecules are known to form complex secondary structures including pseudoknots. A systematic framework for the enumeration, classification and prediction of secondary structures is critical to determine the biological significance of…

Biomolecules · Quantitative Biology 2025-12-24 Rayan Ibrahim , Allison H. Moore

Constructing atom-resolved states from low-resolution data is of practical importance in many areas of science and engineering. This problem is addressed in this paper in the context of multiscale factorization methods for molecular…

Computational Physics · Physics 2016-04-14 Andrew Abi Mansour , Peter J. Ortoleva

During the last decade coarse-grained nucleotide models have emerged that allow us to DNA and RNA on unprecedented time and length scales. Among them is oxDNA, a coarse-grained, sequence-specific model that captures the hybridisation…

Soft Condensed Matter · Physics 2018-05-08 Oliver Henrich , Yair Augusto Gutierrez-Fosado , Tine Curk , Thomas E. Ouldridge

This paper presents a novel graph-based deep learning model for tasks involving relations between two nodes (edge-centric tasks), where the focus lies on predicting relationships and interactions between pairs of nodes rather than node…

Machine Learning · Computer Science 2025-07-08 Eugenio Borzone , Leandro Di Persia , Matias Gerard

The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and proteins, and solves it with Graph Convolutional Networks (GCNs).…

Machine Learning · Computer Science 2020-01-29 Hao Xu , Shengqi Sang , Haiping Lu

We consider a coarse-grained (CG) model with pairwise interactions, suitable to describe low-density solutions of star-branched polymers of functionality $f$. Each macromolecule is represented by a CG molecule with $(f+1)$ interaction…

Soft Condensed Matter · Physics 2017-08-02 Roberto Menichetti , Andrea Pelissetto , Ferdinando Randisi

We review a coarse-graining strategy (multiblob approach) for polymer solutions in which groups of monomers are mapped onto a single atom (a blob) and effective blob-blob interactions are obtained by requiring the coarse-grained model to…

Soft Condensed Matter · Physics 2015-03-19 Giuseppe D'Adamo , Andrea Pelissetto , Carlo Pierleoni

The techniques of data-driven backmapping from coarse-grained (CG) to fine-grained (FG) representation often struggle with accuracy, unstable training, and physical realism, especially when applied to complex systems such as proteins. In…

Machine Learning · Computer Science 2025-05-26 Georgios Kementzidis , Erin Wong , John Nicholson , Ruichen Xu , Yuefan Deng

Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce. We introduce \emph{RiboSphere}, a…

Machine Learning · Computer Science 2026-03-23 Zhou Zhang , Hanqun Cao , Cheng Tan , Fang Wu , Pheng Ann Heng , Tianfan Fu

Coarse-grained (CG) models play a crucial role in the study of protein structures, protein thermodynamic properties, and protein conformation dynamics. Due to the information loss in the coarse-graining process, backmapping from CG to…

Quantitative Methods · Quantitative Biology 2023-11-30 Yikai Liu , Ming Chen , Guang Lin

Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having…

Computational Physics · Physics 2024-03-29 Arthur Y. Lin , Kevin K. Huguenin-Dumittan , Yong-Cheol Cho , Jigyasa Nigam , Rose K. Cersonsky

Molecules are commonly represented as SMILES strings, which can be readily converted to fixed-size molecular fingerprints. These fingerprints serve as feature vectors to train ML/DL models for molecular property prediction tasks in the…

Machine Learning · Computer Science 2026-03-10 Rajan , Ishaan Gupta

Elastic network models (ENMs) are valuable and efficient tools for characterizing the collective internal dynamics of proteins based on the knowledge of their native structures. The increasing evidence that the biological functionality of…

Biomolecules · Quantitative Biology 2015-09-01 Giovanni Pinamonti , Sandro Bottaro , Cristian Micheletti , Giovanni Bussi

Simulations of knotting and unknotting in polymers or other filaments rely on random processes to facilitate topological changes. Here we introduce a method of \textit{topological steering} to determine the optimal pathway by which a…

Geometric Topology · Mathematics 2025-04-18 Agnese Barbensi , Alexander R. Klotz , Dimos Gkountaroulis

Conjugated organic molecules play a central role in a wide range of optoelectronic devices, including organic light-emitting diodes, organic field-effect transistors, and organic solar cells. A major bottleneck in the computational design…

Coarse-grained (CG) models parameterized using atomistic reference data, i.e., 'bottom up' CG models, have proven useful in the study of biomolecules and other soft matter. However, the construction of highly accurate, low resolution CG…

We introduce oxNA, a new model for the simulation of DNA-RNA hybrids which is based on two previously developed coarse-grained models$\unicode{x2014}$oxDNA and oxRNA. The model naturally reproduces the physical properties of hybrid duplexes…

Soft Condensed Matter · Physics 2024-03-19 Eryk J. Ratajczyk , Petr Šulc , Andrew J. Turberfield , Jonathan P. K. Doye , Ard A. Louis