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Related papers: Coarse-Grained Simulation of DNA using LAMMPS

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Coarse-grained (CG) force field methods for molecular systems are a crucial tool to simulate large biological macromolecules and are therefore essential for characterisations of biomolecular systems. While state-of-the-art deep learning…

Deoxyribonucleic acid (DNA) has shown great promise in enabling computational applications, most notably in the fields of DNA digital data storage and DNA computing. Information is encoded as DNA strands, which will naturally bind in…

Machine Learning · Computer Science 2021-10-22 David Buterez

Large language models (LLMs) have revolutionized natural language processing and are increasingly applied to other sequential data types, including genetic sequences. However, adapting LLMs to genomics presents significant challenges.…

Machine Learning · Computer Science 2025-10-29 Qihao Duan , Bingding Huang , Zhenqiao Song , Irina Lehmann , Lei Gu , Roland Eils , Benjamin Wild

Short, partially complementary, single-stranded (ss)DNA strands can form nanostructures with a wide variety of shapes and mechanical properties. It is well known that semiflexible, linear dsDNA can undergo an isotropic to nematic (IN) phase…

Soft Condensed Matter · Physics 2024-03-07 Kit Gallagher , Jiaming Yu , David A. King , Ren Liu , Erika Eiser

In recent years, a few multiple-resolution modelling strategies have been proposed, in which functionally relevant parts of a biomolecule are described with atomistic resolution, while the remainder of the system is concurrently treated…

Soft Condensed Matter · Physics 2023-01-20 Raffaele Fiorentini , Thomas Tarenzi , Raffaello Potestio

Large Language Models (LLMs) demonstrate remarkable generalizability across diverse tasks, yet genomic foundation models (GFMs) still require separate finetuning for each downstream application, creating significant overhead as model sizes…

Genomics · Quantitative Biology 2025-02-07 Zehui Li , Vallijah Subasri , Yifei Shen , Dongsheng Li , Yiren Zhao , Guy-Bart Stan , Caihua Shan

Large Genomic Foundation Models have recently achieved remarkable results and in-vivo translation capabilities. However these models quickly grow to over a few Billion of parameters and are expensive to run when compute is limited. To…

Machine Learning · Computer Science 2026-04-13 Rasched Haidari , Sam Martin , Maxime Allard

Coarse-grained simulations of conjugated polymers have become a popular way of investigating the device physics of organic photovoltaics. While UV-Vis spectroscopy remains one of key experimental methods for the interrogation of these…

Disordered Systems and Neural Networks · Physics 2019-09-10 Lena Simine , Thomas C. Allen , Peter J. Rossky

Atomistic or ab-initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such…

With the rapid advancement of computational techniques, Molecular Dynamics (MD) simulations have emerged as powerful tools in biomedical research, enabling in-depth investigations of biological systems at the atomic level. Among the diverse…

Biomolecules · Quantitative Biology 2024-09-05 Reza Bozorgpour

Coarse-grained models have emerged as valuable tools to simulate long DNA molecules while maintaining computational efficiency. These models aim at preserving interactions among coarse-grained variables in a manner that mirrors the…

Soft Condensed Matter · Physics 2024-05-01 Wout Laeremans , Midas Segers , Aderik Voorspoels , Enrico Carlon , Jef Hooyberghs

Advances in natural language processing and large language models have sparked growing interest in modeling DNA, often referred to as the "language of life". However, DNA modeling poses unique challenges. First, it requires the ability to…

Due to their sequential nature, traditional DNA synthesis methods are expensive in terms of time and resources. They also fabricate multiple copies of the same strand, introducing redundancy. This redundancy can be leveraged to enhance the…

Information Theory · Computer Science 2025-10-29 Frederik Walter , Yonatan Yehezkeally

We establish, through coarse-grained computation, a connection between traditional, continuum numerical algorithms (initial value problems as well as fixed point algorithms) and atomistic simulations of the Larson model of micelle…

Soft Condensed Matter · Physics 2009-11-10 Dmitry I. Kopelevich , Athanassios Z. Panagiotopoulos , Ioannis G. Kevrekidis

Molecular Dynamics is an important tool for computational biologists, chemists, and materials scientists, consuming a sizable amount of supercomputing resources. Many of the investigated systems contain charged particles, which can only be…

Computational Engineering, Finance, and Science · Computer Science 2017-02-15 William McDoniel , Markus Höhnerbach , Rodrigo Canales , Ahmed E. Ismail , Paolo Bientinesi

This dissertation explores how deep generative models can advance the analysis of challenging biological problems by integrating domain knowledge with deep learning. It focuses on two areas: DNA reaction kinetics and cryogenic electron…

Machine Learning · Computer Science 2026-05-07 Chenwei Zhang

Understanding the three-dimensional (3D) structure and stability of DNA is fundamental for its biological function and the design of novel drugs. In this study, we introduce an improved coarse-grained (CG) model, incorporating a more…

Soft Condensed Matter · Physics 2025-01-22 Xunxun Wang , Ya-Zhou Shi

We study the behaviour of double-stranded RNA under twist and tension using oxRNA, a recently developed coarse-grained model of RNA. Introducing explicit salt-dependence into the model allows us to directly compare our results to data from…

Biomolecules · Quantitative Biology 2016-01-19 Christian Matek , Petr Šulc , Ferdinando Randisi , Jonathan P. K. Doye , Ard A. Louis

Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation. However, such CG procedure induces information losses, which makes…

Machine Learning · Computer Science 2022-06-20 Wujie Wang , Minkai Xu , Chen Cai , Benjamin Kurt Miller , Tess Smidt , Yusu Wang , Jian Tang , Rafael Gómez-Bombarelli

LAMMPS is a widely popular classical Molecular Dynamics package. It was designed for materials modeling but it is well prepared for simulations in Soft Matter. The use packages like LAMMPS has advantages and disadvantages. The main…

Soft Condensed Matter · Physics 2021-02-25 C. S. Dias