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Abstract Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of the relevant underlying free energy landscapes. In this sense, software that can be seamlessly adapted to a broad range of complex systems is essential. Building on past efforts to provide open-source community-supported software for advanced sampling, we introduce PySAGES, a Python implementation of the Software Suite for Advanced General Ensemble Simulations (SSAGES) that provides full GPU support for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, or forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the management of a system’s configuration, the inclusion of new collective variables, and the implementation of sophisticated free energy-based sampling methods, the PySAGES library serves as a general platform for the development and implementation of emerging simulation techniques. The capabilities, core features, and computational performance of this tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems. We anticipate that PySAGES will provide the scientific community with a robust and easily accessible platform to accelerate simulations, improve sampling, and enable facile estimation of free energies for a wide range of materials and processes. 

ABSTRACT The physical organization of DNA within the nucleus is fundamental to a wide range of biological processes. The experimental investigation of the structure of genomic DNA remains challenging due to its large size and hierarchical arrangement. These challenges present considerable opportunities for combined experimental and modeling approaches. Physics‐based computational models, in particular, have emerged as essential tools for probing chromatin structure and dynamics across a wide range of length scales. Such models must necessarily be capable of bridging scales, and each scale presents its own subtleties and intricacies. This review discusses recent methodological advances in genomic structural modeling, emphasizing the need for multiscale integration to capture the hierarchical organization and molecular mechanisms that underlie chromatin structure and function. We present an analysis of state‐of‐the‐art methods, as well as a perspective on challenges and future opportunities across length scales ranging from bare DNA to nucleosomes and chromatin fibers, up to TAD and chromosome‐scale models. We emphasize models that connect genome organization to gene expression, models that leverage emerging machine learning capabilities, and models that develop multiscale approaches. We examine gaps in experimental data that computational models are poised to address and propose directions for future research that bridge theory and experiment in DNA structural biology.