Lian Duan, Kowit Hengphasatporn* and Yasuteru Shigeta,
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PaCS-Q: Python Toolkits for Path Sampling in MD and QM/MM MD Simulation
PaCS-Q is an open-source Python toolkit that simplifies QM/MM MD and MD simulations, making complex pathway sampling accessible and user-friendly. Seamlessly integrated with the AMBER MD suite, it automates QM/MM MD simulations using the parallel cascade selection (PaCS) algorithm, enabling efficient exploration of reaction pathways without predefined reaction coordinates. PaCS-Q supports both RMSD- and distance-based sampling, which is ideal for studying covalent reactions and ligand binding/unbinding events. A key feature is its ability to automatically generate QM input files for Gaussian and ORCA directly from representative structures, streamlining the transition from MD to quantum calculations. With built-in tools for structure analysis and energy profiling, PaCS-Q minimizes setup complexity and enhances reproducibility. Easy to install via pip and compatible with Unix-based systems, PaCS-Q offers a practical, versatile solution for researchers in computational chemistry and drug discovery, enabling advanced simulations with speed, accuracy, and minimal effort. The PaCS-Q Python toolkit publicly available at https://github.com/nyelidl/PaCS-Q/.
期刊介绍:
The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery.
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