PyL3dMD: Python LAMMPS 3D molecular descriptors package
Abstract Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-07-01
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| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13321-023-00737-5 |
| _version_ | 1827890235376140288 |
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| author | Pawan Panwar Quanpeng Yang Ashlie Martini |
| author_facet | Pawan Panwar Quanpeng Yang Ashlie Martini |
| author_sort | Pawan Panwar |
| collection | DOAJ |
| description | Abstract Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure–property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies. Graphical Abstract |
| first_indexed | 2024-03-12T21:06:47Z |
| format | Article |
| id | doaj.art-3b5b017811dc4478bd0411bf2489ebc0 |
| institution | Directory Open Access Journal |
| issn | 1758-2946 |
| language | English |
| last_indexed | 2024-03-12T21:06:47Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj.art-3b5b017811dc4478bd0411bf2489ebc02023-07-30T11:23:29ZengBMCJournal of Cheminformatics1758-29462023-07-0115111310.1186/s13321-023-00737-5PyL3dMD: Python LAMMPS 3D molecular descriptors packagePawan Panwar0Quanpeng Yang1Ashlie Martini2Department of Mechanical Engineering, University of California MercedDepartment of Mechanical Engineering, University of California MercedDepartment of Mechanical Engineering, University of California MercedAbstract Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure–property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies. Graphical Abstracthttps://doi.org/10.1186/s13321-023-00737-5LAMMPSMolecular descriptorQSPRCheminformaticsMD simulationsPython |
| spellingShingle | Pawan Panwar Quanpeng Yang Ashlie Martini PyL3dMD: Python LAMMPS 3D molecular descriptors package Journal of Cheminformatics LAMMPS Molecular descriptor QSPR Cheminformatics MD simulations Python |
| title | PyL3dMD: Python LAMMPS 3D molecular descriptors package |
| title_full | PyL3dMD: Python LAMMPS 3D molecular descriptors package |
| title_fullStr | PyL3dMD: Python LAMMPS 3D molecular descriptors package |
| title_full_unstemmed | PyL3dMD: Python LAMMPS 3D molecular descriptors package |
| title_short | PyL3dMD: Python LAMMPS 3D molecular descriptors package |
| title_sort | pyl3dmd python lammps 3d molecular descriptors package |
| topic | LAMMPS Molecular descriptor QSPR Cheminformatics MD simulations Python |
| url | https://doi.org/10.1186/s13321-023-00737-5 |
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