Journal
JOURNAL OF COMPUTATIONAL CHEMISTRY
Volume 38, Issue 24, Pages 2118-2126Publisher
WILEY
DOI: 10.1002/jcc.24847
Keywords
molecular modeling; protein-ligand docking; multi-objective optimization; genetic algorithms; metallopeptides
Categories
Funding
- Spanish MINECO [CTQ2014-54071-P]
- Generalitat de Catalunya [2014SGR989]
- UAB
Ask authors/readers for more resources
GaudiMM (for Genetic Algorithms with Unrestricted Descriptors for Intuitive Molecular Modeling) is here presented as a modular platform for rapid 3D sketching of molecular systems. It combines a Multi-Objective Genetic Algorithm with diverse molecular descriptors to overcome the difficulty of generating candidate models for systems with scarce structural data. Its grounds consist in transforming any molecular descriptor (i.e. those generally used for analysis of data) as a guiding objective for PES explorations. The platform is written in Python with flexibility in mind: the user can choose which descriptors to use for each problem and is even encouraged to code custom ones. Illustrative cases of its potential applications are included to demonstrate the flexibility of this approach, including metal coordination of multidentate ligands, peptide folding, and protein-ligand docking. GaudiMM is available free of charge from https://github.com/insilichem/gaudi. (C) 2017 Wiley Periodicals, Inc.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available