| Literature DB >> 28605037 |
Jaime Rodríguez-Guerra Pedregal1, Giuseppe Sciortino1, Jordi Guasp1, Martí Municoy1, Jean-Didier Maréchal1.
Abstract
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.Entities:
Keywords: genetic algorithms; metallopeptides; molecular modeling; multi-objective optimization; protein-ligand docking
Year: 2017 PMID: 28605037 DOI: 10.1002/jcc.24847
Source DB: PubMed Journal: J Comput Chem ISSN: 0192-8651 Impact factor: 3.376