GaudiMM: A modular multi-objective platform for molecular modeling
| Autor: | Giuseppe Sciortino, Martí Municoy, Jaime Rodríguez-Guerra Pedregal, Jordi Guasp, Jean-Didier Maréchal |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Theoretical computer science Molecular model business.industry Computer science General Chemistry Molecular systems Modular design Multi-objective optimization 03 medical and health sciences Computational Mathematics ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Protein–ligand docking Molecular descriptor Genetic algorithm Data analysis Artificial intelligence business |
| Zdroj: | Journal of Computational Chemistry. 38:2118-2126 |
| ISSN: | 0192-8651 |
| DOI: | 10.1002/jcc.24847 |
| Popis: | 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. © 2017 Wiley Periodicals, Inc. |
| Databáze: | OpenAIRE |
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