GaudiMM: A modular multi-objective platform for molecular modeling

Jaime Rodríguez Guerra Pedregal, Giuseppe Sciortino, Jordi Guasp, Martí Municoy, Jean Didier Maréchal

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)

Abstract

© 2017 Wiley Periodicals, Inc. 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.
Original languageEnglish
Pages (from-to)2118-2126
JournalJournal of Computational Chemistry
Volume38
Issue number24
DOIs
Publication statusPublished - 1 Jan 2017

Keywords

  • Genetic algorithms
  • Metallopeptides
  • Molecular modeling
  • Multi-objective optimization
  • Protein-ligand docking

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