First-Principles Molecular Structure Search with a Genetic Algorithm.
The identification of low-energy conformers for a given molecule is a fundamental problem in computational chemistry and cheminformatics. We assess here a conformer search that employs a genetic algorithm for sampling the low-energy segment of the conformation space of molecules. The algorithm is designed to work with first-principles methods, facilitated by the incorporation of local optimization and blacklisting conformers to prevent repeated evaluations of very similar solutions. The aim of the search is not only to find the global minimum but to predict all conformers within an energy window above the global minimum. The performance of the search strategy is (i) evaluated for a reference data set extracted from a database with amino acid dipeptide conformers obtained by an extensive combined force field and first-principles search and (ii) compared to the performance of a systematic search and a random conformer generator for the example of a drug-like ligand with 43 atoms, 8 rotatable bonds, and 1 cis/trans bond.
Fritz-Haber-Institut der Max-Planck-Gesellschaft , 14195 Berlin, Germany.
Adriana
A
Supady
Fritz-Haber-Institut der Max-Planck-Gesellschaft , 14195 Berlin, Germany.|Department of Mechanical Engineering & Materials Science, Duke University , Durham, North Carolina 27708, United States.
Volker
V
Blum
Fritz-Haber-Institut der Max-Planck-Gesellschaft , 14195 Berlin, Germany.
Carsten
C
Baldauf
D000465:Algorithms; D004151:Dipeptides / Q000737:chemistry*; D015195:Drug Design; D055808:Drug Discovery; D008024:Ligands; D008958:Models, Molecular; D008968:Molecular Conformation; D009173:Mycophenolic Acid / Q000737:chemistry; D013816:Thermodynamics
D004151:Dipeptides; D008024:Ligands; D009173:Mycophenolic Acid