Identification of low energy conformers is important in the fields of drug discovery, combustion chemistry, and proteomics, to name a few. To address this, we present a conformer search tool that identifies the lowest energy conformer of molecules through two different optimization methods: a genetic algorithm and an evolutionary strategy. Both algorithms automatically create a random initial population, rank and select the fittest fraction of the population (the “parent” molecules), and create “children” molecules based on the dihedral angles of each possible torsion of the “parent” molecules. This process is iterated using an approximate but fast force-field calculator to rank the population, followed by a higher accuracy DFT optimization of the final geometry. To assess the efficacy of our tools, we perform conformer analysis on several well studied peptides and a common anti-cancer drug (Taxol). At the time of submission, we have performed our genetic algorithm and evolutionary strategies on glycine, alanine, valine, leucine, isoleucine, phenylalanine, and tryptophan and have close agreement with published low energy conformers.

Implementing genetic algorithms and evolutionary strategies in conformer analysis

256th National Meeting (2018)

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