First-principles molecular structure search with a genetic algorithm

TL;DR

This paper presents a genetic algorithm-based method for efficiently identifying low-energy molecular conformers using first-principles calculations, improving conformer sampling in computational chemistry.

Contribution

It introduces a novel conformer search algorithm that combines genetic algorithms with local optimization and blacklisting, enabling comprehensive conformer prediction within an energy window.

Findings

Effective in finding low-energy conformers compared to systematic and random methods.

Validated on amino acid dipeptides and a drug-like ligand with promising results.

Reduces computational cost by avoiding repeated evaluations of similar conformers.

Abstract

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.