Conformational Coverage by a Genetic Algorithm

A new approach for coverage of the conformational space by a limited number of conformers is proposed. Instead of using a systematic search whose time complexity increases exponentially with degrees of freedom, a genetic algorithm (GA) is employed to minimize 3D similarity among the conformers generated. This makes the problem computationally feasible even for large, flexible molecules. The 3D similarity of a pair of conformers is assumed to be reciprocal to the root-mean-square (rms) distance between identical atomic sites in an alignment providing its minimum. Thus, in contrast to traditional GA, the fitness of a conformer is not quantified individually but only in conjunction with the population it belongs to. The approach handles the following stereochemical and conformational degrees of freedom: rotation around acyclic single and double bonds, inversion of stereocenters, flip of free corners in saturated rings, and reflection of pyramids on the junction of two or three saturated rings. The latter two were particularly introduced to encompass the structural diversity of polycyclic structures. However, they generally affect valence angles and can be restricted up to a certain level of severity of such changes. Stereochemical modifications are totally/selectively disabled when the stereochemistry is exactly/partially specified on input. Three quality criteria, namely robustness, reproducibility, and coverage of the conformational space, are used to assess the performance of various GA experimental settings employed on four molecules with different numbers of conformational degrees of freedom. It was found that with the increase of the ratio between the number of parents and children, the reproducibility of GA runs increases whereas their robustness and coverage decrease. Force field optimization of conformers for each generation was found to improve significantly the reproducibility of results, at the cost of worse conformational coverage.