Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation.

Area of Science:

• Computational chemistry
• Molecular modeling
• Drug discovery

Background:

• Conformation generation is critical for molecular modeling applications, requiring sampling of diverse molecular structures.
• Existing in silico methods for conformation generation vary in approach, including deterministic, non-deterministic, systemic, and stochastic strategies.
• Efficient and accurate conformation generation remains a key challenge in computational chemistry.

Purpose of the Study:

• To introduce Cyndi, an efficient conformation sampling method.
• To validate Cyndi's performance against existing conformation generation techniques.
• To assess Cyndi's utility for preparing large-scale conformer databases.

Main Methods:

• Cyndi utilizes a multi-objective evolution algorithm (MOEA) for conformation generation.
• The method encodes molecular conformations using dihedral torsions and applies evolutionary operations.
• Objectives include energy favorability, conformational diversity, and control over molecular extension.

Main Results:

• Cyndi achieved an average minimum RMSD of 0.864 Å to bioactive conformations across a test set of 329 molecules.
• The method demonstrated high-speed performance, averaging 0.49 ± 0.18 seconds per molecule.
• Cyndi outperformed four other conformer generators in reproducing bioactive conformations.

Conclusions:

• Cyndi, based on MOEA, is a highly efficient and accurate method for conformation generation.
• The tool generates geometrically diverse conformers and excels at reproducing bioactive conformations.
• Cyndi's speed and accuracy make it suitable for extensive conformational sampling and database preparation.