Efficient Amino Acid Conformer Search with Bayesian Optimization.

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Quantum Chemistry

Background:

• Finding low-energy molecular conformers is computationally expensive.
• Accurate quantum chemical methods are needed for conformer structure and energy determination.
• High dimensionality of search space poses a challenge.

Purpose of the Study:

• To develop an efficient and accurate method for identifying low-energy molecular conformers.
• To reduce the computational cost associated with conformer searches.
• To validate the method's performance and transferability.

Main Methods:

• Integration of active-learning Bayesian optimization (BO) algorithms with quantum chemistry.
• Application to cysteine, serine, tryptophan, and aspartic acid.
• Utilizing single-point calculations and structure relaxations.

Main Results:

• Successfully identified low-energy conformers for cysteine with high accuracy.
• Achieved results in good agreement with experimental measurements and reference calculations.
• Demonstrated method's efficiency, requiring less than 10% of the computational cost of existing methods.
• Validated transferability across multiple amino acids.

Conclusions:

• The combined BO and quantum chemistry approach is efficient and accurate for conformer searches.
• This method offers a significant reduction in computational expense.
• The approach is transferable and reliable for various molecular systems.