Finding the lowest energy conformation of given molecule!

Generating low-energy molecular conformers is important for many areas of computational chemistry, molecular modeling and cheminformatics. Many tools have been developed to generate conformers, including BALLOON (1), Confab (2), FROG2 (3),  MOE (4), OMEGA (5) and RDKit (6). The search algorithm implemented in these tools can be broadly classified as either systematic or stochastic. These algorithms primarily focus on generating geometrically diverse low-energy conformers. Here, we are interested in finding lowest energy conformation of a molecule instead of achieving geometric diversity and Bayesian optimization is used to find the lowest energy conformation (7).

General Idea of Bayesian Optimization:
1. Construct a surrogate model to approximate the objective function
2. Sample useful points and update the model
3. Repeat Step 1-2 until stopping criteria is reached

Example

(a) Biphenyl in 2D; (b) True energy landscape (MMFF94);

(c) – (f) Bayesian Optimization with Gaussian Process — red points are the evaluated points. The blue line and the shaded region represent the mean function and express the uncertainty of the function respectively. Red curve at the bottom shows the acquisition function. 15 iterations and 5 initial random samples are used in this example. (c) First step; (d) Second step; (e) Third step; (f) Final step

In the final step, we have a good approximation of the energy landscape and we can obtain the lowest energy conformation easily.  We compared Bayesian optimization with uniform random search and Confab (systematic search) and the results are shown in (7).

Reference:

  1. Vainio, M.J., Johnson, M.S.: Generating conformer ensembles using a multiobjective genetic algorithm
  2. O’Boyle, N.M., Vandermeersch, T., Flynn, C.J., Maguire, A.R., Hutchison, G.R.: Confab – Systematic generation of diverse low-energy conformers
  3. Miteva, M.A., Guyon, F., Tuff ́ery, P.: Frog2: Efficient 3D conformation ensemble generator for small compounds.
  4. CCG: Molecular Operating Environment (MOE). Chemical Computing Group ULC (2018). http://www.chemcomp.com/
  5. Hawkins, P.C., Skillman, A.G., Warren, G.L., Ellingson, B.A., Stahl, M.T.: Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural database.
  6. RDKit: Open-Source Cheminformatics. Available at http://www.rdkit.org
  7. Lucian Chan, Geoffrey R. Hutchison, Garrett M. Morris   Bayesian Optimization for Conformer Generation    https://chemrxiv.org/articles/Bayesian_Optimization_for_Conformer_Generation/7228940

 

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