Regensburg 2019 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 36: Condensed-matter simulations augmented by advanced statistical methodologies (joint session DY/CPP)

DY 36.5: Talk

Wednesday, April 3, 2019, 16:15–16:30, H20

Machine Learning of Free Energies — •Clemens Rauer and Tristan Bereau — Max Planck Institute for Polymer Science, Ackermannweg 10, 55128 Mainz, Germany

Free energies are important molecular properties which can provide an insight into the thermodynamic state of the respective system. Accurate calculations of free energies are an important tool for many biophysical applications, ranging from protein-ligand binding[1] to the insertion of small molecules into a lipid[2]. However, computationally expensive high level simulations are necessary in order to obtain accurate free energy estimates, and therefore, only a small subset of chemical space can be accurately covered. We overcome this problem by building a Δ-machine learning[3] model. Using this approach we can use a "cheap" low level method to predict free energies and learn the correction to a higher level method or experimental value. Then, we can predict high level free energies for significantly larger compound sets than was used in the training of the model. We show that by using only limited high level data, highly accurate free energies can be calculated using this method. As a first system we apply this approach to the prediction of hydration free energies.

[1] Mobley, D.L. & Gilson, M.K. Annu. Rev. Biophys. 2017, 46:531-58

[2] Menichetti, R. et al. J. Chem. Phys. 2017, 147, 125101

[3] Ramakrishnan et al. J. Chem. Theory Comput. 2015, 11, 2087-2096