Hydration free energies from kernel-based machine learning: Compound-database bias
We consider the prediction of a basic thermodynamic property-hydration free energies-across a large subset of the chemical space of small organic molecules. Our in silico study is based on computer simulations at the atomistic level with implicit solvent. We report on a kernel-based machine learning approach that is inspired by recent work in learning electronic properties but differs in key aspects: The representation is averaged over several conformers to account for the statistical ensemble. We also include an atomic-decomposition ansatz, which offers significant added transferability compared to molecular learning. Finally, we explore the existence of severe biases from databases of experimental compounds. By performing a combination of dimensionality reduction and cross-learning models, we show that the rate of learning depends significantly on the breadth and variety of the training dataset. Our study highlights the dangers of fitting machine-learning models to databases of a narrow chemical range.
This study considers the prediction of a basic thermodynamic property-hydration free energies-across a large subset of the chemical space of small organic molecules and reports on a kernel-based machine learning approach that is inspired by recent work in learning electronic properties but differs in key aspects.
@article{Rauer_2020,
doi = {10.1063/5.0012230},
url = {https://doi.org/10.1063%2F5.0012230},
year = 2020,
month = {jul},
publisher = {{AIP} Publishing},
volume = {153},
number = {1},
author = {Clemens Rauer and Tristan Bereau},
title = {Hydration free energies from kernel-based machine learning: Compound-database bias},
journal = {The Journal of Chemical Physics}
}