A focus on simulation and machine learning as complementary tools for chemical space navigation

Matteo Aldeghi; Connor W Coley

doi:10.1039/d2sc90130g

A focus on simulation and machine learning as complementary tools for chemical space navigation

Chem Sci. 2022 Jul 11;13(28):8221-8223. doi: 10.1039/d2sc90130g. eCollection 2022 Jul 20.

Authors

Matteo Aldeghi¹, Connor W Coley^{1

2}

Affiliations

¹ Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA maldeghi@google.com ccoley@mit.edu.
² Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology Cambridge MA 02139 USA.

Abstract

Computer-aided molecular design benefits from the integration of two complementary approaches: machine learning and first-principles simulation. Mohr et al. (B. Mohr, K. Shmilovich, I. S. Kleinwächter, D. Schneider, A. L. Ferguson and T. Bereau, Chem. Sci., 2022, 13, 4498-4511, https://pubs.rsc.org/en/content/articlelanding/2022/sc/d2sc00116k) demonstrated the discovery of a cardiolipin-selective molecule via the combination of coarse-grained molecular dynamics, alchemical free energy calculations, Bayesian optimization and interpretable regression to reveal design principles.