title: Robustness in the fitting of molecular mechanics parameters.
abstract: Automated methods for force field parametrization have attracted renewed interest of the community, but the robustness issues associated with the often ill-conditioned nature of parameter optimization have been vastly underappreciated in the recent literature. For this reason, this article offers a detailed description of the origin and nature of these issues. This includes a discussion of the restrained electrostatic potential fit (RESP) charge model, which does contain explicit robustness-enhancing measures albeit not in the context of bonded parameters, and which forms an inspiration for the present work. It is also discussed how all the bonded parameters in a Class I force field can be simultaneously fit using the linear least squares (LLS) procedure, and a novel restraining strategy is presented that overcomes robustness issues in the LLS fitting of bonded parameters while minimally impacting the fitted values of well-behaved parameters. Two variants of this methodology are then validated through a number of case studies, including the fitting of bond-charge increments, which illustrates the method's potential for robustly solving general LLS problems beyond force field parametrization.
authors.affiliation: Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Maryland, 21201.
authors.forename: Kenno
authors.initials: K
authors.lastname: Vanommeslaeghe
authors.forename: Mingjun
authors.initials: M
authors.lastname: Yang
authors.forename: Alexander D
authors.initials: AD
authors.lastname: MacKerell
mesh_terms: D003198:Computer Simulation; D008956:Models, Chemical; D015394:Molecular Structure; D011789:Quantum Theory; D013816:Thermodynamics
keywords: CHARMM; empirical force fields; linear least squares; optimization; robustness
references.citation: J Comput Chem. 2004 Aug;25(11):1322-32
references.pmid: 15185325
references.citation: Biochim Biophys Acta. 2015 May;1850(5):861-871
references.pmid: 25149274
references.citation: J Comput Chem. 2010 Mar;31(4):671-90
references.pmid: 19575467
references.citation: J Chem Theory Comput. 2013 Jan 8;9(1):452-60
references.pmid: 26589047
references.citation: J Phys Chem B. 2015 Jan 22;119(3):637-52
references.pmid: 24564643
references.citation: J Comput Chem. 2009 Jul 30;30(10):1545-614
references.pmid: 19444816
references.citation: J Chem Inf Model. 2014 Jul 28;54(7):1978-86
references.pmid: 24960267
references.citation: J Chem Phys. 2005 May 22;122(20):204303
references.pmid: 15945720
references.citation: Curr Pharm Des. 2014;20(20):3281-92
references.pmid: 23947650
references.citation: J Chem Inf Model. 2012 Dec 21;52(12):3155-68
references.pmid: 23145473
references.citation: J Phys Chem B. 2009 Sep 17;113(37):12466-76
references.pmid: 19694450
references.citation: J Chem Theory Comput. 2013 Aug 13;9(8):
references.pmid: 24223528
references.citation: J Mol Model. 2008 Aug;14(8):667-79
references.pmid: 18458967
references.citation: J Comput Chem. 2013 Dec 15;34(32):2757-70
references.pmid: 24000174
references.citation: J Comput Chem. 2014 Jul 15;35(19):1438-45
references.pmid: 24831846
references.citation: J Comput Chem. 2008 Nov 30;29(15):2543-64
references.pmid: 18470966
references.citation: J Chem Theory Comput. 2009 Aug 20;5(9):2353-2370
references.pmid: 20161005
references.citation: J Chem Theory Comput. 2007 May;3(3):1120-33
references.pmid: 26627431