posted on 2016-03-30, 00:00authored byZhaomin Liu, Joshua Pottel, Moeed Shahamat, Anna Tomberg, Paul Labute, Nicolas Moitessier
Computational chemists
use structure-based drug design and molecular
dynamics of drug/protein complexes which require an accurate description
of the conformational space of drugs. Organic chemists use qualitative
chemical principles such as the effect of electronegativity on hyperconjugation,
the impact of steric clashes on stereochemical outcome of reactions,
and the consequence of resonance on the shape of molecules to rationalize
experimental observations. While computational chemists speak about
electron densities and molecular orbitals, organic chemists speak
about partial charges and localized molecular orbitals. Attempts to
reconcile these two parallel approaches such as programs for natural
bond orbitals and intrinsic atomic orbitals computing Lewis structures-like
orbitals and reaction mechanism have appeared. In the past, we have
shown that encoding and quantifying chemistry knowledge and qualitative
principles can lead to predictive methods. In the same vein, we thought
to understand the conformational behaviors of molecules and to encode
this knowledge back into a molecular mechanics tool computing conformational
potential energy and to develop an alternative to atom types and training
of force fields on large sets of molecules. Herein, we describe a
conceptually new approach to model torsion energies based on fundamental
chemistry principles. To demonstrate our approach, torsional energy
parameters were derived on-the-fly from atomic properties. When the
torsional energy terms implemented in GAFF, Parm@Frosst, and MMFF94
were substituted by our method, the accuracy of these force fields
to reproduce MP2-derived torsional energy profiles and their transferability
to a variety of functional groups and drug fragments were overall
improved. In addition, our method did not rely on atom types and consequently
did not suffer from poor automated atom type assignments.