The concept of atomic partial charge is important for simulating physical properties of compounds. Classical molecular dynamic simulations use force field models that depend on atomic partial charge parameters to model electrostatic interactions. The partial charges are typically fitted to quantum chemical electrostatic potentials with restraints, known as restrained electrostatic potential fitting (RESP). While RESP has seen broad application in the past 20 years, many unresolved challenges remain, including significant dependence of the fitted charges on many heuristic choices; for example, the deliberate use of low levels of theory for error cancellation and the high complexity of the fitting procedure all make it difficult to improve on the original implementation. As a first step in this project to develop the next generation of the ESP-based charge model, we developed an open-source software implementation of RESP in Python, called “<i>respyte</i>”. This new tool is capable of exactly reproducing the original implementation, and is also easily extensible for developing improved charge models that are more physically and numerically rigorous and have fewer dependencies on heuristic choices. We propose an improved normalization formula for the RESP design matrix to impose restraints that scale properly with molecular size or grid density, and study the effects of incorporating electric field terms in the charge fitting procedure.

Moving beyond restrained electrostatic potential fitting

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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