Treating Polarization Effects in Charged and Polar Bio-Molecules Through Variable Electrostatic Parameters

Abstract

Polarization plays important roles in charged and hydrogen bonding containing systems. Much effort ranging from the construction of physics-based models to quantum mechanism (QM)-based and machine learning (ML)-assisted models have been devoted to incorporating the polarization effect into the conventional force fields at different levels, such as atomic and coarse grained (CG). The application of polarizable force fields or polarization models was limited by two aspects, namely, computational cost and transferability. Different from physics-based models, no predetermining parameters were required in the QM-based approaches. Taking advantage of both the accuracy of QM calculations and efficiency of molecular mechanism (MM) and ML, polarization effects could be treated more efficiently while maintaining the QM accuracy. The computational cost could be reduced with variable electrostatic parameters, such as the charge, dipole, and electronic dielectric constant with the help of linear scaling fragmentation-based QM calculations and ML models. Polarization and entropy effects on the prediction of partition coefficient of druglike molecules are demonstrated by using both explicit or implicit all-atom molecular dynamics simulations and machine learning-assisted models. Directions and challenges for future development are also envisioned.

Machine Learning Prediction of Hydration Free Energy with Physically Inspired Descriptors

“…54,55 This work can be also extended to predict the solubility of biomolecules. Even for very large molecules, it is not a difficult task to obtain the values of physically inspired descriptors using polarizable force fields 56 or electronic structure calculation on fragments. 57 ■ ASSOCIATED CONTENT * sı Supporting Information…”

“…However, more samples with very negative HFEs are urgently required; advanced techniques such as active learning or a multifidelity strategy may be useful. , This work can be also extended to predict the solubility of biomolecules. Even for very large molecules, it is not a difficult task to obtain the values of physically inspired descriptors using polarizable force fields or electronic structure calculation on fragments …”

Streamlining and Optimizing Strategies of Electrostatic Parameterization

“…This drawback mainly comes from the absence of a polarization effect by surrounding molecules and omission of the higher-order multipoles. Consequently, many force fields have been proposed to include higher-order multipoles, 9,44,45 such as induced dipoles, 46 and by using multipole moment expansion. 44,47 Among the induced dipole models, AMOE-BA 48 and polarizable Gaussian multipole model (pGM) 49−51 are two of the models that can reproduce the nonadditive contributions of the electrostatic interactions.…”

“…5 Despite the limitations of partial charge derivation in systems containing lone-pairs, σ-holes, and π clouds, this remains an active field of research. 6−8 Approaches for partial charge derivation can be broadly divided into two categories 9 based on how the reference properties are estimated: the first class utilizes experimental data or nonquantum mechanical approaches, 10,11 while the second class relies on quantum mechanical (QM) estimation, such as the partition of the electron charge density or wave functions, 12−19 that reproduces charge-dependent properties, 20−22 interaction energies, 23,24 and electrostatic potentials (ESPs). 25−27 Different from partitioning electron densities that may incur the ambiguous depiction of molecular dipoles or higher-order moments, methods based on fitting ESPs are physically welldefined since ESP at a certain point is a QM observable and can be calculated readily.…”

“…The accurate description of electrostatic interactions is crucial for molecular simulations across various fields, including physical, chemical, and complex biological systems. − In classical force fields (FFs), electrostatic interactions are typically represented by fixed partial charges to achieve a balance between efficiency and accuracy, ignoring higher-order multipole effects . Despite the limitations of partial charge derivation in systems containing lone-pairs, σ-holes, and π clouds, this remains an active field of research. − Approaches for partial charge derivation can be broadly divided into two categories based on how the reference properties are estimated: the first class utilizes experimental data or nonquantum mechanical approaches, , while the second class relies on quantum mechanical (QM) estimation, such as the partition of the electron charge density or wave functions, − that reproduces charge-dependent properties, − interaction energies, , and electrostatic potentials (ESPs). − …”