Force field development phase II: Relaxation of physics-based criteria… or inclusion of more rigorous physics into the representation of molecular energetics

Hagler, A. T.

doi:10.1007/s10822-018-0134-x

Cited by 66 publications

(84 citation statements)

References 287 publications

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“…Therefore, we sought to analyze these subsets in detail with the goal of aiding the development of more accurate methods. [31][32] We began with the arylation of 1,1-disubstituted alkenols (Scheme 3). Our previous studies have shown that the enantioselectivity of the migratory insertion is dictated primarily by the steric difference between the cis-oriented alkyl and hydrogen substituents on the alkene.…”

Section: Validation To Experimental Selectivitiesmentioning

confidence: 99%

Transition State Force Field for the Asymmetric Redox-Relay Heck Reaction

et al. 2020

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A transition state force field (TSFF) was developed using the quantum-guided molecular mechanics (Q2MM) method to describe the stereodetermining migratory insertion step of the enantioselective redox-relay Heck reaction for a range of multisubstituted alkenes. We show that the TSFF is highly predictive through an external validation of the TSFF against 151 experimentally determined stereoselectivities resulting in an R 2 of 0.89 and MUE of 1.8 kJ/mol. In addition, limitations in the underlying force field were identified by comparison of the TSFF results to DFT level calculations. A novel application of the TSFF was demonstrated for 31 cases where the enantiomer predicted by the TSFF differed from the originally published values. Experimental determination of the absolute configuration demonstrated that the computational predictions were accurate, suggesting that TSFFs can be used for the rapid prediction of the absolute stereochemistry for a class of reactions. Finally, a virtual ligand screen was conducted utilizing both the TSFF and a simple molecular correlation method. Both methods were similarly predictive, but the TSFF was able to show greater utility through transferability, speed, and interpretability.

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Section: Validation To Experimental Selectivitiesmentioning

confidence: 99%

Transition State Force Field for the Asymmetric Redox-Relay Heck Reaction

et al. 2020

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“…The interaction energy between atom and is expressed as please read our previous publications. [1][2][3] In the current implementation, we loop over each bond (two atoms are involved) and angle (three atoms are involved) in the system to derive the second term (Equation S4) in the following. Finally, a term related to the Ewald self-energy is also derived.…”

Section: Derivation Of Chain Rule Terms Due To Charge Fluxmentioning

confidence: 99%

Implementation of Geometry-Dependent Charge Flux into the Polarizable AMOEBA+ Potential

Liu

Piquemal

Ren

2019

J. Phys. Chem. Lett.

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Molecular dynamics (MD) simulations employing classical force fields (FFs) have been widely used to model molecular systems. The important ingredient of the current FFs, atomic charge, remains fixed during MD simulations despite the atomic environment or local geometry changes. This approximation hinders the transferability of the potential being used in multiple phases. Here we implement a geometry dependent charge flux (GDCF) model into the multipole-based AMOEBA+ polarizable potential. The CF in the current work explicitly depends on the local geometry (bond and angle) of the molecule. To our knowledge, this is the first study that derives energy and force expressions due to GDCF in a multipole-based polarizable FF framework. Due to the inclusion of GDCF, the AMOEBA+ water model is noticeably improved in terms of describing the monomer properties, cluster binding/interaction energy and a variety of liquid properties, including the infrared spectra that previous flexible water models were not able to capture. File list (2) download file view on ChemRxiv AMOEBA+CF_Manuscript.pdf (1.22 MiB) download file view on ChemRxiv AMOEBA+CF_SI.pdf (0.91 MiB)

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“…Surpassing 1 kcal mol −1 accuracy requires model improvements that are difficult to generalize Relative free energy methods almost universally utilize fixed-charge MM force fields to model small, organic, drug-like molecules and interactions with their respective receptors and aqueous environments, such as GAFF [13,14], CGenFF [15,16,16], or OPLS [17]. Importantly, these popular class I [18,19] MM force fields have well-characterized drawbacks, in part, because they omit a number of important energetic contributions known to limit their ability to achieve chemical accuracy [7,20,21]. For example, while moving to more complex electrostatics models which include fixed multipoles and polarizable dipoles [22] are promising, the development of polarizable force fields that broadly deliver accuracy gains has proven challenging [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…Even so, the environment-dependent coupling between torsions and other valence degrees of freedom [7,20,21,28] (including adjacent torsions [26,[29][30][31][32]) makes it difficult for this simple refitting approach to accurately capture often significant ligand conformational reorganization effects [33].…”

Section: Introductionmentioning

confidence: 99%

Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials

Rufa

Macdonald

Fass

et al. 2020

Preprint

106

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Alchemical free energy methods with molecular mechanics (MM) force fields are now widely used in the prioritization of small molecules for synthesis in structure-enabled drug discovery projects because of their ability to deliver 1–2 kcal mol−1 accuracy in well-behaved protein-ligand systems. Surpassing this accuracy limit would significantly reduce the number of compounds that must be synthesized to achieve desired potencies and selectivities in drug design campaigns. However, MM force fields pose a challenge to achieving higher accuracy due to their inability to capture the intricate atomic interactions of the physical systems they model. A major limitation is the accuracy with which ligand intramolecular energetics—especially torsions—can be modeled, as poor modeling of torsional profiles and coupling with other valence degrees of freedom can have a significant impact on binding free energies. Here, we demonstrate how a new generation of hybrid machine learning / molecular mechanics (ML/MM) potentials can deliver significant accuracy improvements in modeling protein-ligand binding affinities. Using a nonequilibrium perturbation approach, we can correct a standard, GPU-accelerated MM alchemical free energy calculation in a simple post-processing step to efficiently recover ML/MM free energies and deliver a significant accuracy improvement with small additional computational effort. To demonstrate the utility of ML/MM free energy calculations, we apply this approach to a benchmark system for predicting kinase:inhibitor binding affinities—a congeneric ligand series for non-receptor tyrosine kinase TYK2 (Tyk2)—wherein state-of-the-art MM free energy calculations (with OPLS2.1) achieve inaccuracies of 0.93±0.12 kcal mol−1 in predicting absolute binding free energies. Applying an ML/MM hybrid potential based on the ANI2x ML model and AMBER14SB/TIP3P with the OpenFF 1.0.0 (“Parsley”) small molecule force field as an MM model, we show that it is possible to significantly reduce the error in absolute binding free energies from 0.97 [95% CI: 0.68, 1.21] kcal mol−1 (MM) to 0.47 [95% CI: 0.31, 0.63] kcal mol−1 (ML/MM).

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Force field development phase II: Relaxation of physics-based criteria… or inclusion of more rigorous physics into the representation of molecular energetics

Cited by 66 publications

References 287 publications

Transition State Force Field for the Asymmetric Redox-Relay Heck Reaction

Transition State Force Field for the Asymmetric Redox-Relay Heck Reaction

Implementation of Geometry-Dependent Charge Flux into the Polarizable AMOEBA+ Potential

Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials

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