Development and Benchmarking of Open Force Field v1.0.0, the Parsley Small Molecule Force Field

We describe the structure and optimization of the Open Force Field 1.0.0 small molecule force field, code-named Parsley. Parsley uses the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism in which parameter types are assigned directly by chemical perception, in contrast to traditional atom type-based approaches. This method provides a natural means to incorporate increasingly diverse chemistry without needlessly increasing force field complexity. In this work, we present essentially a full optimization of the valence parameters in the force field. The optimization was carried out with the ForceBalance tool and was informed by reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These data were computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. Tests of the resulting force field against compounds and data types outside the training set show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields. <br>

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Development and Benchmarking of Open Force Field v1.0.0, the Parsley Small Molecule Force Field

10.26434/chemrxiv.13082561.v1 ◽

2020 ◽

Author(s):

Yudong Qiu ◽

Daniel Smith ◽

Simon Boothroyd ◽

Hyesu Jang ◽

Jeffrey Wagner ◽

...

Keyword(s):

Force Field ◽

Small Molecule ◽

Data Types ◽

Software Ecosystem ◽

Energy Profiles ◽

Torsion Potential ◽

Full Optimization ◽

General Force ◽

Chemical Perception ◽

Natural Means

We describe the structure and optimization of the Open Force Field 1.0.0 small molecule force field, code-named Parsley. Parsley uses the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism in which parameter types are assigned directly by chemical perception, in contrast to traditional atom type-based approaches. This method provides a natural means to incorporate increasingly diverse chemistry without needlessly increasing force field complexity. In this work, we present essentially a full optimization of the valence parameters in the force field. The optimization was carried out with the ForceBalance tool and was informed by reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These data were computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. Tests of the resulting force field against compounds and data types outside the training set show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields. <br>

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Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0.1

10.1101/286542 ◽

2018 ◽

Cited By ~ 8

Author(s):

David L. Mobley ◽

Caitlin C. Bannan ◽

Andrea Rizzi ◽

Christopher I. Bayly ◽

John D. Chodera ◽

...

Keyword(s):

Force Field ◽

Molecular Mechanics ◽

Small Molecule ◽

Force Fields ◽

Chemical Space ◽

Dielectric Constants ◽

Intermediate Step ◽

Amber Force Field ◽

Molecular Systems ◽

Chemical Perception

AbstractHere, we focus on testing and improving force fields for molecular modeling, which see widespread use in diverse areas of computational chemistry and biomolecular simulation. A key issue affecting the accuracy and transferrability of these force fields is the use of atom typing. Traditional approaches to defining molecular mechanics force fields must encode, within a discrete set of atom types, all information which will ever be needed about the chemical environment; parameters are then assigned by looking up combinations of these atom types in tables. This atom typing approach leads to a wide variety of problems such as inextensible atom-typing machinery, enormous difficulty in expanding parameters encoded by atom types, and unnecessarily proliferation of encoded parameters. Here, we describe a new approach to assigning parameters for molecular mechanics force fields based on the industry standard SMARTS chemical perception language (with extensions to identify specific atoms available in SMIRKS). In this approach, each force field term (bonds, angles, and torsions, and nonbonded interactions) features separate definitions assigned in a hierarchical manner without using atom types. We accomplish this using direct chemical perception, where parameters are assigned directly based on substructure queries operating on the molecule(s) being parameterized, thereby avoiding the intermediate step of assigning atom types — a step which can be considered indirect chemical perception. Direct chemical perception allows for substantial simplification of force fields, as well as additional generality in the substructure queries. This approach is applicable to a wide variety of (bio)molecular systems, and can greatly reduce the number of parameters needed to create a complete force field. Further flexibility can also be gained by allowing force field terms to be interpolated based on the assignment of fractional bond orders via the same procedure used to assign partial charges. As an example of the utility of this approach, we provide a minimalist small molecule force field derived from Merck’s parm@Frosst (an Amber parm99 descendant), in which a parameter definition file only ≈ 300 lines long can parameterize a large and diverse spectrum of pharmaceutically relevant small molecule chemical space. We benchmark this minimalist force field on the FreeSolv small molecule hydration free energy set and calculations of densities and dielectric constants from the ThermoML Archive, demonstrating that it achieves comparable accuracy to the Generalized Amber Force Field (GAFF) that consists of many thousands of parameters.

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Assessment of Amyloid Forming Tendency of Peptide Sequences from Amyloid Beta and Tau Proteins Using Force-Field, Semi-Empirical, and Density Functional Theory Calculations

International Journal of Molecular Sciences ◽

10.3390/ijms22063244 ◽

2021 ◽

Vol 22 (6) ◽

pp. 3244

Author(s):

Charuvaka Muvva ◽

Natarajan Arul Murugan ◽

Venkatesan Subramanian

Keyword(s):

Force Field ◽

Density Functional ◽

Amyloid Β ◽

Density Functional Theory Calculations ◽

Tau Proteins ◽

Functional Theory ◽

Energy Profiles ◽

Α Helix ◽

Semi Empirical ◽

Β Sheet

A wide variety of neurodegenerative diseases are characterized by the accumulation of protein aggregates in intraneuronal or extraneuronal brain regions. In Alzheimer’s disease (AD), the extracellular aggregates originate from amyloid-β proteins, while the intracellular aggregates are formed from microtubule-binding tau proteins. The amyloid forming peptide sequences in the amyloid-β peptides and tau proteins are responsible for aggregate formation. Experimental studies have until the date reported many of such amyloid forming peptide sequences in different proteins, however, there is still limited molecular level understanding about their tendency to form aggregates. In this study, we employed umbrella sampling simulations and subsequent electronic structure theory calculations in order to estimate the energy profiles for interconversion of the helix to β-sheet like secondary structures of sequences from amyloid-β protein (KLVFFA) and tau protein (QVEVKSEKLD and VQIVYKPVD). The study also included a poly-alanine sequence as a reference system. The calculated force-field based free energy profiles predicted a flat minimum for monomers of sequences from amyloid and tau proteins corresponding to an α-helix like secondary structure. For the parallel and anti-parallel dimer of KLVFFA, double well potentials were obtained with the minima corresponding to α-helix and β-sheet like secondary structures. A similar double well-like potential has been found for dimeric forms for the sequences from tau fibril. Complementary semi-empirical and density functional theory calculations displayed similar trends, validating the force-field based free energy profiles obtained for these systems.

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Extension of the CHARMM general force field to sulfonyl-containing compounds and its utility in biomolecular simulations

Journal of Computational Chemistry ◽

10.1002/jcc.23067 ◽

2012 ◽

Vol 33 (31) ◽

pp. 2451-2468 ◽

Cited By ~ 321

Author(s):

Wenbo Yu ◽

Xibing He ◽

Kenno Vanommeslaeghe ◽

Alexander D. MacKerell

Keyword(s):

Force Field ◽

Biomolecular Simulations ◽

General Force

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Molecular Dynamics Simulation of Membrane Free Energy Profiles Using Accurate Force Field for Ionic Liquids

Scientific Computing and Algorithms in Industrial Simulations ◽

10.1007/978-3-319-62458-7_14 ◽

2017 ◽

pp. 265-284

Author(s):

Thorsten Köddermann ◽

Martin R. Schenk ◽

Marco Hülsmann ◽

Andreas Krämer ◽

Karl N. Kirschner ◽

...

Keyword(s):

Molecular Dynamics ◽

Free Energy ◽

Ionic Liquids ◽

Molecular Dynamics Simulation ◽

Force Field ◽

Dynamics Simulation ◽

Energy Profiles ◽

Free Energy Profiles

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A New Big-Data Paradigm for Target Identification and Drug Discovery

10.1101/134973 ◽

2017 ◽

Cited By ~ 9

Author(s):

Neel S. Madhukar ◽

Prashant K. Khade ◽

Linda Huang ◽

Kaitlyn Gayvert ◽

Giuseppe Galletti ◽

...

Keyword(s):

Drug Discovery ◽

Small Molecules ◽

Clinical Application ◽

Small Molecule ◽

Target Identification ◽

Clinical Development ◽

Data Types ◽

Public Data ◽

Drug Target Identification ◽

Approved Drugs

AbstractDrug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules – substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated ∼4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 – an active anti- cancer small molecule in clinical development – whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

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