A general graph neural network based implicit solvation model for organic molecules in water

Chemical Science, Journal Year: 2024, Volume and Issue: 15(28), P. 10794 - 10802

Published: Jan. 1, 2024

Novel approach combining graph neural network and the physically motivated functional form of an implicit solvent model enables description solvation effects with accuracy explicit simulations at a fraction time.

Language: Английский

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Benchmarking Quantum Mechanical Levels of Theory for Valence Parametrization in Force Fields

The Journal of Physical Chemistry B, Journal Year: 2024, Volume and Issue: 128(32), P. 7888 - 7902

Published: Aug. 1, 2024

A wide range of density functional methods and basis sets are available to derive the electronic structure properties molecules. Quantum mechanical calculations too computationally intensive for routine simulation molecules in condensed phase, prompting development efficient force fields based on quantum data. Parametrizing general fields, which cover a vast chemical space, necessitates generation sizable data with optimized geometries torsion scans. To achieve this efficiently, choosing method that balances computational cost accuracy is crucial. In study, we seek assess theory specific such as conformer energies energetics. comprehensively evaluate various methods, focus representative set 59 diverse small molecules, comparing approximately 25 combinations against reference level coupled cluster at complete limit.

Language: Английский

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Polarizable Water Model with Ab Initio Neural Network Dynamic Charges and Spontaneous Charge Transfer

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: March 29, 2025

Simulating water accurately has been a challenge due to the complexity of describing polarization and intermolecular charge transfer. Quantum mechanical (QM) electronic structures provide an accurate description in response local environments, which is nevertheless too expensive for large systems. In this study, we have developed polarizable model integrating Charge Model 5 atomic charges at level second-order Mo̷ller–Plesset perturbation theory, predicted by transferable neural network (ChargeNN) model. The spontaneous transfer explicitly accounted for, enabling precise treatment hydrogen bonds out-of-plane polarization. Our ChargeNN successfully reproduces various properties gas, liquid, solid phases. For example, correctly captures hydrogen-bond stretching peak bending-libration combination band, are absent spectra using fixed charges, highlighting significance Finally, molecular dynamical simulations liquid droplet with ∼4.5 nm radius reveal that strong interfacial electric fields concurrently induced partial collapse surface-to-interior study paves way QM-polarizable force fields, aiming large-scale high accuracy.

Language: Английский

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On the design space between molecular mechanics and machine learning force fields

Applied Physics Reviews, Journal Year: 2025, Volume and Issue: 12(2)

Published: April 2, 2025

A force field as accurate quantum mechanics (QMs) and fast molecular (MMs), with which one can simulate a biomolecular system efficiently enough meaningfully to get quantitative insights, is among the most ardent dreams of biophysicists—a dream, nevertheless, not be fulfilled any time soon. Machine learning fields (MLFFs) represent meaningful endeavor in this direction, where differentiable neural functions are parametrized fit ab initio energies forces through automatic differentiation. We argue that, now, utility MLFF models no longer bottlenecked by accuracy but primarily their speed, well stability generalizability—many recent variants, on limited chemical spaces, have long surpassed 1 kcal/mol—the empirical threshold beyond realistic predictions possible—though still magnitudes slower than MM. Hoping kindle exploration design faster, albeit perhaps slightly less MLFFs, review, we focus our attention technical space (the speed-accuracy trade-off) between MM ML fields. After brief review building blocks (from machine learning-centric point view) either kind, discuss desired properties challenges now faced development community, survey efforts make more envision what next generation might look like.

Language: Английский

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Parameterization of General Organic Polymers within the Open Force Field Framework

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(4), P. 1290 - 1305

Published: Feb. 2, 2024

Polymer and chemically modified biopolymer systems present unique challenges to traditional molecular simulation preparation workflows. First, typical polymer biomolecular input formats, such as Protein Data Bank (PDB) files, lack adequate chemical information needed for the parameterization of new chemistries. Second, polymers are typically too large accurate partial charge generation methods. In this work, we employ direct perception through Open Force Field toolkit create a flexible workflow organic polymers, encompassing everything from biopolymers soft materials. We propose test specification monomer that can, along with 3D conformational geometry, parametrize simulate most soft-material within same used smaller ligands. The format encompasses subset SMIRKS substructure query language uniquely identify repeating charges in underspecified matching atomic connectivity. This is combined several different approaches automatic partial-charge larger systems. As an initial proof concept, variety diverse polymeric were parametrized toolkit, including functionalized proteins, DNA, homopolymers, cross-linked systems, sugars. Additionally, shape properties radial distribution functions computed dynamics simulations poly(ethylene glycol), polyacrylamide, poly(N-isopropylacrylamide) homopolymers aqueous solution compared previous results order demonstrate start-to-finish property prediction. expect these tools will greatly expedite day-to-day computational research soft-matter robust atomic-scale conjunction existing structural notations.

Language: Английский

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Validating Small-Molecule Force Fields for Macrocyclic Compounds Using NMR Data in Different Solvents

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(20), P. 7938 - 7948

Published: Oct. 15, 2024

Macrocycles are a promising class of compounds as therapeutics for difficult drug targets due to favorable combination properties: They often exhibit improved binding affinity compared their linear counterparts reduced conformational flexibility, while still being able adapt environments different polarity. To assist in the rational design macrocyclic drugs, there is need computational methods that can accurately predict ensembles macrocycles environments. Molecular dynamics (MD) simulations remain one most accurate quantitatively, although accuracy governed by underlying force field. In this work, we benchmark four fields application performing replica exchange with solute tempering (REST2) 11 and comparing obtained nuclear Overhauser effect (NOE) upper distance bounds from NMR experiments. Especially, modern OpenFF 2.0 XFF yield good results, outperforming like GAFF2 OPLS/AA. We conclude REST2 produce compounds. However, also highlight examples which all examined fail fulfill experimental constraints.

Language: Английский

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DASH properties: Estimating atomic and molecular properties from a dynamic attention-based substructure hierarchy

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(7)

Published: Aug. 15, 2024

Recently, we presented a method to assign atomic partial charges based on the DASH (dynamic attention-based substructure hierarchy) tree with high efficiency and quantum mechanical (QM)-like accuracy. In addition, approach can be considered “rule based”—where rules are derived from attention values of graph neural network—and thus, each assignment is fully explainable by visualizing underlying molecular substructures. this work, demonstrate that these hierarchically sorted substructures capture key features local environment an atom allow us predict different properties accuracy without building new for property. The fast prediction in molecules can, example, used as efficient way generate feature vectors machine learning need expensive QM calculations. final well complete dataset wave functions made freely available.

Language: Английский

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lwreg: A Lightweight System for Chemical Registration and Data Storage

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(16), P. 6247 - 6252

Published: Aug. 8, 2024

Here, we present lwreg, a lightweight, yet flexible chemical registration system supporting the capture of both two-dimensional molecular structures (topologies) and three-dimensional conformers. lwreg is open source, with simple Python API, designed to be easily integrated into computational workflows. In addition itself, also introduce straightforward schema for storing experimental data metadata in database. This direct connection between compound structural information generated using those creates powerful tool analysis reproducibility. The software available at installable directly from https://github.com/rinikerlab/lightweight-registration.

Language: Английский

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