Topology Automated Force-Field Interactions (TAFFI): A Framework for Developing Transferable Force Fields

Force-field development has undergone a revolution in the past decade with the proliferation of quantum chemistry based parametrizations and the introduction of machine learning approximations of the atomistic potential energy surface. Nevertheless, transferable force fields with broad coverage of organic chemical space remain necessary for applications in materials and chemical discovery where throughput, consistency, and computational cost are paramount. Here, we introduce a force-field development framework called Topology Automated Force-Field Interactions (TAFFI) for developing transferable force fields of varying complexity against an extensible database of quantum chemistry calculations. TAFFI formalizes the concept of atom typing and makes it the basis for generating systematic training data that maintains a one-to-one correspondence with force-field terms. This feature makes TAFFI arbitrarily extensible to new chemistries while maintaining internal consistency and transferability. As a demonstration of TAFFI, we have developed a fixed-charge force-field, TAFFI-gen, from scratch that includes coverage for common organic functional groups that is comparable to established transferable force fields. The performance of TAFFI-gen was benchmarked against OPLS and GAFF for reproducing several experimental properties of 87 organic liquids. The consistent performance of these force fields, despite their distinct origins, validates the TAFFI framework while also providing evidence of the representability limitations of fixed-charge force fields.

[1]  Sereina Riniker,et al.  Fixed-Charge Atomistic Force Fields for Molecular Dynamics Simulations in the Condensed Phase: An Overview , 2018, J. Chem. Inf. Model..

[2]  K-R Müller,et al.  SchNet - A deep learning architecture for molecules and materials. , 2017, The Journal of chemical physics.

[3]  Klaus Schulten,et al.  Rapid parameterization of small molecules using the force field toolkit , 2013, J. Comput. Chem..

[4]  Lili Wang,et al.  A polarizable ellipsoidal force field for halogen bonds , 2013, J. Comput. Chem..

[5]  D. Case,et al.  Further along the Road Less Traveled: AMBER ff15ipq, an Original Protein Force Field Built on a Self-Consistent Physical Model , 2016, Journal of chemical theory and computation.

[6]  Robert Abel,et al.  OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules. , 2019, Journal of chemical theory and computation.

[7]  J. Šponer,et al.  W-RESP: Well-Restrained Electrostatic Potential-Derived Charges. Revisiting the Charge Derivation Model. , 2021, Journal of chemical theory and computation.

[8]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[9]  W. Goddard,et al.  UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations , 1992 .

[10]  Pascal T. Merz,et al.  A GROMOS-Compatible Force Field for Small Organic Molecules in the Condensed Phase: The 2016H66 Parameter Set. , 2016, Journal of chemical theory and computation.

[11]  Pavel Hobza,et al.  On Extension of the Current Biomolecular Empirical Force Field for the Description of Halogen Bonds. , 2012, Journal of chemical theory and computation.

[12]  Mahmoud A. A. Ibrahim,et al.  Molecular mechanical study of halogen bonding in drug discovery , 2011, J. Comput. Chem..

[13]  William L. Jorgensen,et al.  OPLS ALL-ATOM MODEL FOR AMINES : RESOLUTION OF THE AMINE HYDRATION PROBLEM , 1999 .

[14]  Teresa Head-Gordon,et al.  New developments in force fields for biomolecular simulations. , 2018, Current opinion in structural biology.

[15]  Alexandre Tkatchenko,et al.  Quantum-chemical insights from deep tensor neural networks , 2016, Nature Communications.

[16]  Jennifer L. Knight,et al.  OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins. , 2016, Journal of chemical theory and computation.

[17]  Alexander D. MacKerell,et al.  Force field development and simulations of intrinsically disordered proteins. , 2018, Current opinion in structural biology.

[18]  Yi Yao,et al.  Free Energy Profile of NaCl in Water: First-Principles Molecular Dynamics with SCAN and ωB97X-V Exchange-Correlation Functionals. , 2018, Journal of chemical theory and computation.

[19]  Jesse G. McDaniel,et al.  Conformational and Dynamic Properties of Poly(ethylene oxide) in an Ionic Liquid: Development and Implementation of a First-Principles Force Field. , 2016, The journal of physical chemistry. B.

[20]  Qiyuan Zhao,et al.  A Self-Consistent Component Increment Theory for Predicting Enthalpy of Formation. , 2020, Journal of chemical information and modeling.

[21]  R. Friesner,et al.  Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides† , 2001 .

[22]  Alexander D. MacKerell,et al.  All-atom empirical potential for molecular modeling and dynamics studies of proteins. , 1998, The journal of physical chemistry. B.

[23]  Alexander D. MacKerell,et al.  CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields , 2009, J. Comput. Chem..

[24]  Woody Sherman,et al.  Improving the Prediction of Absolute Solvation Free Energies Using the Next Generation OPLS Force Field. , 2012, Journal of chemical theory and computation.

[25]  W. L. Jorgensen,et al.  Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids , 1996 .

[26]  E. Izgorodina,et al.  A systematic study of DFT performance for geometry optimisations of ionic liquid clusters. , 2020, Journal of chemical theory and computation.

[27]  Timothy Clark,et al.  Halogen bonding: the σ-hole , 2007 .

[28]  Jeng-Da Chai,et al.  Long-Range Corrected Hybrid Density Functionals with Improved Dispersion Corrections. , 2012, Journal of chemical theory and computation.

[29]  Alexander D. MacKerell,et al.  Automation of the CHARMM General Force Field (CGenFF) II: Assignment of Bonded Parameters and Partial Atomic Charges , 2012, J. Chem. Inf. Model..

[30]  S. Grimme,et al.  A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. , 2010, The Journal of chemical physics.

[31]  R. Dror,et al.  Improved side-chain torsion potentials for the Amber ff99SB protein force field , 2010, Proteins.

[32]  Alireza Khorshidi,et al.  Amp: A modular approach to machine learning in atomistic simulations , 2016, Comput. Phys. Commun..

[33]  Christian Kramer,et al.  Charge Anisotropy: Where Atomic Multipoles Matter Most. , 2014, Journal of chemical theory and computation.

[34]  Wei Zhang,et al.  A point‐charge force field for molecular mechanics simulations of proteins based on condensed‐phase quantum mechanical calculations , 2003, J. Comput. Chem..

[35]  C. Simmerling,et al.  ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. , 2015, Journal of chemical theory and computation.

[36]  D. Mobley,et al.  Non-bonded force field model with advanced restrained electrostatic potential charges (RESP2) , 2020, Communications Chemistry.

[37]  Carl Caleman,et al.  Force Field Benchmark of Organic Liquids: Density, Enthalpy of Vaporization, Heat Capacities, Surface Tension, Isothermal Compressibility, Volumetric Expansion Coefficient, and Dielectric Constant , 2011, Journal of chemical theory and computation.

[38]  Frank Neese,et al.  The ORCA program system , 2012 .

[39]  W. L. Jorgensen,et al.  The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. , 1988, Journal of the American Chemical Society.

[40]  Jesse G. McDaniel,et al.  Physically-motivated force fields from symmetry-adapted perturbation theory. , 2013, The journal of physical chemistry. A.

[41]  A. V. Duin,et al.  Reactive Potentials for Advanced Atomistic Simulations , 2013 .

[42]  Hans W. Horn,et al.  Fully optimized contracted Gaussian basis sets for atoms Li to Kr , 1992 .

[43]  Junmei Wang,et al.  Application of Molecular Dynamics Simulations in Molecular Property Prediction I: Density and Heat of Vaporization. , 2011, Journal of chemical theory and computation.

[44]  P. Kollman,et al.  A well-behaved electrostatic potential-based method using charge restraints for deriving atomic char , 1993 .

[45]  C. Breneman,et al.  Determining atom‐centered monopoles from molecular electrostatic potentials. The need for high sampling density in formamide conformational analysis , 1990 .

[46]  Brett M Savoie,et al.  Evidence of information limitations in coarse-grained models. , 2019, The Journal of chemical physics.

[47]  Justin A. Lemkul,et al.  Parametrization of halogen bonds in the CHARMM general force field: Improved treatment of ligand-protein interactions. , 2016, Bioorganic & medicinal chemistry.

[48]  V. Hornak,et al.  Comparison of multiple Amber force fields and development of improved protein backbone parameters , 2006, Proteins.

[49]  Ivo Cacelli,et al.  Parametrization and Validation of Intramolecular Force Fields Derived from DFT Calculations. , 2007, Journal of chemical theory and computation.

[50]  P. Kollman,et al.  A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules , 1995 .

[51]  B. L. de Groot,et al.  CHARMM36m: an improved force field for folded and intrinsically disordered proteins , 2016, Nature Methods.

[52]  Huai Sun,et al.  COMPASS II: extended coverage for polymer and drug-like molecule databases , 2016, Journal of Molecular Modeling.

[53]  R. T. Sanderson.  Electronegativity and bond energy , 1983 .

[54]  Szczepan Roszak,et al.  New theoretical insight into the interactions and properties of formic acid: development of a quantum-based pair potential for formic acid. , 2005, The Journal of chemical physics.

[55]  A. Rappé,et al.  Scalable Anisotropic Shape and Electrostatic Models for Biological Bromine Halogen Bonds. , 2012, Journal of chemical theory and computation.

[56]  Alexander D. MacKerell,et al.  Extending the treatment of backbone energetics in protein force fields: Limitations of gas‐phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations , 2004, J. Comput. Chem..

[57]  Steve Plimpton,et al.  Fast parallel algorithms for short-range molecular dynamics , 1993 .

[58]  Daniel J Cole,et al.  QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics , 2018, J. Chem. Inf. Model..

[59]  Maurizio Sironi,et al.  Halogen bonding in ligand-receptor systems in the framework of classical force fields. , 2011, Physical chemistry chemical physics : PCCP.

[60]  Kent R. Wilson,et al.  Thermodynamics and quantum corrections from molecular dynamics for liquid water , 1982 .

[61]  P. Hünenberger,et al.  Evaluating Classical Force Fields against Experimental Cross-Solvation Free Energies. , 2020, Journal of chemical theory and computation.

[62]  Jean-Philip Piquemal,et al.  Modeling Organochlorine Compounds and the σ-Hole Effect Using a Polarizable Multipole Force Field , 2014, The journal of physical chemistry. B.

[63]  David L Mobley,et al.  Escaping Atom Types in Force Fields Using Direct Chemical Perception. , 2018, Journal of chemical theory and computation.

[64]  David L Mobley,et al.  Small molecule hydration free energies in explicit solvent: An extensive test of fixed-charge atomistic simulations. , 2009, Journal of chemical theory and computation.

[65]  John M Herbert,et al.  Accurate description of intermolecular interactions involving ions using symmetry-adapted perturbation theory. , 2015, Journal of chemical theory and computation.

[66]  M. Klein,et al.  Ab initio theory and modeling of water , 2017, Proceedings of the National Academy of Sciences.

[67]  J. Murray,et al.  σ-hole bonding: molecules containing group VI atoms , 2007 .

[68]  D. van der Spoel,et al.  Theoretical Infrared Spectra: Quantitative Similarity Measures and Force Fields , 2020, Journal of chemical theory and computation.

[69]  O. Acevedo,et al.  Revisiting OPLS Force Field Parameters for Ionic Liquid Simulations. , 2017, Journal of chemical theory and computation.

[70]  Alexander D. MacKerell,et al.  An Empirical Polarizable Force Field Based on the Classical Drude Oscillator Model: Development History and Recent Applications , 2016, Chemical reviews.

[71]  Christian Kramer,et al.  Leveraging Symmetries of Static Atomic Multipole Electrostatics in Molecular Dynamics Simulations. , 2013, Journal of chemical theory and computation.

[72]  Alexander D. MacKerell,et al.  Determination of Electrostatic Parameters for a Polarizable Force Field Based on the Classical Drude Oscillator. , 2005, Journal of chemical theory and computation.

[73]  Zhe Shen,et al.  Hierarchical atom type definitions and extensible all‐atom force fields , 2016, J. Comput. Chem..

[74]  K. Müller,et al.  Towards exact molecular dynamics simulations with machine-learned force fields , 2018, Nature Communications.

[75]  Nongnuch Artrith,et al.  An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2 , 2016 .

[76]  David L. Mobley,et al.  A Fixed-Charge Model for Alcohol Polarization in the Condensed Phase, and Its Role in Small Molecule Hydration , 2014, The journal of physical chemistry. B.

[77]  Alexander D. MacKerell,et al.  Polarizable Empirical Force Field for Halogen-Containing Compounds Based on the Classical Drude Oscillator. , 2018, Journal of chemical theory and computation.

[78]  F. Weigend,et al.  Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy. , 2005, Physical chemistry chemical physics : PCCP.

[79]  Mark S. Gordon,et al.  Perspective: Ab initio force field methods derived from quantum mechanics , 2018 .

[80]  Patric Schyman,et al.  Treatment of Halogen Bonding in the OPLS-AA Force Field; Application to Potent Anti-HIV Agents. , 2012, Journal of chemical theory and computation.

[81]  Michael A Webb,et al.  Enhancing Cation Diffusion and Suppressing Anion Diffusion via Lewis-Acidic Polymer Electrolytes. , 2016, The journal of physical chemistry letters.

[82]  Stefan Grimme,et al.  Comprehensive Benchmark of Association (Free) Energies of Realistic Host-Guest Complexes. , 2015, Journal of chemical theory and computation.

[83]  Hans Hasse,et al.  Molecular model for formic acid adjusted to vapor–liquid equilibria , 2007 .

[84]  Alexander D. MacKerell,et al.  Understanding the dielectric properties of liquid amides from a polarizable force field. , 2008, The journal of physical chemistry. B.

[85]  Julian Tirado-Rives,et al.  Potential energy functions for atomic-level simulations of water and organic and biomolecular systems. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[86]  S. Grimme.  A General Quantum Mechanically Derived Force Field (QMDFF) for Molecules and Condensed Phase Simulations. , 2014, Journal of chemical theory and computation.

[87]  Alexander D. MacKerell,et al.  Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles. , 2012, Journal of chemical theory and computation.