Hierarchical atom type definitions and extensible all-atom force fields

The Open Force Field Initiative: Open Software and Open Science for Molecular Modeling

Force fields are a key component of physics-based molecular modeling, describing the energies and forces in a molecular system as a function of the positions of the atoms and molecules involved. Here, we provide a review and scientific status report on the work of the Open Force Field (OpenFF) Initiative, which focuses on the science, infrastructure and data required to build the next generation of biomolecular force fields. We introduce the OpenFF Initiative and the related OpenFF Consortium, describe its approach to force field development and software, and discuss accomplishments to date as well as future plans. OpenFF releases both software and data under open and permissive licensing agreements to enable rapid application, validation, extension, and modification of its force fields and software tools. We discuss lessons learned to date in this new approach to force field development. We also highlight ways that other force field researchers can get involved, as well as some recent successes of outside researchers taking advantage of OpenFF tools and data.

Enhanced cisplatin chemotherapy sensitivity by self-assembled nanoparticles with Olaparib

Cisplatin (CDDP) is widely used as one kind of chemotherapy drugs in cancer treatment. It functions by interacting with DNA, leading to the DNA damage and subsequent cellular apoptosis. However, the presence of intracellular PARP1 diminishes the anticancer efficacy of CDDP by repairing DNA strands. Olaparib (OLA), a PARP inhibitor, enhances the accumulation of DNA damage by inhibiting its repair. Therefore, the combination of these two drugs enhances the sensitivity of CDDP chemotherapy, leading to improved therapeutic outcomes. Nevertheless, both drugs suffer from poor water solubility and limited tumor targeting capabilities. To address this challenge, we proposed the self-assembly of two drugs, CDDP and OLA, through hydrogen bonding to form stable and uniform nanoparticles. Self-assembled nanoparticles efficiently target tumor cells and selectively release CDDP and OLA within the acidic tumor microenvironment, capitalizing on their respective mechanisms of action for improved anticancer therapy. In vitro studies demonstrated that the CDDP-OLA NPs are significantly more effective than CDDP/OLA mixture and CDDP at penetrating cancer cells and suppressing their growth. In vivo studies revealed that the nanoparticles specifically accumulated at the tumor site and enhanced the therapeutic efficacy without obvious adverse effects. This approach holds great potential for enhancing the drugs' water solubility, tumor targeting, bioavailability, and synergistic anticancer effects while minimizing its toxic side effects.

Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation

Biomolecular simulations have become an essential tool in contemporary drug discovery, and molecular mechanics force fields (FFs) constitute its cornerstone. Developing a high quality and broad coverage general FF is a significant undertaking that requires substantial expert knowledge and computing resources, which is beyond the scope of general practitioners. Existing FFs originate from only a limited number of groups and organizations, and they either suffer from limited numbers of training sets, lower than desired quality because of oversimplified representations, or are costly for the molecular modeling community to access. To address these issues, in this work, we developed an AMBER-consistent small molecule FF with extensive chemical space coverage, and we provide Open Access parameters for the entire modeling community. To validate our FF, we carried out benchmarks of quantum mechanics (QM)/molecular mechanics conformer comparison and free energy perturbation calculations on several benchmark data sets. Our FF achieves a higher level of performance at reproducing QM energies and geometries than two popular open-source FFs, OpenFF2 and GAFF2. In relative binding free energy calculations for 31 protein-ligand data sets, comprising 1079 pairs of ligands, the new FF achieves an overall root-mean-square error of 1.19 kcal/mol for ΔΔG and 0.92 kcal/mol for ΔG on a subset of 463 ligands without bespoke fitting to the data sets. The results are on par with those of the leading commercial series of OPLS FFs.

Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands

The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.

Potential Energy Surfaces Sampled in Cremer-Pople Coordinates and Represented by Common Force Field Functionals for Small Cyclic Molecules

The complex conformations of the cyclic moieties impact the physical and chemical properties of molecules. In this work, we chose 22 molecules of four-, five-, and six-membered rings and performed a thorough conformational sampling using Cremer-Pople coordinates. With consideration of symmetries, we obtained a total of 1504 conformational structures for four-membered, 5576 for five-membered, and 13509 for six-membered rings. All well-known and many less well-known conformers for each molecule were identified. We represented the potential energy surfaces (PESs) by fitting the data to common analytical force field (FF) functional forms. We found that the general features of PESs can be described by the essential FF functional forms; however, the accuracy of representation can be improved remarkably by including the torsion-bond and torsion-angle coupling terms. The best fit yields R-squared (R²) values close to 1.0 and mean absolute errors in energy less than 0.3 kcal/mol.

Evidence That Less Can Be More for Transferable Force Fields

Graph-based parameter assignment has been the basis for developing transferable force fields for molecular dynamics simulations for decades. Nevertheless, transferable force fields vary in how specifically terms are defined with respect to the molecular graph and the procedures for generating parametrization data. More-specific force-field terms increase the complexity of the force field, theoretically increasing accuracy but also increasing training data requirements. In contrast, less-specific force fields can be reused across larger regions of chemical space, theoretically reducing accuracy but also reducing the number of parameters and training data requirements. Here, the tradeoffs between force-field specificity and accuracy are quantified by parametrizing three new sets of force fields with varying levels of graph specificity, using a shared procedure for generating training data. These force fields are benchmarked for their ability to reproduce the structural features and liquid properties of 87 organic molecules at 146 distinct state points. The overall accuracy for properties that were directly trained on rapidly saturates as the graph specificity of the force-field increases. From this, we conclude there is at best a marginal benefit of using less transferable and more complex force fields with common sources of quantum-chemically derived training data. When looking at properties unseen during training, there is some evidence that the more-complex force fields even perform slightly worse. These results are rationalized by the fortuitous regularization of force fields based on less-specific and more-transferable atom types. Both the saturation in the accuracy of training properties and the marginally worse performance on off-target properties fundamentally contradict the expectation that bespoke force fields are generally more accurate, given their larger number of parameters, and suggests that increasing force-field complexity should be carefully justified against performance gains and balanced against available training data.

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.

Toward Learned Chemical Perception of Force Field Typing Rules

Molecular mechanics force fields define how the energy and forces in a molecular system are computed from its atomic positions, thus enabling the study of such systems through computational methods like molecular dynamics and Monte Carlo simulations. Despite progress toward automated force field parametrization, considerable human expertise is required to develop or extend force fields. In particular, human input has long been required to define atom types, which encode chemically unique environments that determine which parameters will be assigned. However, relying on humans to establish atom types is suboptimal. Human-created atom types are often developed without statistical justification, leading to over- or under-fitting of data. Human-created types are also difficult to extend in a systematic and consistent manner when new chemistries must be modeled or new data becomes available. Finally, human effort is not scalable when force fields must be generated for new (bio)polymers, compound classes, or materials. To remedy these deficiencies, our long-term goal is to replace human specification of atom types with an automated approach, based on rigorous statistics and driven by experimental and/or quantum chemical reference data. In this work, we describe novel methods that automate the discovery of appropriate chemical perception: SMARTY allows for the creation of atom types, while SMIRKY goes further by automating the creation of fragment (nonbonded, bonds, angles, and torsions) types. These approaches enable the creation of move sets in atom or fragment type space, which are used within a Monte Carlo optimization approach. We demonstrate the power of these new methods by automating the rediscovery of human defined atom types (SMARTY) or fragment types (SMIRKY) in existing small molecule force fields. We assess these approaches using several molecular data sets, including one which covers a diverse subset of the DrugBank database.

Escaping Atom Types in Force Fields Using Direct Chemical Perception

Traditional approaches to specifying a molecular mechanics force field encode all the information needed to assign force field parameters to a given molecule into a discrete set of atom types. This is equivalent to a representation consisting of a molecular graph comprising a set of vertices, which represent atoms labeled by atom type, and unlabeled edges, which represent chemical bonds. Bond stretch, angle bend, and dihedral parameters are then assigned by looking up bonded pairs, triplets, and quartets of atom types in parameter tables to assign valence terms and using the atom types themselves to assign nonbonded parameters. This approach, which we call indirect chemical perception because it operates on the intermediate graph of atom-typed nodes, creates a number of technical problems. For example, atom types must be sufficiently complex to encode all necessary information about the molecular environment, making it difficult to extend force fields encoded this way. Atom typing also results in a proliferation of redundant parameters applied to chemically equivalent classes of valence terms, needlessly increasing force field complexity. Here, we describe a new approach to assigning force field parameters via direct chemical perception. Rather than working through the intermediary of the atom-typed graph, direct chemical perception operates directly on the unmodified chemical graph of the molecule to assign parameters. In particular, parameters are assigned to each type of force field term (e.g., bond stretch, angle bend, torsion, and Lennard-Jones) based on standard chemical substructure queries implemented via the industry-standard SMARTS chemical perception language, using SMIRKS extensions that permit labeling of specific atoms within a chemical pattern. We use this to implement a new force field format, called the SMIRKS Native Open Force Field (SMIRNOFF) format. We demonstrate the power and generality of this approach using examples of specific molecules that pose problems for indirect chemical perception and construct and validate a minimalist yet very general force field, SMIRNOFF99Frosst. We find that a parameter definition file only ∼300 lines long provides coverage of all but <0.02% of a 5 million molecule drug-like test set. Despite its simplicity, the accuracy of SMIRNOFF99Frosst for small molecule hydration free energies and selected properties of pure organic liquids is similar to that of the General Amber Force Field, whose specification requires thousands of parameters. This force field provides a starting point for further optimization and refitting work to follow.

Accuracy Test of the OPLS-AA Force Field for Calculating Free Energies of Mixing and Comparison with PAC-MAC

We have calculated the excess free energy of mixing of 1053 binary mixtures with the OPLS-AA force field using two different methods: thermodynamic integration (TI) of molecular dynamics simulations and the Pair Configuration to Molecular Activity Coefficient (PAC-MAC) method. PAC-MAC is a force field based quasi-chemical method for predicting miscibility properties of various binary mixtures. The TI calculations yield a root mean squared error (RMSE) compared to experimental data of 0.132 k_BT (0.37 kJ/mol). PAC-MAC shows a RMSE of 0.151 k_BT with a calculation speed being potentially 1.0 × 10⁴ times greater than TI. OPLS-AA force field parameters are optimized using PAC-MAC based on vapor–liquid equilibrium data, instead of enthalpies of vaporization or densities. The RMSE of PAC-MAC is reduced to 0.099 k_BT by optimizing 50 force field parameters. The resulting OPLS-PM force field has a comparable accuracy as the OPLS-AA force field in the calculation of mixing free energies using TI.