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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.

We introduce the Open Force Field (OpenFF) 2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this work, we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root-mean-square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔE). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.

Force fields form the basis for classical molecular simulations, and their accuracy is crucial for the quality of, for instance, protein-ligand binding simulations in drug discovery. The huge diversity of small-molecule chemistry makes it a challenge to build and parameterize a suitable force field. The Open Force Field Initiative is a combined industry and academic consortium developing a state-of-the-art small-molecule force field. In this report, industry members of the consortium worked together to objectively evaluate the performance of the force fields (referred to here as OpenFF) produced by the initiative on a combined public and proprietary dataset of 19,653 relevant molecules selected from their internal research and compound collections. This evaluation was important because it was completely blind; at most partners, none of the molecules or data were used in force field development or testing prior to this work. We compare the Open Force Field "Sage" version 2.0.0 and "Parsley" version 1.3.0 with GAFF-2.11-AM1BCC, OPLS4, and SMIRNOFF99Frosst. We analyzed force-field-optimized geometries and conformer energies compared to reference quantum mechanical data. We show that OPLS4 performs best, and the latest Open Force Field release shows a clear improvement compared to its predecessors. The performance of established force fields such as GAFF-2.11 was generally worse. While OpenFF researchers were involved in building the benchmarking infrastructure used in this work, benchmarking was done entirely in-house within industrial organizations and the resulting assessment is reported here. This work assesses the force field performance using separate benchmarking steps, external datasets, and involving external research groups. This effort may also be unique in terms of the number of different industrial partners involved, with 10 different companies participating in the benchmark efforts.

The development of accurate transferable force fields is key to realizing the full potential of atomistic modeling in the study of biological processes such as protein-ligand binding for drug discovery. State-of-the-art transferable force fields, such as those produced by the Open Force Field Initiative, use modern software engineering and automation techniques to yield accuracy improvements. However, force field torsion parameters, which must account for many stereoelectronic and steric effects, are considered to be less transferable than other force field parameters and are therefore often targets for bespoke parametrization. Here, we present the Open Force Field QCSubmit and BespokeFit software packages that, when combined, facilitate the fitting of torsion parameters to quantum mechanical reference data at scale. We demonstrate the use of QCSubmit for simplifying the process of creating and archiving large numbers of quantum chemical calculations, by generating a dataset of 671 torsion scans for druglike fragments. We use BespokeFit to derive individual torsion parameters for each of these molecules, thereby reducing the root-mean-square error in the potential energy surface from 1.1 kcal/mol, using the original transferable force field, to 0.4 kcal/mol using the bespoke version. Furthermore, we employ the bespoke force fields to compute the relative binding free energies of a congeneric series of inhibitors of the TYK2 protein, and demonstrate further improvements in accuracy, compared to the base force field (MUE reduced from 0.560.390.77 to 0.420.280.59 kcal/mol and R2 correlation improved from 0.720.350.87 to 0.930.840.97).

Community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default.

We present a methodology for defining and optimizing a general force field for classical molecular simulations, and we describe its use to derive the Open Force Field 1.0.0 small-molecule force field, codenamed Parsley. Rather than using traditional atom typing, our approach is built on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which handles increases in the diversity and specificity of the force field definition without needlessly increasing the complexity of the specification. Parameters are optimized with the ForceBalance tool, based on reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These quantum reference data are computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. In this initial application of the method, we present essentially a full optimization of all valence parameters and report tests of the resulting force field against compounds and data types outside the training set. These tests 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, as is accuracy on binding free energies. We find that this initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems. Additionally, the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.

Docking calculations can accelerate drug discovery by predicting the bound poses of ligands for a targeted protein. However, it is not clear which docking methods work best. Furthermore, predicting poses requires steps outside the docking algorithm itself, such as preparation of the protein and ligand, and it is not known which components are most in need of improvement. The Continuous Evaluation of Ligand Protein Predictions (CELPP) is a blinded prediction challenge designed to address these issues. Participants create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow's predictions and posts the scores online. The results can be used to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.

APOBEC3B (A3B) is a prominent source of mutation in many cancers. To date, it has been difficult to capture the native protein-DNA interactions that confer A3B's substrate specificity by crystallography due to the highly dynamic nature of wild-type A3B active site. We use computational tools to restore a recent crystal structure of a DNA-bound A3B C-terminal domain mutant construct to its wild type sequence, and run molecular dynamics simulations to study its substrate recognition mechanisms. Analysis of these simulations reveal dynamics of the native A3Bctd-oligonucleotide interactions, including the experimentally inaccessible loop 1-oligonucleotide interactions. A second series of simulations in which the target cytosine nucleotide was computationally mutated from a deoxyribose to a ribose show a change in sugar ring pucker, leading to a rearrangement of the binding site and revealing a potential intermediate in the binding pathway. Finally, apo simulations of A3B, starting from the DNA-bound open state, experience a rapid and consistent closure of the binding site, reaching conformations incompatible with substrate binding. This study reveals a more realistic and dynamic view of the wild type A3B binding site and provides novel insights for structure-guided design efforts for A3B.

We present a substantial update to the open-source POVME binding pocket analysis software. New capabilities of POVME 3.0 include a flexible chemical coloring scheme for feature identification, postanalysis tools for comparing large ensembles of pockets (e.g., from molecular dynamics simulations), and the introduction of scripts and methods that facilitate binding pocket comparison and analysis. We envision the use of this software for visualization of binding pocket dynamics, selection of representative structures for ensemble docking, and incorporation of molecular dynamics results into ligand design efforts.

Non-functional pituitary adenomas (NFPAs) are among the commonest intracranial neoplasms. While histologically benign, NFPAs sometimes become large enough to limit therapeutic options and reduce quality of life. Investigations of the molecular etiology of NFPAs have failed to identify prevalent genetic changes and, while a role for p53 has been suggested, TP53 gene alterations have yet to be described in NFPAs. We found that the polymorphism rs1042522:C > G in codon 72 of exon 4 of the TP53 gene, whose C variant produces a proline and is more common in most ethnicities, has a G variant producing an arginine in 79.8% of NFPAs (n = 42; p

Clinical trials revealed limited response duration of glioblastomas to VEGF-neutralizing antibody bevacizumab. Thriving in the devascularized microenvironment occurring after antiangiogenic therapy requires tumor cell adaptation to decreased glucose, with 50% less glucose identified in bevacizumab-treated xenografts. Compared with bevacizumab-responsive xenograft cells, resistant cells exhibited increased glucose uptake, glycolysis, 13C NMR pyruvate to lactate conversion, and survival in low glucose. Glucose transporter 3 (GLUT3) was upregulated in bevacizumab-resistant versus sensitive xenografts and patient specimens in a HIF-1α-dependent manner. Resistant versus sensitive cell mitochondria in oxidative phosphorylation-selective conditions produced less ATP. Despite unchanged mitochondrial numbers, normoxic resistant cells had lower mitochondrial membrane potential than sensitive cells, confirming poorer mitochondrial health, but avoided the mitochondrial dysfunction of hypoxic sensitive cells. Thin-layer chromatography revealed increased triglycerides in bevacizumab-resistant versus sensitive xenografts, a change driven by mitochondrial stress. A glycogen synthase kinase-3β inhibitor suppressing GLUT3 transcription caused greater cell death in bevacizumab-resistant than -responsive cells. Overexpressing GLUT3 in tumor cells recapitulated bevacizumab-resistant cell features: survival and proliferation in low glucose, increased glycolysis, impaired oxidative phosphorylation, and rapid in vivo proliferation only slowed by bevacizumab to that of untreated bevacizumab-responsive tumors. Targeting GLUT3 or the increased glycolysis reliance in resistant tumors could unlock the potential of antiangiogenic treatments.