Driving torsion scans with wavefront propagation

Fine-tuning property domain weighting factors and the objective function in force-field parameter optimization

Force field (FF) based molecular modeling is an often used method to investigate and study structural and dynamic properties of (bio-)chemical substances and systems. When such a system is modeled or refined, the force-field parameters need to be adjusted. This force-field parameter optimization can be a tedious task and is always a trade-off in terms of errors regarding the targeted properties. To better control the balance of various properties' errors, in this study we introduce weighting factors for the optimization objectives. Different weighting strategies are compared to fine-tune the balance between bulk-phase density and relative conformational energies (RCE), using n-octane as a representative system. Additionally, a non-linear projection of the individual property-specific parts of the optimized loss function is deployed to further improve the balance between them. The results show that the combined error for the reproduction of the properties targeted in this optimization is reduced. Furthermore, the transferability of the force field parameters (FFParams) to chemically similar systems is increased. One interesting outcome is a large variety in the resulting optimized FFParams and corresponding errors, suggesting that the optimization landscape is multi-modal and very dependent on the weighting factor setup. We conclude that adjusting the weighting factors can be a very important feature to lower the overall error in the FF optimization procedure, giving researchers the possibility to fine-tune their FFs.

Evaluation of Machine Learning/Molecular Mechanics End-State Corrections with Mechanical Embedding to Calculate Relative Protein-Ligand Binding Free Energies

The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclusion of quantum-mechanical effects in molecular interactions. However, ML simulations are several times more computationally demanding than MM simulations, so there is a trade-off between speed and accuracy. One possible compromise are hybrid machine learning/molecular mechanics (ML/MM) approaches with mechanical embedding that treat the intramolecular interactions of the ligand at the ML level and the protein-ligand interactions at the MM level. Recent studies have reported improved protein-ligand binding free energy results based on ML/MM using ANI-2x with mechanical embedding, arguing that intramolecular interactions like torsion potentials of the ligand are often the limiting factor for accuracy. This claim is evaluated based on 108 relative binding free energy calculations for four different benchmark systems. As an alternative strategy, we also tested a tool that fits the MM dihedral potentials to the ML level of theory. Fitting was performed with the ML potentials ANI-2x and AIMNet2, and, for the benchmark system TYK2, also with quantum-mechanical calculations using ωB97M-D3(BJ)/def2-TZVPPD. Overall, the relative binding free energy results from MM with Open Force Field 2.2.0, MM with ML-fitted torsion potentials, and the corresponding ML/MM end-state corrected simulations show no statistically significant differences in the mean absolute errors (between 0.8 and 0.9 kcal mol-1). This can probably be explained by the usage of the same MM parameters to calculate the protein-ligand interactions. Therefore, a well-parametrized force field is on a par with simple mechanical embedding ML/MM simulations for protein-ligand binding. In terms of computational costs, the reparametrization of poor torsional potentials is preferable over employing computationally intensive ML/MM simulations of protein-ligand complexes with mechanical embedding. Also, the refitting strategy leads to lower variances of the protein-ligand binding free energy results than the ML/MM end-state corrections. For free energy corrections with ML/MM, the results indicate that better convergence and more advanced ML/MM schemes will be required for applications in computer-guided drug discovery.

Determinants of chemoselectivity in ubiquitination by the J2 family of ubiquitin-conjugating enzymes

Ubiquitin-conjugating enzymes (E2) play a crucial role in the attachment of ubiquitin to proteins. Together with ubiquitin ligases (E3), they catalyze the transfer of ubiquitin (Ub) onto lysines with high chemoselectivity. A subfamily of E2s, including yeast Ubc6 and human Ube2J2, also mediates noncanonical modification of serines, but the structural determinants for this chemical versatility remain unknown. Using a combination of X-ray crystallography, molecular dynamics (MD) simulations, and reconstitution approaches, we have uncovered a two-layered mechanism that underlies this unique reactivity. A rearrangement of the Ubc6/Ube2J2 active site enhances the reactivity of the E2-Ub thioester, facilitating attack by weaker nucleophiles. Moreover, a conserved histidine in Ubc6/Ube2J2 activates a substrate serine by general base catalysis. Binding of RING-type E3 ligases further increases the serine selectivity inherent to Ubc6/Ube2J2, via an allosteric mechanism that requires specific positioning of the ubiquitin tail at the E2 active site. Our results elucidate how subtle structural modifications to the highly conserved E2 fold yield distinct enzymatic activity.

Evaluation of All-Atom and Martini 3 Coarse-Grained Force Fields from the Structural Investigation of Nitroxide Spin Probes and Their Confinement in Beta-Cyclodextrin

Nitroxide radicals have found wide applications as spin labels or probes, and their guest-host interactions with cyclodextrins exhibit enhanced applications in electron spin resonance (ESR) spectroscopy and imaging due to improved biostability toward reducing agents. Although the computational prediction of the guest-host binding has become increasingly common for small ligands, molecular simulations regarding the conformational preferences of hosted spin probes have not been conducted. Here we present molecular dynamics simulations at an atomistic level for a set of four TEMPO (2,2,6,6-tetramethylpiperidine 1-oxyl) spin probes and thereafter develop coarse-grained models compatible with the recent version of the Martini force field (v 3.0) to tackle their encapsulation in the cavity of β-cyclodextrin (βCD) for which experimental ESR data are available. The results indicate that the atomistic descriptions perform well in relation to the structural parameters derived from X-ray diffraction as well as hydrogen bonding and hydrogen patterns and predict that the guest-host complexation is hydrophobically driven by the presence of a methyl group pair of the spin probe at the cavity center of βCD. The spin probe mobility at the binding site reveals the nitroxide group orientation toward the secondary rim of the cyclodextrin and the alternating presence of the two methyl group pairs inside the cavity, features in agreement with the experimental behavior of the ESR parameters. The coarse-grained parameterizations of TEMPO probes and βCD rely on optimizing the bonded and nonbonded parameters with references to the atomistic simulation results, and they are capable of recovering the orientation and location of the spin probe inside the cyclodextrin cavity predicted by the atomistic guest-host complexes. The results suggest the cyclodextrin host-guest system as a powerful validation suite to evaluate new coarse-grained parameterizations of small ligands and future extensions to functionalized cyclodextrins in inclusion complexes.

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

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

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.

Discovery of macrocyclic CDK2/4/6 inhibitors with improved potency and DMPK properties through a highly efficient macrocyclic drug design platform

Cyclin-dependent kinases (CDKs) are critical cell cycle regulators that are often overexpressed in tumors, making them promising targets for anti-cancer therapies. Despite substantial advancements in optimizing the selectivity and drug-like properties of CDK inhibitors, safety of multi-target inhibitors remains a significant challenge. Macrocyclization is a promising drug discovery strategy to improve the pharmacological properties of existing compounds. Here we report the development of a macrocyclization platform that enabled the highly efficient discovery of a novel, macrocyclic CDK2/4/6 inhibitor from an acyclic precursor (NUV422). Using dihedral angle scan and structure-based, computer-aided drug design to select an optimal ring-closing site and linker length for the macrocycle, we identified compound 8 as a potent new CDK2/4/6 inhibitor with optimized cellular potency and safety profile compared to NUV422. Our platform leverages both experimentally-solved as well as generative chemistry-derived macrocyclic structures and can be deployed to streamline the design of macrocyclic new drugs from acyclic starting compounds, yielding macrocyclic compounds with enhanced potency and improved drug-like properties.

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.

PTM-Psi: A python package to facilitate the computational investigation of post-translational modification on protein structures and their impacts on dynamics and functions

Post-translational modification (PTM) of a protein occurs after it has been synthesized from its genetic template, and involves chemical modifications of the protein's specific amino acid residues. Despite of the central role played by PTM in regulating molecular interactions, particularly those driven by reversible redox reactions, it remains challenging to interpret PTMs in terms of protein dynamics and function because there are numerous combinatorially enormous means for modifying amino acids in response to changes in the protein environment. In this study, we provide a workflow that allows users to interpret how perturbations caused by PTMs affect a protein's properties, dynamics, and interactions with its binding partners based on inferred or experimentally determined protein structure. This Python-based workflow, called PTM-Psi, integrates several established open-source software packages, thereby enabling the user to infer protein structure from sequence, develop force fields for non-standard amino acids using quantum mechanics, calculate free energy perturbations through molecular dynamics simulations, and score the bound complexes via docking algorithms. Using the S-nitrosylation of several cysteines on the GAP2 protein as an example, we demonstrated the utility of PTM-Psi for interpreting sequence-structure-function relationships derived from thiol redox proteomics data. We demonstrate that the S-nitrosylated cysteine that is exposed to the solvent indirectly affects the catalytic reaction of another buried cysteine over a distance in GAP2 protein through the movement of the two ligands. Our workflow tracks the PTMs on residues that are responsive to changes in the redox environment and lays the foundation for the automation of molecular and systems biology modeling.

Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field

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.

Phosphorylation Modification Force Field FB18CMAP Improving Conformation Sampling of Phosphoproteins

Phosphorylation of proteins plays an important regulatory role at almost all levels of cellular organization. Molecular dynamics (MD) simulation is a promising tool to reveal the mechanism of how phosphorylation regulates many key biological processes at the atomistic level. MD simulation accuracy depends on force field precision, while the current force fields for phospho-amino acids have resulted in notable inconsistency with experimental data. Here, a new force field parameter (named FB18CMAP) is generated by fitting against quantum mechanics (QM) energy in aqueous solution with φ/ψ dihedral potential-energy surfaces optimized using CMAP parameters. MD simulations of phosphorylated dipeptides, intrinsically disordered proteins (IDPs), and ordered (folded) proteins show that FB18CMAP can mimic NMR observables and structural characteristics of phosphorylated dipeptides and proteins more accurately than the FB18 force field. These findings suggest that FB18CMAP performs well in both the simulation of ordered and disordered states of phosphorylated proteins.

Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions

Simulations of molecular systems using electronic structure methods are still not feasible for many systems of biological importance. As a result, empirical methods such as force fields (FF) have become an established tool for the simulation of large and complex molecular systems. The parametrization of FF is, however, time-consuming and has traditionally been based on experimental data. Recent years have therefore seen increasing efforts to automatize FF parametrization or to replace FF with machine-learning (ML) based potentials. Here, we propose an alternative strategy to parametrize FF, which makes use of ML and gradient-descent based optimization while retaining a functional form founded in physics. Using a predefined functional form is shown to enable interpretability, robustness, and efficient simulations of large systems over long time scales. To demonstrate the strength of the proposed method, a fixed-charge and a polarizable model are trained on ab initio potential-energy surfaces. Given only information about the constituting elements, the molecular topology, and reference potential energies, the models successfully learn to assign atom types and corresponding FF parameters from scratch. The resulting models and parameters are validated on a wide range of experimentally and computationally derived properties of systems including dimers, pure liquids, and molecular crystals.

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale

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 0.56_0.39^0.77 to 0.42_0.28^0.59 kcal/mol and R² correlation improved from 0.72_0.35^0.87 to 0.93_0.84^0.97).

End-to-end differentiable construction of molecular mechanics force fields

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process-spanning chemical perception to parameter assignment-is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with low inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field ("Parsley") openff-1.2.0 small molecule force field augmented with a peptide dataset, the resulting espaloma model shows superior accuracy vis-á-vis experiments in computing relative alchemical free energy calculations for a popular benchmark. This approach is implemented in the free and open source package espaloma, available at https://github.com/choderalab/espaloma.

Automation of AMOEBA polarizable force field for small molecules: Poltype 2

A next-generation protocol (Poltype 2) has been developed which automatically generates AMOEBA polarizable force field parameters for small molecules. Both features and computational efficiency have been drastically improved. Notable advances include improved database transferability using SMILES, robust torsion fitting, non-aromatic ring torsion parameterization, coupled torsion-torsion parameterization, Van der Waals parameter refinement using ab initio dimer data and an intelligent fragmentation scheme that produces parameters with dramatically reduced ab initio computational cost. Additional improvements include better local frame assignment for atomic multipoles, automated formal charge assignment, Zwitterion detection, smart memory resource defaults, parallelized fragment job submission, incorporation of Psi4 quantum package, ab initio error handling, ionization state enumeration, hydration free energy prediction and binding free energy prediction. For validation, we have applied Poltype 2 to ~1000 FDA approved drug molecules from DrugBank. The ab initio molecular dipole moments and electrostatic potential values were compared with Poltype 2 derived AMOEBA counterparts. Parameters were further substantiated by calculating hydration free energy (HFE) on 40 small organic molecules and were compared with experimental data, resulting in an RMSE error of 0.59 kcal/mol. The torsion database has expanded to include 3543 fragments derived from FDA approved drugs. Poltype 2 provides a convenient utility for applications including binding free energy prediction for computational drug discovery. Further improvement will focus on automated parameter refinement by experimental liquid properties, expansion of the Van der Waals parameter database and automated parametrization of modified bio-fragments such as amino and nucleic acids.

Absolute binding free energy calculations improve enrichment of actives in virtual compound screening

We determined the effectiveness of absolute binding free energy (ABFE) calculations to refine the selection of active compounds in virtual compound screening, a setting where the more commonly used relative binding free energy approach is not readily applicable. To do this, we conducted baseline docking calculations of structurally diverse compounds in the DUD-E database for three targets, BACE1, CDK2 and thrombin, followed by ABFE calculations for compounds with high docking scores. The docking calculations alone achieved solid enrichment of active compounds over decoys. Encouragingly, the ABFE calculations then improved on this baseline. Analysis of the results emphasizes the importance of establishing high quality ligand poses as starting points for ABFE calculations, a nontrivial goal when processing a library of diverse compounds without informative co-crystal structures. Overall, our results suggest that ABFE calculations can play a valuable role in the drug discovery process.

Exploration and validation of force field design protocols through QM-to-MM mapping

The scale of the parameter optimisation problem in traditional molecular mechanics force field construction means that design of a new force field is a long process, and sub-optimal choices made in the early stages can persist for many generations. We hypothesise that careful use of quantum mechanics to inform molecular mechanics parameter derivation (QM-to-MM mapping) should be used to significantly reduce the number of parameters that require fitting to experiment and increase the pace of force field development. Here, we design and train a collection of 15 new protocols for small, organic molecule force field derivation, and test their accuracy against experimental liquid properties. Our best performing model has only seven fitting parameters, yet achieves mean unsigned errors of just 0.031 g cm^-3 and 0.69 kcal mol^-1 in liquid densities and heats of vaporisation, compared to experiment. The software required to derive the designed force fields is freely available at https://github.com/qubekit/QUBEKit.

Development and Validation of AMBER-FB15-Compatible Force Field Parameters for Phosphorylated Amino Acids

Phosphorylation of select amino acid residues is one of the most common biological mechanisms for regulating protein structures and functions. While computational modeling can be used to explore the detailed structural changes associated with phosphorylation, most molecular mechanics force fields developed for the simulation of phosphoproteins have been noted to be inconsistent with experimental data. In this work, we parameterize force fields for the phosphorylated forms of the amino acids serine, threonine, and tyrosine using the ForceBalance software package with the goal of improving agreement with experiments for these residues. Our optimized force field, denoted as FB18, is parameterized using high-quality ab initio potential energy scans and is designed to be fully compatible with the AMBER-FB15 protein force field. When utilized in MD simulations together with the TIP3P-FB water model, we find that FB18 consistently enhances the prediction of experimental quantities such as ³J NMR couplings and intramolecular hydrogen-bonding propensities in comparison to previously published models. As was reported with AMBER-FB15, we also see improved agreement with the reference QM calculations in regions at and away from local minima. We thus believe that the FB18 parameter set provides a promising route for the further investigation of the varied effects of protein phosphorylation.

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

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.

Modeling Molecular Emitters in Organic Light-Emitting Diodes with the Quantum Mechanical Bespoke Force Field

Combined molecular dynamics (MD) and quantum mechanics (QM) simulation procedures have gained popularity in modeling the spectral properties of functional organic molecules. However, the potential energy surfaces used to propagate long-time scale dynamics in these simulations are typically described using general, transferable force fields designed for organic molecules in their electronic ground states. These force fields do not typically include spectroscopic data in their training, and importantly, there is no general protocol for including changes in geometry or intermolecular interactions with the environment that may occur upon electronic excitation. In this work, we show that parameters tailored for thermally activated delayed fluorescence (TADF) emitters used in organic light-emitting diodes (OLEDs), in both their ground and electronically excited states, can be readily derived from a small number of QM calculations using the QUBEKit (QUantum mechanical BEspoke toolKit) software and improve the overall accuracy of these simulations.

Q-Force: Quantum Mechanically Augmented Molecular Force Fields

The quality of molecular dynamics simulations strongly depends on the accuracy of the underlying force fields (FFs) that determine all intra- and intermolecular interactions of the system. Commonly, transferable FF parameters are determined based on a representative set of small molecules. However, such an approach sacrifices accuracy in favor of generality. In this work, an open-source and automated toolkit named Q-Force is presented, which augments these transferable FFs with molecule-specific bonded parameters and atomic charges that are derived from quantum mechanical (QM) calculations. The molecular fragmentation procedure allows treatment of large molecules (>200 atoms) with a low computational cost. The generated Q-Force FFs can be used at the same computational cost as transferable FFs, but with improved accuracy: We demonstrate this for the vibrational properties on a set of small molecules and for the potential energy surface on a complex molecule (186 atoms) with photovoltaic applications. Overall, the accuracy, user-friendliness, and minimal computational overhead of the Q-Force protocol make it widely applicable for atomistic molecular dynamics simulations.

Implementation of the QUBE Force Field in SOMD for High-Throughput Alchemical Free-Energy Calculations

The quantum mechanical bespoke (QUBE) force-field approach has been developed to facilitate the automated derivation of potential energy function parameters for modeling protein-ligand binding. To date, the approach has been validated in the context of Monte Carlo simulations of protein-ligand complexes. We describe here the implementation of the QUBE force field in the alchemical free-energy calculation molecular dynamics simulation package SOMD. The implementation is validated by demonstrating the reproducibility of absolute hydration free energies computed with the QUBE force field across the SOMD and GROMACS software packages. We further demonstrate, by way of a case study involving two series of non-nucleoside inhibitors of HIV-1 reverse transcriptase, that the availability of QUBE in a modern simulation package that makes efficient use of graphics processing unit acceleration will facilitate high-throughput alchemical free-energy calculations.

Computational and photophysical characterization of a Laurdan malononitrile derivative

The malononitrile group is considered one of the strongest natural electron-withdrawing groups in a chemist's arsenal. However, surprisingly little is known about how this group functions in push-pull fluorophores. In a recent computational study, we reported that replacing the ketone group of the traditional push-pull dye Laurdan with a malononitrile group significantly improves the optical properties while retaining the membrane behavior of the parent molecule Laurdan. Motivated by these results, we report here the synthesis and photophysical characterization of the said compound, 6-(1-undecyl-2,2-dicyanovinyl)-N,N-dimethyl-2-naphthylamine (CN-Laurdan). To our surprise, this new CN-Laurdan probe is found to be much less bright than the parent Laurdan due to a large drop in the fluorescence quantum yield. Using computational methods, we determine that the origin of this low quantum yield is related to the existence of a non-radiative decay pathway related to a rotation of the malononitrile moiety, suggesting that the molecule could nonetheless function very well as a molecular rotor. We confirm experimentally that CN-Laurdan functions as a molecular rotor by measuring the quantum yield in methanol/glycerol mixtures of increasing viscosity. Specifically, we found a consistent increase in the quantum yield across the entire range of tested viscosities.

Mechanistic analysis of light-driven overcrowded alkene-based molecular motors by multiscale molecular simulations

We analyze light-driven overcrowded alkene-based molecular motors, an intriguing class of small molecules that have the potential to generate MHz-scale rotation rates. The full rotation process is simulated at multiple scales by combining quantum surface-hopping molecular dynamics (MD) simulations for the photoisomerization step with classical MD simulations for the thermal helix inversion step. A Markov state analysis resolves conformational substates, their interconversion kinetics, and their roles in the motor's rotation process. Furthermore, motor performance metrics, including rotation rate and maximal power output, are computed to validate computations against experimental measurements and to inform future designs. Lastly, we find that to correctly model these motors, the force field must be optimized by fitting selected parameters to reference quantum mechanical energy surfaces. Overall, our simulations yield encouraging agreement with experimental observables such as rotation rates, and provide mechanistic insights that may help future designs.