Electrostatically embedded symmetry-adapted perturbation theory.

Symmetry-adapted perturbation theory (SAPT) is an ab initio approach that directly computes noncovalent interaction energies in terms of electrostatics, exchange repulsion, induction/polarization, and London dispersion components. Due to its high computational scaling, routine applications of even the lowest order of SAPT are typically limited to a few hundred atoms. To address this limitation, we report here the addition of electrostatic embedding to the SAPT (EE-SAPT) and ISAPT (EE-ISAPT) methods.

Discovery and Optimization of Aryl Piperidinone Ureas as Selective Formyl Peptide Receptor 2 Agonists.

We report the discovery and optimization of aryl piperidinone urea formyl peptide receptor 2 (FPR2) agonists from a weakly active high-throughput screening (HTS) hit to potent and selective agonists with favorable efficacy in acute models. A basis for the selectivity for FPR2 over FPR1 is proposed based on docking molecules into recently reported FPR2 and FPR1 cryoEM structures. Compounds from the new scaffold reported in this study exhibited superior potency and selectivity and favorable ADME profiles.

Optimisation of dynamic nuclear polarisation using "off-the-shelf" Gd(III)-based polarising agents.

Complexes of paramagnetic metal ions, in particular Gd, have been demonstrated as efficient polarising agents for magic-angle spinning (MAS) dynamic nuclear polarisation (DNP). We recently demonstrated that commercially available and inexpensive Gd(NO) is suitable for use as an "off-the-shelf" MAS DNP polarising agent, providing promising sensitivity enhancements to H, C, and N NMR signals. Here we expand upon this approach by investigating the impact of the Gd(NO) concentration and by exploring a larger range of readily available Gd sources.

A physics-aware neural network for protein-ligand interactions with quantum chemical accuracy.

Quantifying intermolecular interactions with quantum chemistry (QC) is useful for many chemical problems, including understanding the nature of protein-ligand interactions. Unfortunately, QC computations on protein-ligand systems are too computationally expensive for most use cases. The flourishing field of machine-learned (ML) potentials is a promising solution, but it is limited by an inability to easily capture long range, non-local interactions.

Directional Δ Neural Network (DrΔ-Net): A Modular Neural Network Approach to Binding Free Energy Prediction.

The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems.

A quantum chemical interaction energy dataset for accurately modeling protein-ligand interactions.

Fast and accurate calculation of intermolecular interaction energies is desirable for understanding many chemical and biological processes, including the binding of small molecules to proteins. The Splinter ["Symmetry-adapted perturbation theory (SAPT0) protein-ligand interaction"] dataset has been created to facilitate the development and improvement of methods for performing such calculations. Molecular fragments representing commonly found substructures in proteins and small-molecule ligands were paired into >9000 unique dimers, assembled into numerous configurations using an approach designed to adequately cover the breadth of the dimers' potential energy surfaces while enhancing sampling in favorable regions.

A quantitative assessment of deformation energy in intermolecular interactions: How important is it?

Dimer interaction energies have been well studied in computational chemistry, but they can offer an incomplete understanding of molecular binding depending on the system. In the current study, we present a dataset of focal-point coupled-cluster interaction and deformation energies (summing to binding energies, De) of 28 organic molecular dimers. We use these highly accurate energies to evaluate ten density functional approximations for their accuracy.

Modified B-QUIET scoring of kidney images acquired by veterinary students in live canines with versus without sequential practice on ultrasound phantoms.

Ultrasound phantoms are training tools that can help students learn basic ultrasound principles. The purpose of this prospective cohort design study was to determine whether preclinical veterinary students in a curriculum with more phantom training sessions acquire better-quality ultrasound images of kidneys in live canines compared with students in a curriculum without sequential phantom training sessions. In clinical skills labs, 132 second-year (2VM) and 130 third-year (3VM) veterinary students obtained sagittal and transverse images of the left kidney of healthy, student-owned dogs.

Off-the-Shelf Gd(NO) as an Efficient High-Spin Metal Ion Polarizing Agent for Magic Angle Spinning Dynamic Nuclear Polarization.

Magic angle spinning nuclear magnetic resonance spectroscopy experiments are widely employed in the characterization of solid media. The approach is incredibly versatile but deleteriously suffers from low sensitivity, which may be alleviated by adopting dynamic nuclear polarization methods, resulting in large signal enhancements. Paramagnetic metal ions such as Gd have recently shown promising results as polarizing agents for H, C, and N nuclear spins.

Protein-based polymer liquid embolics for cerebral aneurysms.

Cerebral aneurysms (CA), an abnormal bulge in the arteries that supply blood to the brain, are prone to rupture and can cause hemorrhagic stroke. Physicians can treat CA by blocking blood flow to the aneurysmal sac via clipping of the aneurysm neck via open procedure, or endovascular occlusion of the aneurysm with embolic materials to promote thrombus formation to prevent further inflow of blood into the aneurysm. Endovascular treatment options for CA still have significant limitations in terms of safety, usability in coagulopathic patients, and risks of device migration.

Discovery of Heteroaryl Urea Isosteres for Formyl Peptide Receptor 2 Agonists.

Formyl peptide receptor 2 (FPR2) agonists have shown efficacy in inflammatory-driven animal disease models and have the potential to treat a range of diseases. Many reported synthetic agonists contain a phenylurea, which appears to be necessary for activity in the reported chemotypes. We set out to find isosteres for the phenylurea and focused our efforts on heteroaryl rings.

The influence of a solvent environment on direct non-covalent interactions between two molecules: A symmetry-adapted perturbation theory study of polarization tuning of π-π interactions by water.

High-level quantum chemical computations have provided significant insight into the fundamental physical nature of non-covalent interactions. These studies have focused primarily on gas-phase computations of small van der Waals dimers; however, these interactions frequently take place in complex chemical environments, such as proteins, solutions, or solids. To better understand how the chemical environment affects non-covalent interactions, we have undertaken a quantum chemical study of π-π interactions in an aqueous solution, as exemplified by T-shaped benzene dimers surrounded by 28 or 50 explicit water molecules.

Sample volume effects in optical overhauser dynamic nuclear polarization.

The optical dynamic nuclear polarization (DNP) method has been proposed as an alternative to microwave pumping as a hyperpolarization method for solution-state NMR studies. Using continuous laser illumination to photogenerate triplet states in the presence of a persistent radical produces chemically-induced dynamic electron polarization (CIDEP) via the radical-triplet pair mechanism (RTPM), with cross-relaxation transferring this to nuclear hyperpolarization via an Overhauser mechanism. Numerical simulations have previously indicated that reducing the sample volume while maintaining a constant optical density can significantly increase the NMR signal enhancement, due to the larger steady-state concentration of triplets obtained.

Translational Development of a Silk-Elastinlike Protein Polymer Embolic for Transcatheter Arterial Embolization.

Locally blocking blood flow to tumors with embolic materials is the key to transcatheter arterial embolization for treating hepatocellular carcinoma. Current microparticle agents do not deeply penetrate target tissues and are compatible with a very limited selection of therapeutic agents. Silk-elastinlike protein polymers (SELPs) combine the solubility of elastin and the strength of silk to create an easily injected liquid embolic that transition into a solid depot amenable to loading with drugs, gene therapy agents, or biologics.

Optimized damping parameters for empirical dispersion corrections to symmetry-adapted perturbation theory.

Symmetry-adapted perturbation theory (SAPT) has become an invaluable tool for studying the fundamental nature of non-covalent interactions by directly computing the electrostatics, exchange (steric) repulsion, induction (polarization), and London dispersion contributions to the interaction energy using quantum mechanics. Further application of SAPT is primarily limited by its computational expense, where even its most affordable variant (SAPT0) scales as the fifth power of system size [O(N)] due to the dispersion terms. The algorithmic scaling of SAPT0 is reduced from O(N)→O(N) by replacing these terms with the empirical D3 dispersion correction of Grimme and co-workers, forming a method that may be termed SAPT0-D3.

Cartesian message passing neural networks for directional properties: Fast and transferable atomic multipoles.

The message passing neural network (MPNN) framework is a promising tool for modeling atomic properties but is, until recently, incompatible with directional properties, such as Cartesian tensors. We propose a modified Cartesian MPNN (CMPNN) suitable for predicting atom-centered multipoles, an essential component of ab initio force fields. The efficacy of this model is demonstrated on a newly developed dataset consisting of 46 623 chemical structures and corresponding high-quality atomic multipoles, which was deposited into the publicly available Molecular Sciences Software Institute QCArchive server.

Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge.

Atomic charges are critical quantities in molecular mechanics and molecular dynamics, but obtaining these quantities requires heuristic choices based on atom typing or relatively expensive quantum mechanical computations to generate a density to be partitioned. Most machine learning efforts in this domain ignore total molecular charges, relying on overfitting and arbitrary rescaling in order to match the total system charge. Here, we introduce the electron-passing neural network (EPNN), a fast, accurate neural network atomic charge partitioning model that conserves total molecular charge by construction.

AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials.

Intermolecular interactions are critical to many chemical phenomena, but their accurate computation using ab initio methods is often limited by computational cost. The recent emergence of machine learning (ML) potentials may be a promising alternative. Useful ML models should not only estimate accurate interaction energies but also predict smooth and asymptotically correct potential energy surfaces.

One-year chronic toxicity evaluation of single dose intravenously administered silica nanoparticles in mice and their Ex vivo human hemocompatibility.

Chronic toxicity evaluations of nanotechnology-based drugs are essential to support initiation of clinical trials. Ideally such evaluations should address the dosing strategy in human applications and provide sufficient information for long-term usage. Herein, we investigated one-year toxicity of non-surface modified silica nanoparticles (SNPs) with variations in size and porosity (Stöber SNPs 46 ± 4.

Approaches for machine learning intermolecular interaction energies and application to energy components from symmetry adapted perturbation theory.

Accurate prediction of intermolecular interaction energies is a fundamental challenge in electronic structure theory due to their subtle character and small magnitudes relative to total molecular energies. Symmetry adapted perturbation theory (SAPT) provides rigorous quantum mechanical means for computing such quantities directly and accurately, but for a computational cost of at least O(N), where N is the number of atoms. Here, we report machine learned models of SAPT components with a computational cost that scales asymptotically linearly, O(N).

Optically-generated Overhauser dynamic nuclear polarization: A numerical analysis.

Recently, an alternative approach to dynamic nuclear polarization (DNP) in the liquid state was introduced using optical illumination instead of microwave pumping. By exciting a suitable dye to the triplet state which undergoes a diffusive encounter with a persistent radical forming a quartet-doublet pair in the encounter complex, dynamic electron polarization (DEP) is generated via the radical-triplet pair mechanism. Subsequent cross-relaxation generates nuclear polarization without the need for microwave saturation of the electronic transitions.