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.