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.

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.

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.

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.

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).

Assessment of Density Functional Methods for Geometry Optimization of Bimolecular van der Waals Complexes.

We explore the suitability of three popular density functionals (B97-D3, B3LYP-D3, M05-2X) for producing accurate equilibrium geometries of van der Waals (vdW) complexes with diverse binding motifs. For these functionals, optimizations using Dunning's aug-cc-pVDZ basis set best combine accuracy and a reasonable computational expense. Each DFT/aug-cc-pVDZ combination produces optimized equilibrium geometries for 21 small vdW complexes of organic molecules (up to four non-hydrogen atoms total) that agree with high-level CCSD(T)/CBS reference geometries to within ±0.

Improving Accuracy, Diversity, and Speed with Prime Macrocycle Conformational Sampling.

A novel method for exploring macrocycle conformational space, Prime macrocycle conformational sampling (Prime-MCS), is introduced and evaluated in the context of other available algorithms (Molecular Dynamics, LowModeMD in MOE, and MacroModel Baseline Search). The algorithms were benchmarked on a data set of 208 macrocycles which was curated for diversity from the Cambridge Structural Database, the Protein Data Bank, and the Biologically Interesting Molecule Reference Dictionary. The algorithms were evaluated in terms of accuracy (ability to reproduce the crystal structure), diversity (coverage of conformational space), and computational speed.

Neutral macrocyclic factor VIIa inhibitors.

Factor VIIa (FVIIa) inhibitors have shown strong antithrombotic efficacy in preclinical thrombosis models with limited bleeding liabilities. Discovery of potent, orally active FVIIa inhibitors has been largely unsuccessful due to the requirement of a basic P1 group to interact with Asp189 in the S1 binding pocket, limiting their membrane permeability. We have combined recently reported neutral P1 binding substituents with a highly optimized macrocyclic chemotype to produce FVIIa inhibitors with low nanomolar potency and enhanced permeability.

The Surprising Importance of Peptide Bond Contacts in Drug-Protein Interactions.

The study of noncovalent interactions, notably including drug-protein binding, relies heavily on the language of localized functional group contacts: hydrogen bonding, π-π interactions, CH-π contacts, halogen bonding, etc. Applying the state-of-the-art functional group symmetry-adapted perturbation theory (F-SAPT) to an important question of chloro versus methyl aryl substitution in factor Xa inhibitor drugs, we find that a localized contact model provides an incorrect picture for the origin of the enhancement of chloro-containing ligands. Instead, the enhancement is found to originate from many intermediate-range contacts distributed throughout the binding pocket, particularly including the peptide bonds in the protein backbone.

Design and Synthesis of Novel Meta-Linked Phenylglycine Macrocyclic FVIIa Inhibitors.

Two novel series of meta-linked phenylglycine-based macrocyclic FVIIa inhibitors have been designed to improve the rodent metabolic stability and PK observed with the precursor para-linked phenylglycine macrocycles. Through iterative structure-based design and optimization, the TF/FVIIa was improved to subnanomolar levels with good clotting activity, metabolic stability, and permeability.

Discovery of Phenylglycine Lactams as Potent Neutral Factor VIIa Inhibitors.

Inhibitors of Factor VIIa (FVIIa), a serine protease in the clotting cascade, have shown strong antithrombotic efficacy in preclinical thrombosis models with minimal bleeding liabilities. Discovery of potent, orally active FVIIa inhibitors has been largely unsuccessful because known chemotypes have required a highly basic group in the S1 binding pocket for high affinity. A recently reported fragment screening effort resulted in the discovery of a neutral heterocycle, 7-chloro-3,4-dihydroisoquinolin-1(2)-one, that binds in the S1 pocket of FVIIa and can be incorporated into a phenylglycine FVIIa inhibitor.

Synthesis and P1' SAR exploration of potent macrocyclic tissue factor-factor VIIa inhibitors.

Selective tissue factor-factor VIIa complex (TF-FVIIa) inhibitors are viewed as promising compounds for treating thrombotic disease. In this contribution, we describe multifaceted exploratory SAR studies of S1'-binding moieties within a macrocyclic chemotype aimed at replacing cyclopropyl sulfone P1' group. Over the course of the optimization efforts, the 1-(1H-tetrazol-5-yl)cyclopropane P1' substituent emerged as an improved alternative, offering increased metabolic stability and lower clearance, while maintaining excellent potency and selectivity.

Discovery of a Highly Potent, Selective, and Orally Bioavailable Macrocyclic Inhibitor of Blood Coagulation Factor VIIa-Tissue Factor Complex.

Inhibitors of the tissue factor (TF)/factor VIIa complex (TF-FVIIa) are promising novel anticoagulants which show excellent efficacy and minimal bleeding in preclinical models. Starting with an aminoisoquinoline P1-based macrocyclic inhibitor, optimization of the P' groups led to a series of highly potent and selective TF-FVIIa inhibitors which displayed poor permeability. Fluorination of the aminoisoquinoline reduced the basicity of the P1 group and significantly improved permeability.

Atropisomer Control in Macrocyclic Factor VIIa Inhibitors.

Incorporation of a methyl group onto a macrocyclic FVIIa inhibitor improves potency 10-fold but is accompanied by atropisomerism due to restricted bond rotation in the macrocyclic structure, as demonstrated by NMR studies. We designed a conformational constraint favoring the desired atropisomer in which this methyl group interacts with the S2 pocket of FVIIa. A macrocyclic inhibitor incorporating this constraint was prepared and demonstrated by NMR to reside predominantly in the desired conformation.

Structure-Based Design of Macrocyclic Coagulation Factor VIIa Inhibitors.

On the basis of a crystal structure of a phenylpyrrolidine lead and subsequent molecular modeling results, we designed and synthesized a novel series of macrocyclic FVIIa inhibitors. The optimal 16-membered macrocycle was 60-fold more potent than an acyclic analog. Further potency optimization by incorporation of P1' alkyl sulfone and P2 methyl groups provided a macrocycle with TF/FVIIa Ki = 1.