Quantum Mechanics-Based Properties for 3D-QSAR

We have used a set of four local properties based on semiempirical molecular orbital calculations (electron density (ρ), hydrogen bond donor field (HDF), hydrogen bond acceptor field (HAF), and molecular lipophilicity potential (MLP)) for 3D-QSAR studies to overcome the limitations of the current force field-based molecular interaction fields (MIFs). These properties can be calculated rapidly and are thus amenable to high-throughput industrial applications. Their statistical performance was compared with that of conventional 3D-QSAR approaches using nine data sets (angiotensin converting enzyme inhibitors (ACE), acetylcholinesterase inhibitors (AchE), benzodiazepine receptor ligands (BZR), cyclooxygenase-2 inhibitors (COX2), dihydrofolate reductase inhibitors (DHFR), glycogen phosphorylase b inhibitors (GPB), thermolysin inhibitors (THER), thrombin inhibitors (THR), and serine protease factor Xa inhibitors (fXa)). The 3D-QSAR models generated were tested thoroughly for robustness and predictive ability. The average performance of the quantum mechanical molecular interaction field (QM-MIF) models for the nine data sets is better than that of the conventional force field-based MIFs. In the individual data sets, the QM-MIF models always perform better than, or as well as, the conventional approaches. It is particularly encouraging that the relative performance of the QM-MIF models improves in the external validation. In addition, the models generated showed statistical stability with respect to model building procedure variations such as grid spacing size and grid orientation. QM-MIF contour maps reproduce the features important for ligand binding for the example data set (factor Xa inhibitors), demonstrating the intuitive chemical interpretability of QM-MIFs

Thermodynamic Characterization of Hydration Sites from Integral Equation-Derived Free Energy Densities: Application to Protein Binding Sites and Ligand Series

Water molecules play an essential role for mediating interactions between ligands and protein binding sites. Displacement of specific water molecules can favorably modulate the free energy of binding of protein–ligand complexes. Here, the nature of water interactions in protein binding sites is investigated by 3D RISM (three-dimensional reference interaction site model) integral equation theory to understand and exploit local thermodynamic features of water molecules by ranking their possible displacement in structure-based design. Unlike molecular dynamics-based approaches, 3D RISM theory allows for fast and noise-free calculations using the same detailed level of solute–solvent interaction description. Here we correlate molecular water entities instead of mere site density maxima with local contributions to the solvation free energy using novel algorithms. Distinct water molecules and hydration sites are investigated in multiple protein–ligand X-ray structures, namely streptavidin, factor Xa, and factor VIIa, based on 3D RISM-derived free energy density fields. Our approach allows the semiquantitative assessment of whether a given structural water molecule can potentially be targeted for replacement in structure-based design. Finally, PLS-based regression models from free energy density fields used within a 3D-QSAR approach (CARMa - comparative analysis of 3D RISM Maps) are shown to be able to extract relevant information for the interpretation of structure–activity relationship (SAR) trends, as demonstrated for a series of serine protease inhibitors

Discovery and Optimization of 1‑Phenoxy-2-aminoindanes as Potent, Selective, and Orally Bioavailable Inhibitors of the Na⁺/H⁺ Exchanger Type 3 (NHE3)

The design, synthesis, and structure–activity relationship of 1-phenoxy-2-aminoindanes as inhibitors of the Na⁺/H⁺ exchanger type 3 (NHE3) are described based on a hit from high-throughput screening (HTS). The chemical optimization resulted in the discovery of potent, selective, and orally bioavailable NHE3 inhibitors with <b>13d</b> as best compound, showing high in vitro permeability and lacking CYP2D6 inhibition as main optimization parameters. Aligning 1-phenoxy-2-aminoindanes onto the X-ray structure of <b>13d</b> then provided 3D-QSAR models for NHE3 inhibition capturing guidelines for optimization. These models showed good correlation coefficients and allowed for activity estimation. In silico ADMET models for Caco-2 permeability and CYP2D6 inhibition were also successfully applied for this series. Moreover, docking into the CYP2D6 X-ray structure provided a reliable alignment for 3D-QSAR models. Finally <b>13d</b>, renamed as SAR197, was characterized in vitro and by in vivo pharmacokinetic (PK) and pharmacological studies to unveil its potential for reduction of obstructive sleep apneas

Discovery and Optimization of 1‑Phenoxy-2-aminoindanes as Potent, Selective, and Orally Bioavailable Inhibitors of the Na⁺/H⁺ Exchanger Type 3 (NHE3)

The design, synthesis, and structure–activity relationship of 1-phenoxy-2-aminoindanes as inhibitors of the Na⁺/H⁺ exchanger type 3 (NHE3) are described based on a hit from high-throughput screening (HTS). The chemical optimization resulted in the discovery of potent, selective, and orally bioavailable NHE3 inhibitors with <b>13d</b> as best compound, showing high in vitro permeability and lacking CYP2D6 inhibition as main optimization parameters. Aligning 1-phenoxy-2-aminoindanes onto the X-ray structure of <b>13d</b> then provided 3D-QSAR models for NHE3 inhibition capturing guidelines for optimization. These models showed good correlation coefficients and allowed for activity estimation. In silico ADMET models for Caco-2 permeability and CYP2D6 inhibition were also successfully applied for this series. Moreover, docking into the CYP2D6 X-ray structure provided a reliable alignment for 3D-QSAR models. Finally <b>13d</b>, renamed as SAR197, was characterized in vitro and by in vivo pharmacokinetic (PK) and pharmacological studies to unveil its potential for reduction of obstructive sleep apneas

Probing Factor Xa Protein–Ligand Interactions: Accurate Free Energy Calculations and Experimental Validations of Two Series of High-Affinity Ligands

The accurate prediction of protein–ligand binding affinity belongs to one of the central goals in computer-based drug design. Molecular dynamics (MD)-based free energy calculations have become increasingly popular in this respect due to their accuracy and solid theoretical basis. Here, we present a combined study which encompasses experimental and computational studies on two series of factor Xa ligands, which enclose a broad chemical space including large modifications of the central scaffold. Using this integrated approach, we identified several new ligands with different heterocyclic scaffolds different from the previously identified indole-2-carboxamides that show superior or similar affinity. Furthermore, the so far underexplored terminal alkyne moiety proved to be a suitable non-classical bioisosteric replacement for the higher halogen−π aryl interactions. With this challenging example, we demonstrated the ability of the MD-based non-equilibrium free energy calculation approach for guiding crucial modifications in the lead optimization process, such as scaffold replacement and single-site modifications at molecular interaction hot spots