Predicting the Sites and Energies of Noncovalent Intermolecular Interactions Using Local Properties

Feed-forward artificial neural nets have been used to recognize H-bond donor and acceptor sites on drug-like molecules based on local properties (electron density, molecular electrostatic potential and local ionization energy, electron affinity, and polarizability) calculated at grid points around the molecule. Interaction energies for training were obtained from B97-D and ωB97X-D/aug-cc-pVDZ density-functional theory calculations on a series of model central molecules and H-bond acceptor and donor probes constrained to the grid points used for training. The resulting models provide maps of both classical and unusual H- and halogen-bonding sites. Note that these reactions result even though only classical H-bond donors and acceptors were used as probes around the central molecules. Some examples demonstrate the ability of the models to take the electronics of the central molecule into consideration and to provide semiquantitative estimates of interaction energies at low computational cost

Economical and Accurate Protocol for Calculating Hydrogen-Bond-Acceptor Strengths

A series of density functional/basis set combinations and second-order Møller–Plesset calculations have been used to test their ability to reproduce the trends observed experimentally for the strengths of hydrogen-bond acceptors in order to identify computationally efficient techniques for routine use in the computational drug-design process. The effects of functionals, basis sets, counterpoise corrections, and constraints on the optimized geometries were tested and analyzed, and recommendations (M06-2X/cc-pVDZ and X3LYP/cc-pVDZ with single-point counterpoise corrections or X3LYP/aug-cc-pVDZ without counterpoise) were made for suitable moderately high-throughput techniques

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

Directional Noncovalent Interactions: Repulsion and Dispersion

The interaction energies between an argon atom and the dihalogens Br₂, BrCl, and BrF have been investigated using frozen core CCSD­(T)­(fc)/aug-cc-pVQZ calculations as reference values for other levels of theory. The potential-energy hypersurfaces show two types of minima: (1) collinear with the dihalogen bond and (2) in a bridging position. The former represent the most stable minima for these systems, and their binding energies decrease in the order Br > Cl > F. Isotropic atom–atom potentials cannot reproduce this binding pattern. Of the other levels of theory, CCSD­(T)­(fc)/aug-cc-pVTZ reproduces the reference data very well, as does MP2­(fc)/aug-cc-pVDZ, which performs better than MP2 with the larger basis sets (aug-cc-pVQZ and aug-cc-pvTZ). B3LYP-D3 and M06-2X reproduce the binding patterns moderately well despite the former using an isotropic dispersion potential correction. B3LYP-D3­(bj) performs even better. The success of the B3LYP-D3 methods is because polar flattening of the halogens allows the argon atom to approach more closely in the direction collinear with the bond, so that the sum of dispersion potential and repulsion is still negative at shorter distances than normally possible and the minimum is deeper at the van der Waals distance. Core polarization functions in the basis set and including the core orbitals in the CCSD­(T)­(full) calculations lead to a uniform decrease of approximately 20% in the magnitudes of the calculated interaction energies. The EXXRPA+@EXX (exact exchange random phase approximation) orbital-dependent density functional also gives interaction energies that correlate well with the highest level of theory but are approximately 10% low. The newly developed EXXRPA+@dRPA functional represents a systematic improvement on EXXRPA+@EXX